An unmanned aerial vehicle low false alarm identification method based on radar multi-dimensional micro-motion characteristics

Abstract

The application discloses a kind of based on radar multidimensional micro-motion feature's unmanned plane low false alarm identification method, belong to radar target identification technical field, for solving in existing unmanned plane low altitude target identification, the identification performance of target model is difficult to guarantee, it is difficult to realize the technical problem of extremely low false alarm rate to target.Extract processing multidimensional feature between target main signal and micro-motion signal under low altitude according to target micro-motion doppler spectrum, obtain target multidimensional micro-motion feature;Target multidimensional micro-motion feature is set to multi-stage discrimination threshold processing, in series and obtain series three-level threshold determination mechanism;Threshold determination result based on series three-level threshold determination mechanism, obtain target identification result under target track;Through the preset dynamic sliding window, the single target identification result in track level is time series fusion and target identification optimization processing, obtain optimization target identification result in the same target track.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar target recognition, and particularly relates to a low false alarm recognition method for unmanned aerial vehicles based on radar multi-dimensional micro-motion characteristics. BACKGROUND

[0002] Low-altitude target type recognition plays an important supporting role in aviation safety guarantee and low-altitude situation awareness research, and is one of the key tasks of radar low-altitude target detection. However, most of the existing radar recognition methods for unmanned aerial vehicles use signal decomposition or time-frequency transformation methods to extract features, and combine machine learning or deep learning algorithms to realize target classification. Such methods have obvious limitations: on the one hand, the intelligent recognition method based on data has limited generalization ability for target models outside the database, and the recognition performance is difficult to guarantee; on the other hand, such methods usually take high recognition accuracy as the optimization goal, but it is difficult to achieve extremely low false alarm rate in actual application, thereby causing problems such as data sensitivity and insufficient false alarm control ability of the system.

[0003] Therefore, it is urgent to develop a low false alarm recognition method for unmanned aerial vehicle targets based on radar multi-dimensional micro-motion characteristics, which can effectively extract radar discrimination features according to the micro-motion characteristics of low-altitude targets, and effectively control the false alarm while achieving high-precision recognition. SUMMARY

[0004] The embodiment of the present application provides a low false alarm recognition method for unmanned aerial vehicles based on radar multi-dimensional micro-motion characteristics, which is used to solve the technical problem that in the existing low-altitude target recognition for unmanned aerial vehicles, the recognition performance of the target model is difficult to guarantee, and it is difficult to achieve an extremely low false alarm rate for the target.

[0005] The embodiment of the present application adopts the following technical scheme:

[0006] On the one hand, the embodiment of the present application provides a low false alarm recognition method for unmanned aerial vehicles based on radar multi-dimensional micro-motion characteristics, comprising: according to the target micro-motion Doppler spectrum, multi-dimensional feature extraction and processing between the target main body signal and the micro-motion signal under low altitude are performed, to obtain target multi-dimensional micro-motion characteristics; wherein the target multi-dimensional micro-motion characteristics include harmonic interval characteristics, harmonic isolation characteristics and main harmonic ratio characteristics; according to the micro-motion characteristics of the low-altitude target, the target multi-dimensional micro-motion characteristics are subjected to multi-level threshold setting processing, and a series three-level threshold determination mechanism is obtained in series; based on the threshold determination result of the series three-level threshold determination mechanism, a target recognition result under the target track is obtained; through a pre-set dynamic sliding window, a single target recognition result in the track level is subjected to time sequence fusion and target recognition optimization processing, to obtain an optimized target recognition result in the same target track.

[0007] The embodiments of the present application can realize high accuracy and low false alarm recognition of low-altitude unmanned aerial vehicle targets through low false alarm recognition of the unmanned aerial vehicle based on radar multi-dimensional micro-motion characteristics. The embodiments can effectively adapt to the changes in target motion state and target model, and significantly improve the discrimination ability of unmanned aerial vehicles and non-unmanned aerial vehicle targets. Especially when facing out-of-database targets, the proposed recognition based on target micro-motion physical characteristics does not rely on data-driven mode, thereby effectively reducing the dependence of the recognition method on sample data. At the same time, through the dynamic sliding window optimization strategy, the track level can also enhance the time sequence consistency of the recognition result, and improve the reliability and practicality of the system in complex low-altitude environment.

[0008] In a feasible implementation, before the multi-dimensional characteristics between the target main body signal and the micro-motion signal under low altitude are extracted and processed according to the target micro-motion Doppler spectrum to obtain the target multi-dimensional micro-motion characteristics, the method further includes: performing target detection processing on the distance-Doppler plane of the current track point echo through a low false alarm rate CFAR detector to obtain a first target detection result; performing target micro-motion harmonic signal detection processing on the target in the current track point based on the Doppler domain and through a two-stage CFAR detector to obtain a second target detection result; and performing detection fusion extraction processing on the target with respect to the target main body signal and the micro-motion signal according to the first target detection result and the second target detection result to obtain a target Doppler domain detection result.

