CN104808211A - Detector for measuring swimming path of fishes - Google Patents

Abstract

The invention relates to a detector for measuring the swimming path of fishes, and belongs to the field of daily tools. The detector comprises an elastic bar-shaped substrate of a high-molecular material, a sonar detector, a remote control boat, a position indicator, a remote computer system, a personal embedded system and a transmission system, wherein the sonar detector, the position indicator and the transmission system are mounted on the remote control boat, and data of the sonar detector and the position indicator is transmitted to the remote computer system or the personal embedded system via the transmission system for data analysis and processing. The detector can accurately detect the position of a fish shoal, track the fish shoal, and determine the direction of the fish shoal in advance, so that directed baiting and catching can be carried out at points that the fish shoal will pass in advance; and the detector is free of blind points, not limited by regions, and highly accurate, stable and reliable.

Description

A detector for measuring fish swimming trajectories

technical field

The invention relates to a detector for measuring fish swimming tracks, which belongs to the field of living tools.

Background technique

In recent years, the social economy has developed by leaps and bounds, and people's living standards have been continuously improved. my country's electronics industry has entered a stage of rapid development. The mass promotion and popularization of embedded products has caused great changes in people's lives. . People have higher and higher requirements for the quality of life. Some fishing enthusiasts are no longer satisfied with fishing in some ponds, and they turn their attention to reservoirs with larger water surfaces. At the same time, fishing enthusiasts are also worried about finding a suitable fishing spot in the water. Especially for lure fishing, after finding a school of fish, it should be because the drop point of the bait is not correct, so there is no catch. For this reason, they are eager to have a portable mobile device that can help them solve existing problems. It is under this requirement that the market for fish finder systems based on GPS navigation has been opened up and developed.

The currently commonly used ReelSonar is a sonar fish float, which is connected to the iPhone via Bluetooth and thrown into the water. It will use sonar to detect the underwater environment, the depth of the water where each fish is located, etc., and send the data back to the mobile phone. The application will display this information in the form of a map, so that users can easily select fish-rich areas. When a fish bites, ReelSonar will sound an alarm, allowing snoozing anglers to wake up in time to pull up the rod. In addition, this product is also a good choice for friends who like night fishing. It has an LED light on the top, which will flash when a fish passes by. ReelSonar uses sonar technology to track the fish that are about to swim to us. When the fish swims under our boat, the mobile application will display it in real time on an interface similar to a water map, and mark the depth of these fish with numbers . In this way, we will know which area has more fish, and it is best to prepare fully before the fish take the bait; however, the application environment of this device is relatively low, and the ideal condition is to have a fish that can swim For the hull, anglers try hanging in different waters. When fish schools are found, the device can be used to assist in fishing. When no fish schools are found, the device will have no effect. Secondly, the device can only detect the position of the fish, and does not know the trajectory of the fish. Because the fish is relatively vigilant, it has strong applicability to bait only in front of its running trajectory, and vice versa. The effect of the response.

Contents of the invention

In view of the above problems, the present invention provides a detector for measuring fish swimming trajectory, the detector can accurately detect the position of fish school, and can track and predict the direction of fish school in advance and pass the point ahead of fish school For directional baiting and fishing, the detector has no blind spots, is not subject to geographical restrictions, and has high accuracy, stability and reliability.

In order to solve the technical problem, the technical scheme adopted by the present invention: the detector includes an elastic strip substrate of polymer material, a sonar detector, a remote control ship, a locator, a remote computer system, a personal embedded system, and a transmission system; The sensor, locator, and transmission system are installed on the remote control ship. The data of the sonar detector and locator are transmitted to the remote computer system or personal embedded system through the transmission system. The remote computer system or personal embedded system is mainly composed of transducers, man-machine The interactive interface, power amplifier, digital signal processing module and analog signal preprocessing module are composed of five parts; the main work of the transducer is to complete the conversion of the acoustic signal and the electrical signal, the human-computer interaction interface realizes the command reception, and the power amplifier realizes the power Amplification, digital processing module completes signal detection and demodulation; analog signal preprocessing module completes analog signal filtering, A/D conversion function, provides better signal for digital signal processing;

The method of operation of the detector comprises the following steps:

The first step: the operator puts the remote-controlled boat into the water, and the remote-controlled boat is manually operated to swim on the lake surface, and the sonar detector is turned on at the same time;

