JP2002341014A - Device for correlation processing of multisensor track and program therefor - Google Patents

Abstract

(57) [Problem] To perform complicated motion such as acceleration motion in a process of determining whether or not wake data transmitted asynchronously from a plurality of wake monitoring systems that track and monitor a flying object is correlated. A device that achieves highly accurate correlation with flying objects is realized. SOLUTION: When track data received from each track monitoring system is registered as a history in a storage unit, when a track identifier included in the received track data does not correspond to any track identifier already established and registered in a management table. (In the case of a new uncorrelated track), calculate a function relating to a time for interpolating the position information of the plurality of received track data, and determine whether or not the track registered in the management table has a correlation with the calculated function. The identifier is registered in the management table in a format for distinguishing whether or not the track data is a track based on the same flying object as an established track according to whether or not there is a correlation.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for correlating wake data from a plurality of sensor system apparatuses having radar apparatuses and generating wake data, and a program executed by the apparatus.

[0002]

2. Description of the Related Art Normally, areas monitored by a plurality of sensor system devices may overlap each other, so that a plurality of track data obtained from a plurality of sensor system devices are based on the same flying object. , It is necessary to determine whether they relate to different flying objects. Therefore, it is necessary to perform a correlation process between the newly obtained track data and the track data of the track already established.

[0003] Correlation processing includes correlation processing between plotter information from a radar device and an established track, and correlation processing between a plurality of tracks obtained from a sensor system device as described in the present invention. Is also a process of checking whether the data indicates the same flying object. In each of the monitoring system devices that are the premise of the present invention, a correlation process is performed between the plotter data from the radar device and the wake established in the wake management device. In the correlation processing between the plotter data from the radar device and the established track, see, for example, Reference (1): "Utilization of Computers in Air Traffic Control", Information Processing Vol.22 No.3 pp.176-185. The α-β tracking method used is a typical example.
In this method, an estimated value of the track at the next sampling time is obtained based on the information on the position and speed of the track updated after smoothing. In this case, position information at the next sampling time is predicted by the following equation, assuming constant velocity linear motion.

X _{n + 1} (prediction) = X _n + V _n · ΔT X _n is the smoothed position information in the n-th step, and V _n is n
The smoothing speed information in the first step, ΔT, is the sampling time interval, that is, the time difference between the (n + 1) th and the nth. X _{n + 1} (prediction) is a predicted position in the ( _{n + 1} ) th step. In the correlation process, the data closest to the predicted value is regarded as plotter data indicating the same flying object as the established track (the so-called Nearest-Neighbor method), and the smoothed value between the plotter data value and the predicted value is calculated as the next established track. And

[0005] In the correlation processing between the track data asynchronously transmitted from a plurality of monitoring system devices according to the present invention, the prediction method of the track data is basically the same. As an example showing this, a correlation processing method in a multi-sensor device is described in Reference (2): “Improvement of correlation accuracy in state estimation using multi-sensors” system and control Vol.
3-531. In the literature (2), when performing a correlation check between two tracks, it is assumed that one track moves linearly at the same speed as in the above method, and the flight status at the same time as the registration time of the data of the other track is determined. A prediction is made, and a correlation check is performed between the predicted estimation value and the data of the other track.

[0006] In addition to the α-β tracking method, a tracking method using a Kalman filter is known (References 1 and 2). This method can perform good prediction estimation for a flying object that moves at a constant speed. In the case of rapid movement, it is difficult to follow.

[0007]

In the above-described correlation processing between the plotter data and the established track and the prediction of the track by the correlation processing between the tracks, the estimated value is obtained by predicting the track by a constant velocity linear motion with a constant speed vector. Since it is predicted by constant-velocity linear motion, it is possible to correlate with high accuracy for flying objects that move slowly, but for flying objects that perform complex motions such as acceleration motion. Therefore, it was difficult to obtain a highly accurate correlation. In order to solve this, there has been no method other than reducing the interval of the sampling period of the radar device.

[0008] It is an object of the present invention to realize a correlation process between wakes, which enables highly accurate correlation to be made with respect to a flying object that moves in a complicated manner such as an acceleration motion.