[0009] In an implementation, according to the target micro-Doppler spectrum, a multi-dimensional feature extraction process is performed between the target main body signal and the micro-motion signal under low altitude, to obtain a target multi-dimensional micro-motion feature, which specifically includes: according to the target Doppler domain detection result, and based on the target micro-Doppler spectrum, the harmonic with the smallest interval from the main Doppler frequency is selected; and the smallest harmonic and the main Doppler frequency are difference calculated to obtain the fundamental frequency; based on the fundamental frequency, a search process is performed on whether there is a high-order harmonic in the preset absolute frequency error tolerance, to detect and obtain a plurality of high-order harmonic points, and the high-order harmonic points are included in a harmonic set; the harmonic set and the main Doppler are jointly processed to obtain the harmonic interval feature of the target; based on the harmonic interval feature, a pair of adjacent harmonic points of the target are located; the average power of the pair of adjacent harmonic points is calculated and determined as the harmonic power; the average power of adjacent signals in the pair of adjacent harmonic points is calculated and determined as the noise floor power; the harmonic power and the noise floor power are difference calculated to obtain the energy focusing metric of the pair of adjacent harmonic points; all adjacent harmonic pairs are traversed to obtain a set of all power difference values based on different energy focusing metrics; and the set of all power difference values is determined as the harmonic isolation feature of the target; based on the amplitude decibel value of each harmonic in the harmonic interval feature, the main harmonic ratio feature is obtained; the harmonic interval feature, the harmonic isolation feature, and the main harmonic ratio feature are combined into a multi-dimensional feature to obtain the target multi-dimensional micro-motion feature.

[0010] In an implementation, based on the amplitude decibel value of each harmonic in the harmonic interval feature, the main harmonic ratio feature is obtained, which specifically includes: obtaining the amplitude decibel value of each harmonic in the harmonic interval feature; the amplitudes of each harmonic in the harmonic interval feature are numerically compared, and the harmonic with the largest amplitude is determined as the representative harmonic of the micro-Doppler; the target main Doppler and the maximum harmonic amplitude decibel value of the representative harmonic are difference calculated to obtain the main harmonic ratio feature.

[0011] In an embodiment, the target multi-dimensional micro-motion characteristics are subjected to multi-stage threshold setting processing according to the low-altitude target micro-motion characteristics, and a series of three-stage threshold determination mechanisms are obtained in series, specifically including: according to the unmanned aerial vehicle micro-motion model, determining the target category according to whether the target Doppler spectrum contains a preset number of harmonics, and based on a first determination rule, a first-stage threshold determination mechanism based on the harmonic interval characteristics is constructed; the first-stage threshold determination mechanism includes: potential unmanned aerial vehicles and non-unmanned aerial vehicles; according to the high signal-to-noise ratio and narrow band characteristics of the harmonic components of the unmanned aerial vehicle, the threshold of the harmonic signal of the target under high signal-to-noise ratio and narrow bandwidth is determined, and based on a second determination rule, a second-stage threshold determination mechanism based on the harmonic isolation characteristics is constructed; the second-stage threshold determination mechanism includes: potential unmanned aerial vehicles and non-unmanned aerial vehicles; according to the radar scattering interface of the unmanned aerial vehicle body, the power ratio between the target main Doppler component and the strongest harmonic component of the target is calculated to obtain the minimum main harmonic ratio; based on the threshold judgment condition of the minimum main harmonic ratio and a third determination rule, a third-stage threshold determination mechanism based on the main harmonic ratio characteristics is obtained; the third-stage threshold determination mechanism includes: potential unmanned aerial vehicles and non-unmanned aerial vehicles; the first-stage threshold determination mechanism, the second-stage threshold determination mechanism, and the third-stage threshold determination mechanism are sequentially subjected to threshold series processing to obtain the series of three-stage threshold determination mechanisms.

[0012] In an embodiment, based on the threshold determination result of the series of three-stage threshold determination mechanisms, a target recognition result under a target track is obtained, specifically including: through the series of three-stage threshold determination mechanisms, the target multi-dimensional micro-motion characteristics of the target are sequentially subjected to threshold determination; if potential unmanned aerial vehicles are present in each threshold determination mechanism, the threshold determination result is determined as an unmanned aerial vehicle recognition result; if non-unmanned aerial vehicles are present in each threshold determination mechanism, the threshold determination result is determined as a non-unmanned aerial vehicle discrimination result; based on the unmanned aerial vehicle recognition result and the non-unmanned aerial vehicle discrimination result, the target recognition result under the target track is obtained.