Step 2: The sonar detector emits active sonar signals, then receives the echo of the target, and returns the detection results to the personal embedded computer system or remote computer system on the shore in real time; and through program analysis, it can control the signal generator to generate The required signal; and output to the beamforming matrix, through the beamforming matrix, give the signal a suitable weight and delay, the transmitter will get the desired beam just, the target positioning is measured by the sound path difference and phase difference, Measure the azimuth and distance of the target; and use the principle of space direction finding and phase displacement theory to determine the specific position of the object;

Step 3: Calculate by the remote computer system and display the fish signals on the computer in the form of sonar chart or sonar curve in real time. The distance information of the lower part, so as to draw a two-dimensional image of the underwater part of the pipeline, and then form a three-dimensional image according to the data obtained in the axial direction;

Step 4: When the operator finds that the remote control boat has detected a suitable fish school, the operator sets the tracking button on the personal embedded system or remote computer system. At this time, the remote control boat will lock the fish school designated by the operator in real time. Carry out real-time tracking at 200 meters behind the fish school, record the running track of the fish school during the tracking process, analyze and calculate, and turn on the GPS positioning system at the same time, and transmit the real-time positioning results and running track of the fish school to a remote computer system or individual Embedded system; the system can be automatically controlled by the computer in the established program to realize fully automated instrument state control, data sampling, transmission and access, and the obtained data files can be stored on the built-in hard disk or directly by the computer software through the communication interface. Perform data analysis and calculations;

Step 5: The operator can see the running track of the fish in real time and the predictable passing point of the fish school. At this time, the operator rushes to the passing point in front of the fish school to spread bait and catch.

Further: the personal embedded computer system or remote computer system ensures the synchronization of transmission and reception by setting synchronization control signals and handshake signals.

Further: the sonar detection of the personal embedded computer system or the remote computer system is a kind of imaging method based on acoustic emission, adopts a transducer that can do circular scanning, adopts Marr edge detection algorithm, at first is carried out Before the edge extraction, the image is filtered to remove those isolated noises, and then the edge extraction is started to improve the recognition accuracy.

Further: the sonar data of the personal embedded computer system or the remote computer system is stored in the memory, and two buffers I and II with continuous addresses and the same size are opened in the memory, and the size of the buffer is 8 row data, set a variable SWAP to identify the buffer area that needs to be processed currently, when SWAP is 0, process buffer I, and transfer the data to buffer II at the same time, set the value of SWAP when buffer I is processed For l, the system turns to the processing buffer II, and transfers the data to the buffer I. Through the alternate conversion of the two buffers, the system can perform calculation and transmission work at the same time.

Further: the transmission system is GPRS or Beidou satellite communication; the locator is Beidou satellite positioning system or GPS.

Beneficial effects of the present invention: the present invention can accurately detect the position of the fish school, and can track and predict the direction of the fish school in advance to perform directional baiting and fishing at the passing point in front of the fish school. Geographical restrictions, high accuracy, stability and reliability.

Description of drawings

Fig. 1 is a composition diagram of a remote computer system or a personal embedded system of the present invention;

Fig. 2 is the flow chart of sonar generator of the present invention;

Fig. 3 is a schematic diagram of ultrasonic transmission and reception control of the present invention;

Fig. 4 is a simulation diagram of the directivity opening angle of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and implementation process, so as to facilitate the understanding of technical personnel.

The detector of the present invention includes elastic strip substrate of polymer material, sonar detector, remote control boat, locator, remote computer system, personal embedded system, transmission system; sonar detector, locator, transmission system are contained in On the remote control ship, the data of sonar detectors and locators are transmitted to the remote computer system or personal embedded system through the transmission system, as shown in Figure 1: the remote computer system or personal embedded system is mainly composed of transducers, human-computer interaction interfaces, The power amplifier, digital signal processing module and analog signal preprocessing module are composed of five parts; the main work of the transducer is to complete the conversion of acoustic signals and electrical signals, the human-computer interaction interface realizes command reception, and the power amplifier realizes power amplification. The processing module completes signal detection and demodulation; the analog signal preprocessing module completes analog signal filtering and A/D conversion functions to provide relatively good signals for digital signal processing;

The method of operation of the detector comprises the following steps:

The first step: the operator puts the remote-controlled boat into the water, and the remote-controlled boat is manually operated to swim on the lake surface, and the sonar detector is turned on at the same time;

Step 2: The sonar detector locks the target by emitting active sonar signals, and then receives the echo of the target, and returns the detection results to the personal embedded computer system or remote computer system on the shore in real time; and determines the target through program analysis orientation, distance.

