CN106169056A - A kind of smart antenna self-adapting control algolithm of dynamic residence time - Google Patents

Abstract

The invention relates to a smart antenna self-adaptive control algorithm for dynamic dwell time, and belongs to the technical field of radio frequency communication and antenna. The algorithm uses the Chebyshev inequality to estimate the number of tags, selects the optimal frame length according to the frame time slot ALOHA algorithm, and calculates the number of scans required by each beam, and uses a circular queue to realize adaptive control of beam switching to achieve adaptive tag identification the goal of. The algorithm can not only adaptively control the switching sequence of the beams, but also adaptively control the dwell time of the beams. The algorithm can reduce the tag recognition time, improve the tag recognition rate, and reduce energy consumption at the same time, which has important significance and broad application prospects for UHF RFID system recognition.

Description

A Smart Antenna Adaptive Control Algorithm with Dynamic Dwell Time

technical field

The invention belongs to the field of radio frequency communication and antenna technology, and in particular relates to an adaptive control algorithm of a smart antenna with a dynamic dwell time.

Background technique

RFID (Radio Frequency Identification) technology uses radio frequency signals to conduct non-contact two-way communication, automatically identify target objects and obtain relevant information data, and has been successfully applied in various fields such as manufacturing, logistics and warehousing, transportation, medical and health, and public safety. It uses the spatial coupling of radio frequency signals to realize contactless data transmission, and achieves the purpose of identifying objects through the mutual transmission of information.

The maximum recognition distance of commonly used readers is about 10 meters, and the total recognition area is limited, which cannot meet the needs of most users, thus limiting its large-scale application. Another key problem is that it is difficult to achieve 100% recognition rate. There are two main reasons: First, because of the limited transmission power of the antenna and the fixed radiation pattern, the effective identification area of the RFID system is limited. At the same time, multipath interference causes some tags in the identification area to not get enough Second, due to the non-contact data transmission of passive UHF RFID, it is easy to cause "collision" of multi-tag communication, so that the data information cannot be transmitted correctly, which affects the correct reading of the reader.

The smart antenna can use the combination of multiple antenna array elements for signal processing, and automatically adjust the transmission and reception patterns. Smart antennas can be divided into fixed multi-beam antennas, beam switching smart antennas and adaptive smart antennas according to different working principles. Among them, the beam-switching smart antenna has the advantages of simple structure, no need to judge the direction of arrival of user signals, and fast response speed. More importantly, the same beam of the uplink can also be used for the downlink, thus providing gain on the downlink. , its potential application value has been paid more and more attention at home and abroad. The introduction of smart antenna technology into the passive UHF RFID reader system helps to improve the performance of the existing reader's maximum recognition distance, coverage area, anti-collision, positioning and anti-interference.

When the receiving power of the tag antenna remains unchanged, increasing the recognition distance requires increasing the transmitting power or increasing the gain of the reader antenna. The increase of the gain of the single-beam antenna reduces the beam width and the coverage of the reader. The beam-switching array antenna increases the gain of the antenna and narrows the beam. Since the beam scans in multiple directions, the total identification area is expanded. As shown in Figure 1, the coverage areas of beams 1, 2, 3, and 4 The sum is larger than the coverage area of a single low-gain fixed beam.

Existing research mainly focuses on the anti-collision algorithm of tags, but there is no corresponding adaptive control algorithm for beam switching. The present invention combines the channel model of the communication between the reader and the tag of the beam-switching smart antenna, based on the maximum recognition distance of the reader, the effective recognition area, and the influence of the reference tag density on anti-collision, and proposes a UHF RFID reading Beam switching adaptive control algorithm for smart antennas. The algorithm can determine the dwell time and scanning interval time of the beam according to the number of tags in each beam direction, communication time, etc., so as to achieve the purpose of adaptively controlling the beam.

Contents of the invention

The purpose of the present invention is to propose a smart antenna adaptive control algorithm for dynamic dwell time, which can determine the dwell time and scan interval time of the beam according to the number of tags in each beam direction, communication time, etc., to achieve automatic Adapt to the purpose of controlling the beam to reduce the identification time, improve the tag identification rate, and reduce energy consumption at the same time.