[0013] In an embodiment, through a preset dynamic sliding window, a single target recognition result in a track level is subjected to time sequence fusion and target recognition optimization processing to obtain an optimized target recognition result in the same target track, specifically including: through the dynamic sliding window, each target recognition result in the same track level is subjected to sliding strategy control; the historical sliding window optimization result in the dynamic sliding window is taken as a window reference value; through the window reference value, the single target recognition result in the same target track is subjected to recursive recognition update; and based on the historical sliding window optimization result, the current track target recognition result is subjected to consistency constraint to complete abnormal fluctuation correction processing of the single target recognition result to obtain the optimized target recognition result of each track point in the same target track.

[0014] In one feasible implementation, according to The fusion judgment condition is obtained. ;in, For the aforementioned dynamic sliding window; For reference window Weighting factors; for The historical target identification result in the historical sliding window optimization result at the given time; t is the time corresponding to the track point in the same target track; according to The fusion decision function is obtained. ; wherein, the The historical target identification result, and ;according to The optimized target recognition result is obtained. ;in, This represents the original target identification results at the track level.

[0015] This application provides a low false alarm rate identification method for unmanned aerial vehicles (UAVs) based on radar multidimensional micro-motion features. Compared with the prior art, the embodiments of this application have the following beneficial technical effects:

[0016] 1. It can achieve high accuracy and low false alarm rate in identifying low-altitude UAV targets.

[0017] 2. It can effectively adapt to changes in the target's motion state and the target's model, significantly improving the ability to distinguish between UAVs and non-UAV targets.

[0018] 3. When facing targets outside the database, the proposed identification method is based on the physical characteristics of the target microorganisms and does not rely on a data-driven model, thus effectively reducing the dependence of the identification method on sample data.

[0019] 4. Furthermore, the dynamic sliding window optimization strategy can be used to enhance the temporal consistency of track-level recognition results, thereby improving the reliability and practicality of the system in complex low-altitude environments. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0021] Figure 1 A flowchart of a method for identifying low false alarms of unmanned aerial vehicles (UAVs) based on radar multidimensional micro-motion features is provided in this application embodiment;

[0022] Figure 2 A low false alarm recognition whole process schematic diagram of a UAV is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0023] In order for those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0024] It should be noted that the present application includes three parts: low-altitude target radar micro-Doppler feature extraction, low-altitude target recognition based on micro-motion characteristics, and recognition result timing optimization based on dynamic sliding window. In the feature extraction part, the present application is based on the CFAR constant false alarm detection algorithm, and a distance-Doppler domain two-level detection mechanism is constructed, and the effective extraction of the low-altitude target multi-dimensional micro-Doppler discrimination feature is extracted based on the detection result. In the target recognition part, the present application proposes a low-altitude target recognition method based on multi-level threshold discrimination according to the extracted multi-dimensional radar features, and realizes the robust recognition of UAV and non-UAV targets. In the recognition optimization part, the present application proposes a dynamic sliding window recognition result optimization method, which realizes the timing fusion and context perception correction of the recognition result of each track point at the track level, thereby effectively improving the continuity and reliability of the recognition result.

[0025] At the same time, based on the low false alarm recognition method of UAV target based on radar multi-dimensional micro-motion characteristics, a radar needs to be provided in advance to continuously observe the low-altitude area to obtain the target echo electromagnetic signal, and then the present application extracts the target multi-dimensional micro-Doppler discrimination feature from the current track point echo based on the distance-Doppler two-level detection method, and constructs a multi-level discrimination threshold based on the differential micro-motion characteristics of low-altitude targets, and realizes the robust recognition of UAV and non-UAV targets. Finally, the present application uses a dynamic sliding window to perform timing fusion and recognition result correction on the recognition result at the track level, effectively improves the recognition consistency and false alarm suppression ability, and finally realizes the high-confidence and low false alarm recognition of UAV targets in a complex low-altitude environment.

[0026] The embodiments of the present application provide a low false alarm recognition method of UAV based on radar multi-dimensional micro-motion characteristics, as shown in Figure 1 The low false alarm recognition method of UAV based on radar multi-dimensional micro-motion characteristics specifically includes steps S101-S104:

[0027] S101, according to the target micro-doppler spectrum, the multi-dimensional feature extraction processing between the target main body signal and the micro-motion signal under the low altitude is performed to obtain the target multi-dimensional micro-motion feature. Wherein, the target multi-dimensional micro-motion feature includes: harmonic interval feature, harmonic isolated feature and main harmonic ratio feature.