The signal emitted by the sonar transmitter is mainly a single-frequency matrix pulse.

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; E.</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}\mathrm{<span\; class="notranslate">\; rest</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; cos</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;ft</span>}<span\; class="notranslate">\; )</span>}$

The sonar equation is: DT=SL-2TL+TS-NL+DI

The meaning of each parameter is: TS is the target reflection intensity, DI is the receiver directivity index, TL is the propagation loss of the sound wave, DT is the detection valve, SL is the target source, and NL is the environmental noise. The flow chart of the sonar generator is shown in Figure 2;

Through the program, the signal generator can be controlled to generate the required signal; and output to the beamforming matrix, through the beamforming matrix, give the signal an appropriate weight and delay, the transmitter will get the desired beam just, and the target positioning through Measure the sound path difference and phase difference, and measure the azimuth and distance of the target; due to the poor light transmission of the ocean, it is unrealistic to use optical instruments to measure, so sonar imaging can play a better role. However, sonar imaging is not as intuitive as video images. It is necessary to calculate the distance of objects through the transmission and reception of sonar signals, according to the propagation speed of sound waves in water and the interval time between transmission and reception. And use the principle of space direction finding and phase displacement theory to determine the specific position of the object.

In the spatial lateral principle, coherent sources are ubiquitous, and a typical example is the multipath phenomenon in the signal transmission process. In spatial spectrum estimation, the detection and estimation of coherent signal sources is an important research direction. When multiple signals are considered, the signals may be coherent, uncorrelated or correlated. For two stationary signals s ₁ ^(t) and s _(k) ^(t) , define their correlation coefficient as:

According to the Schwart inequality, |ρ _ik |≦1, the coherence between signals is as follows:

ρ _ik =0, s _i (t) and s _(k) ^(t) are independent

|ρ _ik |=1, s _i (t) and s _(k) ^(t) are coherent

0<ρ _ik <1, s _i ^(t) and s _(k) ^(t) are related

Suppose there are Q coherent sources, namely:

s _i ^(t) = a _i s ₀ ^(t) i = 1,2,3...Q

Here s ₀ ^(t) may be called a generating source, which generates Q coherent signal sources incident on the array.

$\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}<span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}$

Bringing (4) into (3), the mathematical model of the coherent source can be derived:

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; AS</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; A</span>}\left[\begin{array}{c}{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}\\ {{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span>\\ {{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; Q</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}\end{array}\right]<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; A</span>}}^{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>$

In the formula, ρ is a Q-dimensional column vector composed of a series of complex constants.

According to the receiving transducer composed of N array elements with or without direction, the distance between each array element is d, and the output signal of any array element is expressed as;

in is the phase difference between the received signals of the array element, A is the signal amplitude, and ω is the signal frequency.

can also be expressed as

In this way, the output can be expressed as:

Through normalization processing, it can be obtained:

R(θ) shows that as the incident angle changes, the output amplitude of the multi-element array changes accordingly. Generally speaking, for any formation, no matter from which direction the sound waves are incident, it is impossible to form in-phase addition or obtain the maximum input. Only a linear array or a space plane array can form in-phase addition in the normal direction of the array. get the maximum output.

Inserting a phase shift β between adjacent array elements is the main purpose of the linear array phase-shifting the beam, so the summation output of the linear array is:

The same goes for normalization:

In this case, the main beam direction satisfies:

$\frac{2\mathrm{\&pi;d}}{\mathrm{\&lambda;}}\sin \mathrm{\&theta;}-\mathrm{\&beta;}=0,$ so get $\mathrm{\&theta;}={\sin }^{-1}\frac{\mathrm{\&beta;\&lambda;}}{2\mathrm{\&pi;d}}.$

By inserting different phase shifts β between the array elements, the main beam is located in different directions. This control method is called phase shift beamforming.

Step 3: Calculate by the remote computer system and display the fish signal on the computer in the form of sonar chart or sonar curve in real time. The distance information of the lower part, so just describe the two-dimensional image of the underwater part of the pipeline, and then form a three-dimensional image according to the data obtained in the axial direction; To ensure the synchronization of transmission and reception; solving the synchronization problem of signal transmission and reception is the most critical link in the collection and reception system. Through the transmission and reception of ultrasonic waves, the distance and orientation of the target object can be accurately located. In the process of data collection, it is necessary to deal with the synchronization problem of transmission and reception, so that the system can reasonably and accurately collect the required information data. Too much collected data will cause calculation complexity, and too little data will cause data loss.