A smart antenna adaptive control algorithm for dynamic dwell time, comprising the following steps: Step 1: Scan all tags within the identification range, and use Chebyshev inequality to estimate the number of tags to be identified within the beam range;

Step 2: Determine the scanning dwell time T _ij according to the number of tags in the beam range and the dynamic frame time slot ALOHA algorithm, i represents the current scanning round number, j represents the serial number of the beam area, scan each beam in this round The corresponding residence time is stored in the circular queue in sequence.

Step 3: Scan each beam in turn, use the Chebyshev inequality again to estimate the number of remaining tags to be identified, and calculate the frame length T _ij corresponding to each beam in the next round of scanning, if the number of tags to be identified within a certain beam range is zero, then set the corresponding T _ij to 0, and then store these frame lengths in the circular queue.

Step 4: Repeat step 3 until the number of tags to be identified in all beam ranges is 0, indicating that all tags in the range have been identified.

Description of drawings:

In order to more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. For an embodiment, those of ordinary skill in the art can also obtain other drawings according to the drawings without any creative work.

Figure 1 is a schematic diagram of beam switching array antenna scanning;

Figure 2 is the optimal frame length for different numbers of tags to be identified;

Fig. 3 is a schematic diagram of four-beam switching adaptive control scanning in the present invention;

Fig. 4 is the time graph that reads label consumption;

Figure 5 is a graph of the energy consumed to read a tag.

detailed description:

The gist of the present invention is to propose a smart antenna adaptive control algorithm for dynamic dwell time, which can determine the dwell time and scanning interval of the beam according to the number of tags in each beam direction, communication time, etc., to achieve automatic Adapt to the purpose of controlling the beam to reduce the identification time, improve the tag identification rate, and reduce energy consumption at the same time. The embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

1. Chebyshev inequality algorithm estimates the number of labels

Assuming that there are a total of n tags to be identified within the recognition range of the reader, and the frame length is L, the probability of r tags occupying a time slot obeys the binomial distribution, namely:

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left(\begin{array}{c}\mathrm{<span\; class="notranslate">\; r</span>}\\ \mathrm{<span\; class="notranslate">\; no</span>}\end{array}\right){<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; r</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Then the probability of a single tag response slot

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Probability of a free slot

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

Probability of Multi-Tag Response Slots

P(k,n,L)=1-P(1,n,L)-P(0,n,L), k>1 (4)

Randomly define the number of initial idle time slots, the number of successfully identified time slots and the number of collision time slots as c ₀ , c ₁ , and c _k , respectively, and calculate the number of idle time slots according to formulas (1)-(4) The number of time slots successfully identified and the number of collision slots theoretically calculated value. In the value range [c ₁ +2c _k ,...,2*(c ₁ +2c _k )] of the number of labels to be identified n, use Chebyshev’s inequality

$\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; |</span>\left(\begin{array}{c}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}}\\ {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}}\end{array}\right)<span\; class="notranslate">\; -</span>\left(\begin{array}{c}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ {\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right)<span\; class="notranslate">\; |</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Find the smallest ε(L, c ₀ , c ₁ , c _k ), and the corresponding n is the number of labels to be identified.

2. Optimal frame length selection for different number of tags to be recognized

The frame slot ALOHA algorithm can maintain the highest throughput rate only when the frame length is approximately equal to the number of tags. When the number of tags to be recognized is greater than the frame length, the tag recognition time will also increase rapidly due to collisions; when the number of tags to be recognized is less than When the frame is long, a large amount of time slots will be wasted. Therefore, in order to maintain a high throughput throughout the identification process, the frame length must be variable, and the selection of the system frame length needs to be adjusted in real time. Figure 2 shows the optimal frame length for different numbers of tags to be identified.

3. Circular Queue Storage

The circular queue is a storage structure that can realize "first in, first out". It can realize the sequential storage and circular reading of multiple beams. The name of each beam and the number of times to be scanned are stored in the circular queue. There is a head in the circular queue The pointer can realize data storage and reading according to the pointer of the head pointer, so we use the circular queue method to realize the beam switching adaptive control algorithm. When a beam is scanned, the number of scans required for this beam is reduced by one. When the number of scans required for a certain beam is 0, the beam is no longer scanned and directly switched to the next beam. If the number of scans required for all beams is 0, it means that all tags have been identified.

4. Example analysis

FIG. 3 is a schematic diagram of four-beam switching adaptive control scanning in the present invention. Assuming that the array antenna can generate 4 beams, the algorithm is described in detail in conjunction with FIG. 3 .