[0009]

SUMMARY OF THE INVENTION The present invention sequentially receives track data including a track identifier, time information, position information of a flying object, and speed information from each of a plurality of track monitoring systems, and identifies the track data by the track identifier. Is a device that determines whether a track is based on the same flight object as a track received from another track monitoring system or a track based on a different flight object.It stores multiple track data for each track monitoring system and each track identifier. First storage means, a second storage means in which a track identifier of a track based on the same flying object and a track identifier of a track based on a different flying object are registered separately, and the track data received from the track monitoring system is stored. First
Means for storing in a corresponding area of the storage means, and whether the track identifier included in the track data received with reference to the second storage means matches any of the track identifiers registered in the second storage means Means for determining whether or not the track information does not correspond to any of the track identifiers; means for calculating a function relating to a time at which position information is interpolated for a plurality of track data received; and a track identifier registered in the second storage means. Means for determining whether or not the position information relating to time has a correlation with the calculated function with respect to the track data including, based on the same flying object as the track having a correlation when having a correlation with any of the track data Means for registering in the second storage means as a track identifier of a track, and a track identifier of a track based on a different flying object when there is no correlation with any track data. Features an apparatus for the correlation of multi-sensor track and a means for registering the second storage means Te.

[0010] The present invention is also characterized by a program having a procedure executed by the above device.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 shows an example of a system configuration which is a prerequisite for realizing a processing method according to the present invention. The track management device 101 is a kind of computer, and a program executed in the device receives track data from a plurality of monitoring system devices 102 for monitoring flying objects via a receiving device 103 and transmits the received track data to its own device. Take in. The linkage between the monitoring system device and the receiving device may be wireless or wired. It is assumed that each monitoring system device 102 has a radar device, and has a capability of monitoring and tracking a flying object existing in its own monitoring area and generating wake data thereof. In this sense, it is different from a mere radar device having only the ability to generate radar plotter data. The areas monitored by A, B, and C of the monitoring system device 102 include areas that overlap with each other.
Since the wake data transmitted by the monitoring system device 102 is monitoring data of a flying object, it is generally transmitted periodically. Upon receiving the track data, the track management apparatus 101 registers the track data in the track data recording unit 104, and when the input data is new uncorrelated track data, performs a correlation process with the checked track data. . The track data recording unit 104 may be an external storage device as shown in FIG. 1, or may be a recording medium such as a memory in the track management device 101. As a result of the correlation processing, when it is determined that the tracks registered in the track data recording unit 104 are correlated, that is, the tracks indicating the same flying object, the correlation information is also recorded in the track data recording unit 10.
Registered in 4. Therefore, the track data recording unit 104 registers the track data that is consistent in the entire area monitored by the plurality of monitoring system devices 102. This matching data can be confirmed by the monitoring terminal 105.

FIG. 2 shows an example of the format of the track data received by the track management apparatus 101. As items, a monitoring system device number 201 indicating which monitoring system device is the track data from, a track number 202 which is a number uniquely assigned to the track, and time information 203 indicating a time when the track data was generated are included. is there. The track number 202 is a track identifier, and the track data 202 determined by each monitoring system device 102 to be the same flying object is assigned the same track number 202. If there is no time information in the data from the monitoring system device 102, the estimated time is obtained by correcting the time received by the receiving device 103. In addition, there are position information 204 indicating the position of the flying object, which is the most important information of the track data, speed information 205, and track quality 206 indicating the quality level of the track data. The track quality is information on the quality level of data such as weather conditions and radar performance.

FIG. 3 shows a wake management device according to this embodiment.
An outline of processing executed by the program 101 will be described. When the wake data is received and input via the receiving device 103, the wake management device 101 first registers the wake data in the corresponding area of the wake data recording unit 104 as history data (step 30).
1). Since the data is history data, a plurality of pieces of data having the same track number are registered in the corresponding area. Subsequently, when the registered track data is new uncorrelated track data (step 302).
Yes), at the stage where the history data for a certain period of time is accumulated, interpolation processing using these history data, that is, an interpolation function that complements the history data that is time-series data as continuous It is determined every time (step 303).
Using this interpolation function, new uncorrelated trajectory data is corrected, and a correlation process between the corrected value and the checked trajectory data is executed (step 304). Then, the information of the correlation processing result is registered in the wake data recording unit 104.
(Step 305). After completion of the correlation processing, the flow returns to step 301 again to receive the wake data.