It can be seen from Figure 3 that the system ensures the synchronization of transmission and reception by setting synchronous control signals and handshake signals, so that the data acquisition card collects the most reasonable amount of data.

Mathematical Model of Data Acquisition and Processing

Indicates the array element input signal vector, Indicates the weight coefficient, its value is adjustable, the full vector can be expressed as Represents the output of the beam operator. There is an error signal between the actual output of the beam operator and the expected output signal, which is represented by e(t), and d(t) represents the expected value. The relationship between the error signal and the expected value is expressed as: By continuously adjusting the The smallest mean square error can be obtained

If put Defined as the autocorrelation matrix of the reference input signal, the matrix R is generally symmetric. P can define the cross-correlation matrix of d(f) and X(f):

It can be seen that the mean square error is non-negative, and the smallest mean square error can be obtained through the adjustment of the weight vector. Using the gradient method, it can be seen that

When the gradient is zero, we know When the minimum is obtained, the full vector has the best solution, expressed as

Called the Wiener-Hopf equation. By fitting the acquired data, we can get

${\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;d</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>$

The sonar detection of described personal embedded computer system or remote computer system is a kind of imaging method based on acoustic emission, has adopted the transducer that can do circular scanning, adopts Marr edge detection algorithm, at first carries out edge extraction Before the image is filtered, those isolated noises are removed, and then the edge extraction is started to improve the recognition accuracy.

h(x) is the impulse response function of the smoothing filter, the signal to be processed is f(x), after filtering, the signal g(x)=h(x)*f(x), for the obtained signal g(x ) for first-order or second-order derivatives, edge detection is possible.

Since the convolution operation has the following properties:

${\mathrm{<span\; class="notranslate">\; g</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; df</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; dx</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}}{\mathrm{<span\; class="notranslate">\; dx</span>}}{<span\; class="notranslate">\; \&Integral;</span>}_{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \&infin;</span>}^{<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&infin;</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; ds</span>}<span\; class="notranslate">\; =</span>\underset{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \&infin;</span>}{\overset{<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&infin;</span>}{<span\; class="notranslate">\; \&Integral;</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; ds</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>$

In this way, the differential operation and the convolution operation can be combined, and the first-order and second-order derivatives of the smoothing filter can be directly convoluted with the image, and the edge point detection of the image can be converted into detecting the zero-crossing point of f(x)*h(x) or local maximum. In this case, the problem falls on the design of the smoothing filter. The smoothing filter h(x) must meet the following conditions:

When |x|→∞, h(x)→0. h(x) is an even function;

${<span\; class="notranslate">\; \&Integral;</span>}_{<span\; class="notranslate">\; \&infin;</span>}^{<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&infin;</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ;</span>$

h(x) is first and second order differentiable.

The Guass function is a commonly used smoothing filter, Taking the first-order and second-order derivatives, we can get:

${\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; x</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\frac{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$

${\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\frac{{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

The variance of the Gauss function is called the spatial size factor of the Gauss distribution. The smaller the spatial scale, the smaller the filtering range. Conversely, the larger the spatial scale, the larger the filtering range. But it is not that the larger the filtering range, the better, because if the filtering range is too large, many useful signal mutation points will be smoothed out. Only by selecting an appropriate filtering range can the quality of edge extraction be guaranteed.

Marr uses a smooth two-dimensional Gsauss function to process two-dimensional image signals, as follows:

$\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Although it is easy to find the first and second derivatives of the two-dimensional Gaussian function, they are not directly convolved with the image. Because partial derivatives are directional, they can only handle edges in certain special directions. Even by solving along the gradient

The second derivative of , because the result obtained is non-linear, the calculation is more complicated. Therefore, Mallr proposes to replace it with the Laplacian operator, and the edge detection operator is obtained as follows:

▽ ² g(x,y)=▽ ² (G(x,y,σ)*f(x,y))=(▽ ² G(x,y,σ))*f(x,y)

To get the edge point of the image, only the zero crossing point of ▽ ² g(x,y) is required.