[0028] Specifically, the target detection processing needs to be performed on the range-doppler plane of the current track point echo by the low false alarm rate CFAR detector to obtain the first target detection result.

[0029] Further, based on the Doppler domain, the target micro-harmonic signal detection processing of the target in the current track point is performed by the secondary CFAR detector to obtain the second target detection result.

[0030] Further, according to the first target detection result and the second target detection result, the detection fusion extraction processing of the target related to the target main body signal and the micro-motion signal is performed to obtain the target Doppler domain detection result.

[0031] In one embodiment, Figure 2 An unmanned aerial vehicle low false alarm identification whole process schematic diagram provided by the embodiment of the application is shown in Figure 2 The low-altitude target usually provides lift and power through the micro-motion behavior of rotor rotation and wing flapping, and different micro-motion modes correspond to significantly different Doppler spectrum expansion characteristics. In order to extract the discriminative Doppler spectrum feature, the secondary detection process needs to be adopted: first, the low false alarm rate CFAR detector is used to perform target detection on the range-doppler (RD) plane of the current track point echo to obtain the first target detection result; second, the secondary CFAR detector is used to detect the micro-harmonic signal of the target in the Doppler domain to obtain the second target detection result; finally, the micro-motion feature of the target is comprehensively extracted from the target Doppler domain detection result according to the two-stage detection results.

[0032] Further, according to the target Doppler domain detection result and based on the target micro-doppler spectrum, the harmonic with the smallest interval from the main Doppler frequency is selected. The difference between the smallest harmonic and the main Doppler frequency is calculated to obtain the fundamental frequency. Then, based on the fundamental frequency, the search processing of whether there is a high-order harmonic is performed on the preset absolute frequency error tolerance, a plurality of high-order harmonic points are detected and obtained, and the high-order harmonic points are included in the harmonic set. Finally, the harmonic set and the main Doppler are jointly processed to obtain the harmonic interval feature of the target.

[0033] In one embodiment, as shown in Figure 2 In the harmonic interval feature extraction, the harmonic with the smallest interval from the main Doppler frequency is selected according to the target Doppler domain detection result, and the difference between the harmonic and the main Doppler frequency is calculated , As the fundamental frequency, within the set absolute frequency error tolerance... The system searches for the presence of higher harmonics. Detected higher harmonic points are included in the harmonic set and, together with the main Doppler, constitute the harmonic interval characteristics of the target. If... If a sufficient number of harmonics are not found at the fundamental frequency (3-5 is an empirical value, and the specific quantitative value can be adjusted according to the actual project), then the second smallest interval is used instead. As the fundamental frequency, repeat the above process until all candidate fundamental frequencies have been traversed.

[0034] Furthermore, based on the harmonic interval characteristics, a pair of adjacent harmonic points of the target are first located. Then, the average power of the pair of adjacent harmonic points is calculated and determined as the harmonic power. Next, the average power of the adjacent signals in the pair of adjacent harmonic points is calculated and determined as the noise floor power. The difference between the harmonic power and the noise floor power is also calculated to obtain the energy focusing metric of the pair of adjacent harmonic points. Finally, all adjacent harmonic pairs are traversed to obtain the set of all power difference values ​​based on different energy focusing metrics. The set of all power difference values ​​is then determined as the harmonic isolation characteristic of the target.

[0035] In one embodiment, such as Figure 2 As shown, in the harmonic isolation feature extraction, firstly, using the extracted harmonic interval features as priors, a pair of adjacent harmonic points are located. Secondly, the average power of this pair of harmonics is calculated and used as the harmonic power. Then, the average power of the signal between two adjacent harmonics is calculated and used as the noise floor power. Further, the difference between the harmonic power and the noise floor power is calculated as the energy focusing measure of this pair of harmonic signals. Finally, all adjacent harmonic pairs are traversed to obtain the set of all power difference values, which constitute the harmonic isolation feature.

[0036] Furthermore, the principal harmonic ratio is first obtained based on the amplitude decibel values ​​of each harmonic in the harmonic interval characteristics. That is, the amplitude decibel values ​​of each harmonic in the harmonic interval characteristics need to be obtained first. Then, the amplitudes of each harmonic in the harmonic interval characteristics are numerically compared, and the harmonic with the largest amplitude is determined as the representative harmonic of the micro-Doppler. Finally, the difference between the target principal Doppler and the maximum harmonic amplitude decibel value of the representative harmonic is calculated to obtain the principal harmonic ratio characteristics.

[0037] In one embodiment, such as Figure 2 As shown, in the extraction of principal harmonic ratio features, firstly, the amplitude decibel value of each harmonic is obtained based on the harmonic interval feature; secondly, the amplitude of each harmonic of the target is compared, and the harmonic with the largest amplitude is determined as the representative harmonic of micro-Doppler; finally, the difference between the target principal Doppler and the amplitude decibel value of the largest harmonic in the representative harmonic is calculated, and this is used as the principal harmonic ratio feature.