FIG. 4 shows an example of the configuration of registered track data in the data recording unit 104. The area 401 is a storage area allocated to each monitoring system device 102. The wake data registration area 402 in the area 401 is an area for storing wake data from the corresponding monitoring system device. FIG. 4B shows the track data registration area 402 more specifically.
First, there is a track number which is a key indicating a track. This is the same content as the track number 202. Since the track data is generally periodic data, the number of track data that can be registered as history information within a certain time is almost fixed, and is registered in chronological order according to the time information 203. If all the finite registration areas have been registered, the oldest data area is overwritten with newly received data, that is, registered in a so-called cyclic manner. The correlation data management table 403 is
It is the table which recorded the information regarding the correlation of each track.
As will be described later, in the correlation data management table 403, a track for which correlation processing has been completed, that is, a track number of a checked track is registered. The new uncorrelated track data is subjected to correlation processing (step 304) with the checked track data registered in the management table, and the result is recorded in the correlation data management table 403 (step 305). . By referring to this table, it is possible to confirm the wake of the flying object in an area where a plurality of monitoring areas are integrated.

Hereinafter, the outline of the processing flow of the present invention shown in FIG. 3 will be described more specifically. First, the wake data reception and history registration in step 301 and the determination processing in step 302 will be described. FIG. 5 shows the processing procedure. Track management device 101
First, the wake data is received via the receiving device 103 (step 501). Track data monitoring system device numbers 201 and 202
From this, it is possible to recognize which track data is from which monitoring system device 102, and to register the track data in a predetermined area in the track data recording unit 104 as history data.
(Step 502). After registering the data, check whether the track related to this track data has been checked for correlation (step 5
03). This can be determined by the fact that the correlation check has been performed if the corresponding track number exists in the correlation data management table 403, and that the correlation check has not been performed yet if the track number does not exist. If the correlation has not been checked in step 503, the data is a new uncorrelated track. However, in the present invention, interpolation processing using history data is performed, so that wake data for a plurality of cycles must be registered as history data within a fixed time. Therefore, it is checked in step 504 whether a sufficient number of track data are registered. If a sufficient number has not been registered, the process returns to the beginning of the process and waits for reception of new track data. If sufficient wake data is registered, the process proceeds to the next process, that is, the interpolation process (step 303).

Next, a case where the received track data is data of a track whose correlation has been checked in step 503 will be described. If there is no problem, it returns to the top again and waits for reception of wake data. However, if the registration time interval with the previous data in the history data is separated by comparing the time information 203, if there is any abnormality in the monitoring system of the transmission source, or the same as a completely different flying object It can also be expected that track numbers have been assigned. Therefore, when the data of the track whose correlation has been checked is received, it is checked whether or not the time information 203 of the received data has passed a certain time or more from the time information of the previous history data (step 505). This fixed interval is a time that is no longer useful as data indicating the current situation of the flying object. If a certain period of time has passed,
The wake of this data is regarded as uncorrelated data (step 50
6). This can be done by deleting the corresponding track number 202 and track quality 206 from the correlation data management table 403. As a result, the data of the wake is subjected to the correlation check again, so that an erroneous correlation setting can be prevented. Then, after step 506, the process returns to the beginning of the process and waits for reception of the wake data. According to the above-described processing procedure, if the data of the track whose correlation has been checked is normally received / registered, at least one or more history data of the track exists within the predetermined time interval. Above, the minimum time interval for registering the history of the data of the new uncorrelated track is the fixed time interval. Uncorrelated tracks also become checked tracks after the correlation check processing, and as a result, history data of all tracks can be registered for a certain time interval or more as described above.