Each frame of data collected by the camera is stored in a two-dimensional array in the computer memory, with M rows*N columns. Suppose we define a u, v rectangular coordinate system on the image, then each pixel (u, v) There are only rows and columns in the array, and (u, v) are called the coordinates of the image coordinate system. The camera coordinates are composed of X, Y, and Z axes, with O as the origin, X, Y, parallel to the x, y axes of the image coordinates, and the Z axis as the optical axis of the camera, perpendicular to the image plane. Let any point P in the space, the projection point on the left and right cameras be p, P ₀ , we can get:

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ {\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{cccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 13</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 14</span>}}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; three</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 33</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 34</span>}}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; x</span>}\\ \mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; Z</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]$

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\\ {\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{cccc}\mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 13</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 14</span>}}\\ \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; three</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}\\ \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}& \mathrm{<span\; class="notranslate">\; 2</span>}\\ {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 33</span>}}& {\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 34</span>}}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; x</span>}\\ \mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; Z</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]$

Represents row i and column j, and eliminates the proportional items Z _c1 and Z _c2 to obtain a linear equation system of X, Y, and Z:

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 33</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 13</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 14</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 34</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}$

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 33</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; three</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 34</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}$

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 33</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 13</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 14</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 34</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Y</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 33</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; three</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 34</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

According to the above formula, the least square method can be used to solve X, Y, Z, that is, the spatial coordinates of point P are known.

In order to be able to complete the sonar imaging task, we usually place several sonar probes on the circumference, assuming that at a certain moment, the directivity opening angle of the sonar is θ. scan, the number of probes and the number of rotations of the probe on the circumference have the following relationship:

n _s represents the number of probes on the circumference, and n _r represents the number of times the probe rotates on the circumference. Within the length of L, if the number of scans is n _z , then In order to reduce the number of scans while ensuring accuracy, we need to select a relatively good directivity opening angle. The simulation diagram of the directivity opening angle is shown in Figure 4. Among the methods of image segmentation, edge detection is the most researched image segmentation method. It uses the discontinuity of the image in the depth and surface direction to perform a research method of feature extraction.

It is not advisable to directly perform the derivative operation on the function to identify the edge. Although the derivative operation has directionality, it cannot guarantee isotropy. The square of the partial derivative, that is, the square of the gradient magnitude is isotropic, so it is often used in image processing. However, this traditional edge detection has the following disadvantages: the traditional edge detection uses the mutation of the pixel gray value to identify the edge of the image, but the noise in the image also has similar characteristics. Using the traditional detection method, it will The edge points of the image and the edge points of the noise are extracted at the same time, which reduces the accuracy of sonar recognition.

Step 4: When the operator finds that the remote control boat has detected a suitable fish school, the operator sets the tracking button on the personal embedded system or remote computer system. At this time, the remote control boat will lock the fish school designated by the operator in real time. Carry out real-time tracking at 200 meters behind the fish school, record the running track of the fish school during the tracking process, analyze and calculate, and turn on the GPS positioning system at the same time, and transmit the real-time positioning results and running track of the fish school to a remote computer system or individual Embedded system; the system can be automatically controlled by the computer in the established program to realize fully automated instrument state control, data sampling, transmission and access, and the obtained data files can be stored on the built-in hard disk or directly by the computer software through the communication interface. Perform data analysis and calculations;

The sonar data of the personal embedded computer system or the remote computer system is stored in the memory, and two consecutive addresses and the same size buffer I and buffer II are opened in the memory, and the size of the buffer is 8 rows of data , set a variable SWAP to identify the current buffer area that needs to be processed. When SWAP is 0, the buffer I is processed, and the data is transferred to the buffer II at the same time. When the buffer I is processed, the value of SWAP is set to 1 , the system turns to the processing buffer II and transfers the data to the buffer I. Through the alternate conversion of the two buffers, the system can perform calculation and transmission at the same time.

Step 5: The operator can see the running track of the fish in real time and the predictable passing point of the fish school. At this time, the operator rushes to the passing point in front of the fish school to spread bait and catch.

In the above steps, the transmission system is GPRS or Beidou satellite communication; the locator is Beidou satellite positioning system or GPS.

The elastic strip-shaped substrate of polymer material is a strip-shaped device made of polymer material with high molecular weight, light weight, low density, excellent mechanical properties, insulation properties, and heat insulation properties. This equipment is used to firmly fix components such as locators, sonar detectors, and data transmission equipment on the remote control boat, so that it is not easy to shake and can maintain proper toughness.