Step 1: First scan all the tags within the identification range, and use the Chebyshev inequality to estimate the number of tags to be identified within the range of the four beams.

Step 2: According to Table 2, select the dwell time T _ij of this scan corresponding to each beam, where the subscript i indicates the current scanning round number, and the subscript j indicates the serial number of the beam area. Store the dwell time corresponding to the current round of scanning of each beam in turn in the circular queue, taking T ₁₁ =16ms, T ₁₂ =4ms, T ₁₃ =32ms, T ₁₄ =48ms as examples, as shown in Figure 3(a) shown.

Step 3: Scan the first area in the circular queue, the scanning time is T ₁₁ , after scanning the first area, adjust the pointing angle of the antenna, point to beam 2, and scan for T ₁₂ time, as shown in Figure 3(b ) shown.

Step 4: Scan the four beams one by one, as shown in Fig. 3(c) and (d). Use the Chebyshev inequality again to estimate the number of remaining tags to be identified within the range of the four beams, and calculate the frame length T _ij corresponding to each beam in the next round of scanning. If the number of tags to be identified in a certain beam range is zero, Then the corresponding T _ij is set to 0, and then these frame lengths are correspondingly stored in the circular queue, taking T ₂₁ =10ms, T ₂₂ =0ms, T ₂₃ =16ms, T ₂₄ =36ms as an example, as shown in Figure 3 (e ) shown.

Step 5: Scan the four beams again according to the corresponding scanning time stored in the circular queue. If T _ij =0, the current beam is skipped, and the next beam is scanned directly, as shown in FIG. 3( f ), T ₂₂ =0 ms, so beam 2 is skipped directly, and beam 3 is scanned. After this round of scanning is completed, the number of tags to be identified within the range of the four beams is estimated by using the Chebyshev inequality again.

Step 6: Until the number of tags to be identified in all beam ranges is 0, it means that all tags in the range have been identified.

Figure 4 is the time diagram of reading tags, and Figure 5 is the energy consumption of reading tags, assuming that there are 1000 tags randomly distributed in the identification area, and each tag can be correctly identified, from Figure 4 we can see It can be seen that the time consumed by the beam switching algorithm using dynamic dwell time is significantly less than the time consumed by low gain dynamic beam; from Figure 5, it can be seen that the energy consumed by the beam switching algorithm using dynamic dwell time is significantly less than that of low gain The energy consumed by the fixed beam.

Examples show that the smart antenna adaptive control algorithm with dynamic dwell time can achieve the purpose of adaptive beam control, reduce the recognition time, improve the tag recognition rate, and reduce energy consumption, which is of great significance and application prospect for UHF RFID system recognition.

Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.

The above descriptions are only preferred embodiments of the present invention, and do not limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention within.

Claims (4)

1. a smart antenna self-adapting control algolithm for dynamic residence time, is characterized in that comprising the following steps:

Step 1: all labels in identification range are scanned, utilize Chebyshev inequality to estimate in beam area Number of tags to be identified；

Step 2: determine the residence time T of scanning according to the number of tags in beam area and dynamic frame CDMA slotted ALOHA algorithm_ij, i Representing the wheel number of Current Scan, j represents the sequence number of beam area, respectively by the residence time corresponding to each this wheel scan of wave beam It is stored in successively in round-robin queue.

Step 3: be scanned each wave beam successively, remains number of tags to be identified again with Chebyshev inequality estimation Mesh, calculates next round and scans the frame length T that each wave beam is corresponding_ijIf number of tags to be identified is in some beam area Zero, then by corresponding T_ijIt is set to 0, then these frame length correspondences is stored in round-robin queue.

Step 4: repeat step 3, until the number of tags to be identified in all of beam area is all 0, then all in the range of showing Label all identifies complete.

The smart antenna self-adapting control algolithm of a kind of dynamic residence time the most according to claim 1, is characterized in that: step In rapid 1, Chebyshev inequality is utilized to estimate the number of tags to be identified in beam area.

The smart antenna self-adapting control algolithm of a kind of dynamic residence time the most according to claim 1, is characterized in that: step In rapid 2, utilize dynamic frame CDMA slotted ALOHA algorithms selection optimum length of frame, so that it is determined that go out the residence time T of scanning_ij。

The smart antenna self-adapting control algolithm of a kind of dynamic residence time the most according to claim 1, is characterized in that: step In rapid 3, introduce round-robin queue and realize storage.