[0038] Furthermore, the harmonic interval characteristics, harmonic isolation characteristics, and principal harmonic ratio characteristics are combined in a multidimensional manner to obtain the target multidimensional micro-motion characteristics.

[0039] S102. Based on the micro-motion characteristics of low-altitude targets, multi-level discrimination thresholds are set for the multi-dimensional micro-motion features of the targets, and these thresholds are then connected in series to obtain a series three-level threshold judgment mechanism. That is, multi-level discrimination thresholds are constructed based on the micro-motion characteristics of low-altitude targets to achieve the classification and identification of UAVs and non-UAV targets. Discrimination rules are established for the three micro-motion features: harmonic interval characteristics, harmonic isolation characteristics, and principal harmonic ratio characteristics, forming a series three-level threshold judgment mechanism.

[0040] Specifically, based on the UAV micro-motion model, the target's Doppler spectrum is first processed to determine whether it contains a preset number of harmonics to classify the target. Then, based on the first determination rule, a first-level threshold determination mechanism based on harmonic interval characteristics is constructed. The first-level threshold determination mechanism includes: potential UAVs and non-UAVs.

[0041] In one embodiment, such as Figure 2 As shown, a first-level threshold for discrimination is set based on the harmonic interval characteristics. According to the UAV micro-motion model, its Doppler spectrum should exhibit the characteristic of having the main Doppler as the center and the harmonics equally distributed on both sides of the main Doppler. Therefore, this application will use whether the target spectrum contains a sufficient number of harmonics as the initial criterion for judging the target category. The first judgment rule is as follows: ,in, For the target category, Indicates the number of harmonics detected. This is the characteristic threshold for harmonic intervals.

[0042] Furthermore, based on the high signal-to-noise ratio and narrow bandwidth characteristics of the harmonic components of UAVs, threshold judgments are made on the target's harmonic signals under the relevant signal-to-noise ratio and bandwidth narrowness. Based on the second judgment rule, a two-level threshold judgment mechanism based on harmonic isolation characteristics is constructed. The two-level threshold judgment mechanism includes: potential UAVs and non-UAVs.

[0043] In one embodiment, a second-level threshold is set based on the harmonic solitary characteristics. Ideally, the harmonic components of the UAV should exhibit high signal-to-noise ratio and narrowband characteristics, typically showing a narrowband Sinc spread pattern in simulations and actual measurements. This application uses whether the harmonic signal possesses sufficiently high power salience (i.e., sufficiently high signal-to-noise ratio) and sufficiently narrow bandwidth as the second criterion, namely: ,in This represents the difference in decibels between the harmonic point power and the inter-harmonic noise floor power. This is the threshold for harmonic isolation characteristics.

[0044] Further, the power ratio between the target main Doppler component and the strongest harmonic component is calculated according to the radar scattering interface of the UAV body, and the minimum main harmonic ratio is obtained.

[0045] Further, based on the threshold judgment condition of the minimum main harmonic ratio and the third determination rule, a three-level threshold determination mechanism based on the main harmonic ratio feature is obtained. The three-level threshold determination mechanism includes: potential UAV and non-UAV.

[0046] In one embodiment, a threshold is set based on the main harmonic ratio feature. The radar scattering interface (RCS) of the UAV body is usually much larger than the rotor blade, so that the power of the target main Doppler component is significantly higher than the power of the harmonic component. Therefore, the application takes whether the target minimum main harmonic ratio (i.e. the power ratio of the main Doppler and the strongest harmonic) reaches a certain threshold as a discrimination condition, and the third determination rule is: wherein, is the main Doppler-strongest harmonic power ratio of the target, is the main harmonic ratio feature threshold.

[0047] Further, the first-level threshold determination mechanism, the second-level threshold determination mechanism and the third-level threshold determination mechanism are sequentially processed in series to obtain a series three-level threshold determination mechanism.

[0048] S103, based on the threshold determination result of the series three-level threshold determination mechanism, a target recognition result under the target track is obtained.

[0049] Specifically, the target multi-dimensional micro-motion feature of the target is sequentially threshold judged by using the series three-level threshold determination mechanism.

[0050] Further, if the potential UAV is in each level threshold determination mechanism, the threshold determination result is determined as the UAV recognition result. If there is a non-UAV in each level threshold determination mechanism, the threshold determination result is determined as the non-UAV discrimination result.