The present invention is illustrated by the accompanying drawings, without departing from the scope of the present invention, various transformations and equivalent substitutions can also be made to the patent of the present invention. Therefore, the patent of the present invention is not limited to the disclosed specific implementation process, but should include All embodiments falling within the scope of the patent claims of the present invention.

Claims (5)

1. A detector for measuring the swimming track of fish, characterized in that, the detector comprises a polymer material elastic strip substrate, a sonar detector, a remote control boat, a locator, a remote computer system, a personal embedded system, transmission system; sonar detectors, locators, and transmission systems are installed on the remote control ship, and the data of sonar detectors and locators are transmitted to the remote computer system or personal embedded system through the transmission system; the remote computer system or personal embedded system is mainly It consists of five parts: transducer, human-computer interaction interface, power amplifier, digital signal processing module and analog signal preprocessing module; the main work of the transducer is to complete the conversion of acoustic signals and electrical signals; the human-computer interaction interface implements commands The power amplifier realizes power amplification; the digital processing module completes the detection and demodulation of the signal; the analog signal preprocessing module completes the filtering of the analog signal, and the A/D conversion function provides relatively good signals for digital signal processing; the detection The method of operation of the device includes the following steps: The first step: the operator puts the remote-controlled boat into the water, and the remote-controlled boat is manually operated to swim on the lake surface, and the sonar detector is turned on at the same time; Step 2: The sonar detector emits active sonar signals, then receives the echo of the target, and returns the detection results to the personal embedded computer system or remote computer system on the shore in real time, and through program analysis, it can control the signal generator to generate The required signal; and output to the beamforming matrix, through the beamforming matrix, give the signal a suitable weighting and delay, the transmitter will get the desired beam just, the target positioning is measured by the sound path difference and phase difference, Measure the azimuth and distance of the target; and use the principle of space direction finding and phase displacement theory to determine the specific position of the object; Step 3: Calculate by the remote computer system and display the fish signals on the computer in the form of sonar chart or sonar curve in real time. The distance information of the lower part, so as to draw a two-dimensional image of the underwater part of the pipeline, and then form a three-dimensional image according to the data obtained in the axial direction; Step 4: When the operator finds that the remote control boat has detected a suitable fish school, the operator sets the tracking button on the personal embedded system or remote computer system. At this time, the remote control boat will lock the fish school designated by the operator in real time. Carry out real-time tracking at 200 meters behind the fish school, record the running track of the fish school during the tracking process, analyze and calculate, and turn on the GPS positioning system at the same time, and transmit the real-time positioning results and running track of the fish school to a remote computer system or individual Embedded system; the system can be automatically controlled by the computer in the established program to realize fully automated instrument state control, data sampling, transmission and access, and the obtained data files can be stored on the built-in hard disk or directly by the computer software through the communication interface. Perform data analysis and calculations; Step 5: The operator can see the running track of the fish in real time and the predictable passing point of the fish school. At this time, the operator rushes to the passing point in front of the fish school to spread bait and catch. 2. A kind of detector that is used to measure fish swimming track according to claim 1, is characterized in that, described personal embedded computer system or remote computer system guarantees transmission and reception by setting synchronous control signal and handshake signal synchronization. 3. A kind of detector that is used to measure fish swimming track according to claim 2, it is characterized in that, the sonar detection of described personal embedded computer system or remote computer system is to be built on the basis of sound wave emission This imaging method uses a transducer capable of circular scanning and Marr edge detection algorithm. First, the image is filtered before edge extraction to remove those isolated noises, and then edge extraction is started to improve recognition. accuracy. 4. A kind of detector that is used to measure fish swimming track according to claim 3, is characterized in that, the sonar data of described personal embedded computer system or remote computer system is stored in memory, in memory Open up two buffers I and II with continuous addresses and the same size. The size of the buffer is 8 lines of data. Set a variable SWAP to identify the buffer area that needs to be processed currently. When SWAP is 0, the buffer area is processed. Ⅰ, at the same time, the data is transferred to the buffer Ⅱ. When the buffer Ⅰ is processed, the value of SWAP is set to 1, and the system turns to the processing buffer Ⅱ, and the data is transferred to the buffer Ⅰ. Through the alternate conversion of the two buffers , so that the system can perform calculation and transmission work at the same time. 5. A kind of detector that is used to measure fish swimming track according to any one of claim 1-4, described transmission system is GPRS or Beidou satellite communication; Locator is Beidou satellite positioning system or GPS.