In the interpolation processing in step 303, an interpolation function for complementing history data, which is time-series data, as continuous data is obtained. First, let's briefly talk about interpolation functions. The interpolation function referred to in the present invention indicates a function connecting the wake history data as shown in FIG. The number of pieces of history data and data information used for obtaining the function, such as a position and a speed, are given conditions. FIG. 6 shows an interpolation function that uses two pieces of history data and connects this section. In the figure, for the sake of simplicity, a one-dimensional movement with respect to the time axis, for example, an X-axis movement is shown. In the figure, if the horizontal axis is time and the vertical axis is position, the speed is given by dx / dt, so the slope represents the speed. When the position and the speed are given as the track data as in the track data format of FIG. 2, a function satisfying the position and the inclination at the designated time (corresponding to the time information 203) of the history data is obtained as an interpolation function. Become. An interpolation function connecting two history data is, for example, (Equation 1)
Can be given by Since this is a third-order polynomial function of time, it can express up to the first-order change in acceleration. Although it depends on the monitoring cycle time interval, considering the maneuverability of the person who controls the flying object, the tracking up to the first-order change in acceleration can be considerably improved compared to the extrapolation method using a linear function.

[0018] ^{x (t) = at 3 +} bt 2 + ct + d ...... ( Equation 1) speed in this case is given by dx (t) / dt = 3at 2 + 2bt + c ...... ( Equation 2) And given conditions in FIG. 6 are x | t _n = Xt _n , dx / dt | t _n = Vt _{n ,} x | t _{n + 1} = Xt _{n + 1 ,} dx
/ dt | t _{n + 1} = Vt _{n + 1} . This is a system of four unknowns and four equations
Since a linear equation is obtained, an interpolation function can be uniquely obtained by solving the equation. However, when this interpolation function is used to estimate the position and velocity of the wake after t _{n + 1} outside the section, for example, in the example of FIG. 6, the higher-order term of the time function, that is, the second or third order Since the influence of the term becomes larger, the earlier the prediction becomes, the larger the value becomes, and the deviation from the actual state of the flying object becomes larger. Therefore, it is not always desirable to predict the previous state using the interpolation function of (Equation 1), and this method can enjoy its advantages in the section of the history data. In the interpolation processing of step 303 by the above method, an interpolation function between time-series history data may be sequentially obtained.

Next, the correlation processing of the wake data using the interpolation function in step 304 and the correlation result registration processing in step 305 will be described. This is to check whether the new uncorrelated track and the track whose correlation has been checked registered in the correlation data management table 403 are not correlated, and if they are correlated, register the correlation in the management table and register the correlation. If not, the process is successively performed to the correlation process with the next checked track, and if not correlated with all the checked tracks, the process is registered as no correlation in the management table.

FIG. 7 shows a detailed flow of steps 304 and 305. First, the track management device 101 sets the correlation data management table 4
Select one track whose correlation has been checked by referring to 03
(Step 701).

FIG. 8 shows a configuration example of the management table. Reference numeral 801 denotes a common ID number uniquely assigned to the flying object in the integrated monitoring area. This number may be registered by the user via the monitoring terminal 105 or may be allocated in the track management device 101 for data management. 201 indicates the monitoring system device number of the transmission source of the track data, 202 indicates the track number, and 206 indicates the track quality.

In the process of step 701, for example, the common ID
The number 801 may be used as a selection key. If there are a plurality of tracks, that is, tracks correlated with the common ID number, for example, the track with the highest quality may be selected. The specific track data of the selected track whose correlation has been checked is stored in the track data registration area 40 using the monitoring system device number 201 and the track number 202 as keys.
It becomes clear because 2 can be specified. Further, at step 701, one history data is selected from the selected checked tracks. That is, history data belonging to a certain period of time is selected from the track data in the corresponding track data registration area 402. In step 702, the interpolation function of the section to which the time information 203 of the selected history data of the correlation-checked track belongs among the sections of the history data of the new uncorrelated track is selected.
If the time of the selected wake data is within a certain time, an interpolation function covering that time must have been calculated.