[0051] Further, based on the UAV recognition result and the non-UAV discrimination result, a target recognition result under the target track is obtained. That is, the above three thresholds are used in series, and when all the thresholds are met, the target is determined as a UAV, otherwise as a non-UAV, so as to realize low false alarm recognition of the UAV target.

[0052] S104, by using a pre-set dynamic sliding window, a single target recognition result in the track level is time-series fused and target recognition optimized to obtain an optimized target recognition result in the same target track.

[0053] It should be noted that, in order to improve the time consistency and reliability of the identification result, and to suppress false alarms and missed detections caused by noise, clutter or tracking failure in the track point identification result, the application proposes a track level identification optimization method based on a dynamic sliding window. The mechanism does not rely on the traditional "fixed input-fixed output" sliding window mode, but realizes dynamic fusion and real-time correction of the historical results in the window on the identification result sequence of the same track.

[0054] Specifically, first, through the dynamic sliding window, each target identification result in the same track level is controlled by a sliding strategy. Then the historical sliding window optimization result in the dynamic sliding window is used as the reference value in the window.

[0055] Further, the historical sliding window optimization result is used as the reference value in the window to recursively update the single target identification result in the same target track. Based on the historical sliding window optimization result, the consistency constraint of the current track target identification result is performed to complete the abnormal fluctuation correction of the single target identification result, and the optimized target identification result of each track point in the same target track is obtained.

[0056] In one embodiment, consider a target trajectory containing track points , where each time corresponds to a target identification result obtained by step S103 . The traditional sliding window method usually applies a fixed-length window to the original identification sequence, and the reference value in the window is the preliminary identification result . However, the dynamic sliding window strategy proposed in the application uses the historical sliding window optimization result as the reference value in the window during the window sliding process to evaluate the identification result of the current track point, and realizes the recursive update of the results in the window.

[0057] As a feasible implementation, in the specific implementation of the optimized target identification result, the fusion judgment condition can be obtained according to . Wherein, is a dynamic sliding window; is a weight factor of the reference window, and can be allowed in the case of lack of prior information; is the historical target identification result in the historical sliding window optimization result at time ; t is the time corresponding to the track point in the same target track. Then the fusion decision function is obtained according to . Wherein, is the historical target identification result, and . Finally, the sliding window length is , then ​The original identification result of the time track point is The dynamic sliding window optimized identification result is the optimization target identification result That is, according to The optimization target identification result is obtained . Wherein, is the original target identification result in the track level.

[0058] As a feasible implementation manner, the core of the above sliding window optimization mechanism is to correct the current track point identification result by using the consistency constraint of the historical identification result in the window. When the current track point identification result abnormally fluctuates due to the problems such as detection missing of the radar front section, tracking beam deflection or measurement mis-association, the mechanism can correct it according to the optimized and stable identification result in the window, thereby effectively suppressing the jump of the identification result on the same target track and improving the robustness of the overall system performance.

[0059] In one embodiment, the above-mentioned unmanned aerial vehicle low false alarm identification algorithm based on multi-dimensional micro-motion features can be described and analyzed based on a single radar low-altitude target detection scene as an implementation example: after the X-band ground-based radar is erected in a relatively open area, the radar working mode is adjusted to the search and tracking (TAS) mode, the low-altitude area is continuously observed, and 10 batches of target tracks are collected, including 2 batches of unmanned aerial vehicle target data (cooperative targets) and 8 batches of natural target data (bird groups, non-cooperative targets). The unmanned aerial vehicle targets include two small four-rotor unmanned aerial vehicles, DJI Ling and DJI Wu, which are controlled by unmanned aerial vehicle pilots to fly back and forth within a range of 2-3 km from the radar, and the collected bird targets move within a range of 5 km from the radar.

[0060] Table 1: target identification method measured data verification results

[0061]

[0062] After the low-altitude target identification algorithm proposed in the present application is verified by actual measurement, the identification result is shown in Table 1. For the cooperative collection of DJI Ling unmanned aerial vehicles, the identification rate before sliding window optimization is 83.42%, and the identification rate after optimization is improved to 99.85%; the identification rate of DJI Wu unmanned aerial vehicle before optimization is 86.47%, and the identification rate after optimization is 97.40%. In terms of false alarm control, the false alarm rate before optimization is 0.10%, and the false alarm rate after sliding window optimization is reduced to 0.00%. The experimental results show that the low-altitude target radar identification algorithm proposed in the present application can control the false alarm rate at a very low level while maintaining the robust identification ability of unmanned aerial vehicle targets, and has good practical value.