This will be specifically described with reference to the example shown in FIG. In the figure, the black circle is the new uncorrelated track data. If the black square is the history data of the selected correlation checked wake, it belongs to the time section [t _n-2 , t _n-1 ], so X (t) _n-2 is selected as the interpolation function. Will be done.

Subsequently, at step 704, first, at step 703
The interpolation value of the new uncorrelated track at the time of the history data of the selected track for which correlation check has been performed is obtained using the interpolation function selected in (see FIG. 9). This is obtained by substituting the time into the selected interpolation function when obtaining the position. The speed is obtained by substituting the time into the first derivative function of the interpolation function. Then, a correlation process is performed between the interpolated data value and the selected history data.
(Step 704). As a content of the correlation processing, a gate method, that is, a method in which there is a correlation when a difference value between a position and a speed is within a certain range, is known. The correlation method itself is not directly related to the present invention and will not be described in detail here.

When it is determined that there is a correlation (step 705),
Correlation is registered in the correlation data management table 403 (step 706). This means that, as the track having the same common ID number 801 in the correlation data management table in FIG. 8, the track number of the track data is registered in the same frame of the correlated registered track area. When the track number 202 is registered in this table, the track that has been newly uncorrelated is correlated. Then, the process returns to step 501 and waits for reception of new track data.

If it is determined in step 705 that there is no correlation, it is checked whether there is a correlation-checked track in the correlation data management table 403 that has not yet been checked for correlation with the uncorrelated track (step 707). Step 701
Return Loop processing is repeated. If not, it means that the correlation check has been performed on all the tracks for which correlation check has been registered in the management table, so that it is registered that there is no correlation (step 708). This is in the management table of FIG.
This means that a new common ID number 801 is given to the track, and the track number and the like are registered in the management table, meaning that the data becomes correlation-checked data. Then return to step 501,
It again waits for reception of track data from the outside.

The common ID number 801 of the correlation data management table 403 obtained in this manner distinguishes between an independent flying object and a flying object that is overlapped by each monitoring system device 102, and includes a plurality of overlapping wakes. 1
Identified as being based on one flying object.

[0028]

As described above, since the present invention uses history information and is a correlation process within a section of the history information, even if the interpolation function is expressed by a higher-order polynomial, the error of the estimated wake data may be reduced. Can be reduced, and the correlation processing can be executed with higher accuracy than the conventional extrapolation method using linear prediction. As a result, even in the correlation processing for a flying object performing an acceleration motion, it is possible to obtain the correlation with higher accuracy than in the conventional method.

[Brief description of the drawings]

FIG. 1 is a system configuration diagram of an embodiment.

FIG. 2 is a diagram illustrating a format of track data according to the embodiment.

FIG. 3 is a diagram showing a schematic processing flow of the embodiment.

FIG. 4 is a diagram illustrating a configuration of data stored in a wake data recording unit according to the embodiment.

FIG. 5 is a flowchart of the wake data reception, history registration, and new uncorrelated data determination processing of the embodiment.

FIG. 6 is a diagram illustrating an interpolation function according to the embodiment.

FIG. 7 is a flowchart regarding a correlation process according to the embodiment.

FIG. 8 is a diagram illustrating a configuration of a correlation data management table according to the embodiment.

FIG. 9 is a diagram illustrating selection of an interpolation function and interpolation values according to the embodiment.

[Explanation of symbols]

101: track management device, 102: monitoring system device, 104: track data recording unit, 402: track data registration area, 403: correlation data management table

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Takekatsu Saito 216 Totsuka-cho, Totsuka-ku, Yokohama-shi, Kanagawa Prefecture Inside the Defense Systems Division, Hitachi, Ltd. 216 F-term in Hitachi, Ltd. Defense System Business Division (Reference) 5J070 AD01 AE04 AH04 AK22 BD01

Claims (10)

[Claims] 1. A wake data including a wake identifier, time information, position information and speed information of a flying object is sequentially received from each of a plurality of wake monitoring systems, and a wake identified by the wake identifier is used as another wake monitoring system. For determining whether a track based on the same flying object and a track based on a different flying object from the track received from the first and second tracking units, and wherein a plurality of track data are stored for each of the track monitoring systems and for each of the track identifiers. Second storage means for separately registering a track identifier of a track based on the same flying object and a track identifier of a track based on a different flying object; and storing the track data received from the track monitoring system in the first Means for storing in a corresponding area of the storage means, and the wake identifier included in the wake data received with reference to the second storage means, Means for determining whether the track information matches one of the track identifiers registered in the storage means, and a time at which the position information is interpolated for the plurality of track data received when the track information does not correspond to any of the track identifiers. Means for calculating a function, and means for determining whether or not the position information regarding time has a correlation with the function with respect to the track data including the track identifier registered in the second storage means. Means for registering in the second storage means as a track identifier of a track based on the same flying object as a track having a correlation when there is no correlation with any track data; Means for registering a track identifier of a track based on different flying objects in the second storage means. Equipment to do. 2. The apparatus according to claim 1, wherein the function is a third-order polynomial function. 3. The correlation of a multi-sensor wake according to claim 1, wherein the function is set so that a speed component at a time represented by the time information becomes the speed information related to the time information. A device that performs processing. 4. When it is determined that the track identifier included in the received track data matches one of the track identifiers registered in the second storage means, the time included in the track data is further determined. When a predetermined time or more has passed since the time information of the latest track data stored in the first storage means in a time-series manner, the track identifier determined to match is deleted from the second storage means. The apparatus according to claim 1, further comprising means for determining the track identifier of the track data as a track identifier that does not correspond to any track identifier. 5. The wake data further includes information indicating a quality level of the data, and the means for judging whether or not the wake data has the correlation further stores a plurality of data based on the same flying object in the second storage means. If the track identifier is registered,
2. The apparatus according to claim 1, further comprising means for selecting the wake data having the highest quality level to determine whether or not the wake data has the correlation. 6. A computer sequentially receives wake data including a wake identifier, time information, position information of a flying object, and speed information from each of a plurality of wake monitoring systems, and determines that the wake identified by the wake identifier is another wake data. A program for executing a process of determining whether a track based on the same flight object as a track received from a track monitoring system or a track based on a different flying object, the computer comprising: for each of the track monitoring systems, for each of the track identifiers, Storing the plurality of wake data received from the wake monitoring system in the first storage means, wherein a wake identifier of a wake based on the same flying object and a wake identifier of a wake based on a different flying object are registered separately. The wake identifier included in the wake data received with reference to the second storage means is registered in the second storage means. Determining whether or not the track information matches any of the track identifiers; calculating a function relating to a time at which the position information is interpolated for the plurality of track data received when the track information does not correspond to any of the track identifiers; A procedure for determining whether or not the position information relating to time has a correlation with the function with respect to the wake data including the wake identifier registered in the storage means of No. 2, when there is a correlation with any of the wake data Registering the track in the second storage means as a track identifier of a track based on the same flying object as a track having a correlation; and as a track identifier of a track based on a different flying object when there is no correlation with any track data. A program for executing a procedure for registering in the second storage means. 7. The program according to claim 6, wherein said function is a cubic polynomial function. 8. The program according to claim 6, wherein the function is set such that a speed component at a time represented by the time information becomes the speed information related to the time information. 9. When it is determined that the track identifier included in the received track data matches one of the track identifiers registered in the second storage means, the time included in the track data is further determined. When a predetermined time or more has passed since the time information of the latest track data stored in the first storage means in a time-series manner, the track identifier determined to match is deleted from the second storage means. 7. The program according to claim 6, wherein the program causes the computer to execute a procedure of determining the track identifier of the track data as a track identifier that does not correspond to any track identifier. 10. The wake data further includes information indicating the quality level of the data, and the step of determining whether or not the wake data has the correlation further includes the step of storing a plurality of data based on the same flying object in the second storage means. 7. The program according to claim 6, further comprising a step of, when a track identifier is registered, selecting the track data having the highest quality level and determining whether or not the track data has the correlation.