[0063] In addition, the application further provides a low false alarm recognition device for unmanned aerial vehicles based on multi-dimensional micro-motion characteristics, which specifically comprises:

[0064] at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions capable of being executed by the at least one processor, so that the at least one processor can execute:

[0065] According to the target micro-motion Doppler spectrum, multi-dimensional feature extraction processing is performed on the target main body signal and the micro-motion signal under low altitude, to obtain target multi-dimensional micro-motion characteristics; wherein the target multi-dimensional micro-motion characteristics include harmonic interval characteristics, harmonic isolation characteristics and main harmonic ratio characteristics.

[0066] According to the low-altitude target micro-motion characteristics, the target multi-dimensional micro-motion characteristics are subjected to multi-stage threshold setting processing, to obtain a series three-stage threshold determination mechanism.

[0067] Based on the threshold determination result of the series three-stage threshold determination mechanism, a target recognition result under a target track is obtained.

[0068] Through a preset dynamic sliding window, time sequence fusion and target recognition optimization processing are performed on a single target recognition result in a track level, to obtain an optimized target recognition result in the same target track.

[0069] The application can realize high-accuracy and low false alarm recognition of low-altitude unmanned aerial vehicle targets through the low false alarm recognition of unmanned aerial vehicles based on radar multi-dimensional micro-motion characteristics. The application can effectively adapt to the changes in target motion state and target model, and significantly improve the discrimination ability of unmanned aerial vehicles and non-unmanned aerial vehicle targets. Especially when facing out-of-database targets, the application recognizes based on target micro-motion physical characteristics, and does not rely on data-driven mode, thereby effectively reducing the dependence of the recognition method on sample data. At the same time, through the dynamic sliding window optimization strategy, the time sequence consistency of the recognition result can be enhanced at the track level, and the reliability and practicability of the system in a complex low-altitude environment can be improved.

[0070] Each of the embodiments in the application is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments. Especially, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0071] The device and medium provided by the embodiments of the present application are one-to-one corresponding, and therefore the device and medium also have similar beneficial technical effects to the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here again.

[0072] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. In addition, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0073] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0074] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0076] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memories.

[0077] Memory can include non-persistent memory, Random Access Memory (RAM), and / or non-volatile memory, such as Read Only Memory (ROM) or flash memory, in a computer readable medium. Memory is an example of computer readable media.

[0078] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements in the list, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0080] The above description is only some embodiments of the present application, and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of the present application.

Claims

1. A method for low false alarm recognition of a UAV based on radar multi-dimensional micro-motion features, characterized in that, The method comprises: According to the target micro-doppler spectrum, the multi-dimensional feature extraction processing between the target main body signal and the micro-motion signal under low altitude is carried out to obtain the target multi-dimensional micro-motion feature; wherein the target multi-dimensional micro-motion feature includes: harmonic interval feature, harmonic isolation feature and main harmonic ratio feature; According to the low-altitude target micro-motion characteristics, the target multi-dimensional micro-motion feature is subjected to multi-stage discriminant threshold setting processing to obtain a series of three-stage threshold discriminant mechanism; Based on the threshold discriminant result of the series of three-stage threshold discriminant mechanism, the target recognition result under the target track is obtained; Through the preset dynamic sliding window, the single target recognition result in the track level is subjected to time sequence fusion and target recognition optimization processing to obtain the optimized target recognition result in the same target track.

2. The method of claim 1, wherein the method comprises: Before the multi-dimensional feature extraction processing between the target main body signal and the micro-motion signal under low altitude according to the target micro-doppler spectrum is carried out to obtain the target multi-dimensional micro-motion feature, the method further comprises: Through the low false alarm rate CFAR detector, target detection processing is carried out on the range-doppler plane of the current track point echo to obtain the first target detection result; Based on the Doppler domain, and through the two-stage CFAR detector, the target in the current track point is subjected to target micro-motion harmonic signal detection processing to obtain the second target detection result; According to the first target detection result and the second target detection result, the target is subjected to detection fusion extraction processing about the target main body signal and the micro-motion signal to obtain the target Doppler domain detection result.

3. The method of claim 2, wherein the method further comprises: According to the target micro-doppler spectrum, the multi-dimensional feature extraction processing between the target main body signal and the micro-motion signal under low altitude is carried out to obtain the target multi-dimensional micro-motion feature, specifically comprising: According to the target Doppler domain detection result and based on the target micro-doppler spectrum, the harmonic with the smallest interval from the main Doppler frequency is selected; and the difference between the smallest harmonic and the main Doppler frequency is calculated to obtain the fundamental frequency; Based on the fundamental frequency, search processing is carried out on whether there is a high-order harmonic in the preset absolute frequency error tolerance to detect and obtain a plurality of high-order harmonic points, and the high-order harmonic points are included in the harmonic set; The harmonic set and the main Doppler are subjected to common composition processing to obtain the harmonic interval feature of the target; Based on the harmonic interval feature, a pair of adjacent harmonic points of the target are located; The average power of the pair of adjacent harmonic points is calculated and determined as the harmonic power; The average power of the adjacent signals in the pair of adjacent harmonic points is calculated and determined as the noise floor power; The difference between the harmonic power and the noise floor power is calculated to obtain the energy focusing metric of the pair of adjacent harmonic points; All adjacent harmonic pairs are traversed to obtain a set of all power difference values based on different energy focusing metrics; and the set of all power difference values is determined as the harmonic isolation feature of the target; Based on the amplitude decibel value of each harmonic in the harmonic interval feature, the main harmonic ratio feature is obtained; The harmonic interval feature, the harmonic isolation feature and the main harmonic ratio feature are subjected to multi-dimensional feature combination to obtain the target multi-dimensional micro-motion feature.

4. The method of claim 3, wherein the method further comprises: The main harmonic ratio feature is obtained based on the amplitude decibel values of each harmonic in the harmonic interval feature, and specifically includes: Obtaining the amplitude decibel values of each harmonic in the harmonic interval feature; Numerical comparison is performed on the amplitudes of each harmonic in the harmonic interval feature, and the harmonic with the largest amplitude is determined as the representative harmonic of the micro-Doppler; Difference calculation is performed between the target main Doppler and the maximum harmonic amplitude decibel value of the representative harmonic to obtain the main harmonic ratio feature.

5. The method of claim 1, wherein the method further comprises: According to the low-altitude target micro-motion characteristics, the target multi-dimensional micro-motion characteristics are subjected to multi-stage threshold setting processing, and a series of three-stage threshold determination mechanisms are obtained in series, specifically including: According to the UAV micro-motion model, whether the target Doppler spectrum contains a preset number of harmonics is determined as the target category, and based on the first determination rule, a first-stage threshold determination mechanism based on the harmonic interval feature is constructed; wherein the first-stage threshold determination mechanism includes: potential UAV and non-UAV; According to the high signal-to-noise ratio and narrow band characteristics of the harmonic components of the UAV, threshold judgment is performed on the harmonic signal of the target under the conditions of high signal-to-noise ratio and narrow bandwidth, and based on the second determination rule, a second-stage threshold determination mechanism based on the harmonic isolation feature is constructed; wherein the second-stage threshold determination mechanism includes: potential UAV and non-UAV; According to the radar scattering interface of the UAV body, power ratio calculation is performed between the target main Doppler component and the strongest harmonic component of the target to obtain the minimum main harmonic ratio; Based on the threshold judgment condition of the minimum main harmonic ratio and the third determination rule, a three-stage threshold determination mechanism based on the main harmonic ratio feature is obtained; wherein the three-stage threshold determination mechanism includes: potential UAV and non-UAV; The first-stage threshold determination mechanism, the second-stage threshold determination mechanism, and the three-stage threshold determination mechanism are sequentially subjected to threshold series processing to obtain the series of three-stage threshold determination mechanisms.

6. The method of claim 1, wherein the method further comprises: Based on the threshold determination result of the series of three-stage threshold determination mechanisms, the target recognition result under the target track is obtained, specifically including: Through the series of three-stage threshold determination mechanisms, the target multi-dimensional micro-motion characteristics of the target are sequentially subjected to threshold judgment; If the potential UAV is in each threshold determination mechanism, the threshold determination result is determined as the UAV recognition result; If there is a non-UAV in each threshold determination mechanism, the threshold determination result is determined as the non-UAV discrimination result; Based on the UAV recognition result and the non-UAV discrimination result, the target recognition result under the target track is obtained.

7. The method of claim 1, wherein the method further comprises: Through the preset dynamic sliding window, the single target recognition result in the track level is subjected to time sequence fusion and target recognition optimization processing to obtain the optimized target recognition result in the same target track, specifically including: Through the dynamic sliding window, each target recognition result in the same track level is subjected to sliding strategy control; The historical sliding window optimization result in the dynamic sliding window is taken as the window reference value; The single target recognition result in the same target track is recursively identified and updated through the reference value in the window, and the consistency constraint is performed on the current track target recognition result based on the historical sliding window optimization result, so that the abnormal fluctuation correction processing of the single target recognition result is completed, and the optimization target recognition result of each track point in the same target track is obtained.

8. The method according to claim 7, wherein, according to The fusion judgment condition is obtained. ;in, For the aforementioned dynamic sliding window; For reference window Weighting factors; for The historical target identification result in the historical sliding window optimization results at the given time; t is the time corresponding to the track point in the same target track; According to , a fusion decision function is obtained; wherein the is the historical target recognition result, and ; According to , the optimization target recognition result is obtained; wherein, is the original target recognition result in the track level.

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