Ku-band unmanned aerial vehicle countering radar and target detection method and device

Abstract

The invention provides a ku-frequency-band unmanned aerial vehicle countering radar and a target detection method and device, and belongs to the technical field of radars, and the ku-frequency-band unmanned aerial vehicle countering radar comprises a transmitting system which is used for outputting electromagnetic waves of a ku frequency band; the antenna is used for performing directional radiation on the electromagnetic waves of the ku frequency band and receiving echo signals reflected by a target object; the antenna comprises at least one array plane module, and the array plane module is detachably arranged. The receiving system is used for sending the echo signal received by the antenna to the signal processing system; the timing synchronization system is used for providing synchronous clock signals for the transmitting system and the receiving system; and the signal processing system is used for detecting the unmanned aerial vehicle based on the echo signals, determining motion state parameters of the unmanned aerial vehicle and outputting the motion state parameters of the unmanned aerial vehicle to the countering execution system. According to the ku-band unmanned aerial vehicle countering radar and the target detection method and device provided by the invention, the universality and flexibility of the unmanned aerial vehicle countering radar can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar, and more particularly relates to a ku-band unmanned aerial vehicle countermeasure radar and a target detection method and device. BACKGROUND

[0002] The unmanned aerial vehicle countermeasure radar is a core perception device in an unmanned aerial vehicle defense system, and is used for early detection, accurate positioning, continuous tracking and target attribute identification of unmanned aerial vehicles in a complex environment, thereby providing key target information support for subsequent countermeasures (such as interference, interception and driving away).

[0003] Due to different unmanned aerial vehicle countermeasure requirements in different occasions, higher requirements are put forward for the universality and flexibility of the unmanned aerial vehicle countermeasure radar. SUMMARY

[0004] The application aims to provide a ku-band unmanned aerial vehicle countermeasure radar and a target detection method and device, so as to improve the universality and flexibility of the unmanned aerial vehicle countermeasure radar.

[0005] The first aspect of the embodiment of the application provides a ku-band unmanned aerial vehicle countermeasure radar, which comprises: A transmitting system used for outputting electromagnetic waves in a ku band; An antenna used for directional radiation of the electromagnetic waves in the ku band and reception of echo signals reflected back by a target object; the antenna comprises at least one array module, and the array module is arranged to be detachable; A receiving system used for sending the echo signals received by the antenna to a signal processing system; A timing synchronization system used for providing a synchronization clock signal to the transmitting system and the receiving system; The signal processing system is used for unmanned aerial vehicle detection based on the echo signals, determination of motion state parameters of the unmanned aerial vehicle, and output of the motion state parameters of the unmanned aerial vehicle to a countermeasure execution system; the motion state parameters of the unmanned aerial vehicle comprise distance, direction and speed of the unmanned aerial vehicle.

[0006] The second aspect of the embodiment of the application provides a target detection method, which comprises: Obtaining multiple frames of time-continuous echo signals reflected back by a target object at a current moment and before the current moment; For each frame of echo signal, obtaining point cloud information based on the frame of echo signal, performing spatial clustering on the point cloud information corresponding to each frame of echo signal, and obtaining multiple candidate target regions corresponding to each frame of echo signal; The inter-frame matching module is configured to calculate similarity between the region features of each candidate target region corresponding to each frame of echo signals and the region features of each candidate target region corresponding to the previous adjacent frame of echo signals, perform inter-frame matching on the multiple candidate target regions corresponding to the multiple frames of time-continuous echo signals respectively based on the similarity calculation results, and obtain multiple groups of candidate target regions. The target recognition module is configured to determine target object category information corresponding to each group of candidate target regions based on the echo signals corresponding to each candidate target region included in the group of candidate target regions, and determine a motion state parameter of a UAV at the current moment based on the echo signals corresponding to each candidate target region included in the group of candidate target regions if the target object category information includes category information of the UAV.

[0007] In a third aspect, the embodiment of the present application provides a target detection device, which comprises: The signal acquisition module is configured to acquire multiple frames of time-continuous echo signals reflected by a target object at a current moment and before the current moment. The region screening module is configured to, for each frame of echo signals, obtain point cloud information based on the frame of echo signals, perform spatial clustering on the point cloud information corresponding to each frame of echo signals, and obtain multiple candidate target regions corresponding to each frame of echo signals. The inter-frame matching module is configured to calculate similarity between the region features of each candidate target region corresponding to each frame of echo signals and the region features of each candidate target region corresponding to the previous adjacent frame of echo signals, perform inter-frame matching on the multiple candidate target regions corresponding to the multiple frames of time-continuous echo signals respectively based on the similarity calculation results, and obtain multiple groups of candidate target regions. The target recognition module is configured to determine target object category information corresponding to each group of candidate target regions based on the echo signals corresponding to each candidate target region included in the group of candidate target regions, and determine a motion state parameter of a UAV at the current moment based on the echo signals corresponding to each candidate target region included in the group of candidate target regions if the target object category information includes category information of the UAV.

[0008] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the target detection method.

[0009] The ku-band UAV countermeasure radar and the target detection method and device provided by the embodiment of the present application have the following beneficial effects: The ku band unmanned aerial vehicle countermeasure radar provided by the embodiment of the present application can realize high directivity of a small size antenna array, thereby realizing fine detection of small unmanned aerial vehicles; meanwhile, the antenna comprises at least one detachable array module, the antenna aperture and the radiation power can be dynamically changed by changing the number of array modules, thereby adapting to the countermeasure requirements of different occasions and improving the versatility and flexibility of the embodiment of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0011] Figure 1 The principle block diagram of the ku band unmanned aerial vehicle countermeasure radar provided by an embodiment of the present application is provided. Figure 2 The flowchart of the target detection method provided by an embodiment of the present application is provided. Figure 3 The structural block diagram of the target detection device provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present application.

[0013] In order to make the purpose, technical solutions and advantages of the present application more clear, the following will be described by specific embodiments in combination with the drawings.

[0014] Reference Figure 1 , Figure 1 The ku band unmanned aerial vehicle countermeasure radar provided by an embodiment of the present application comprises: A transmitting system for outputting electromagnetic waves of the ku band; An antenna for directional radiation of electromagnetic waves of the ku band and receiving echo signals reflected back by a target object; the antenna comprises at least one array module, and the array module is detachably arranged; A receiving system for sending the echo signals received by the antenna to a signal processing system; A timing synchronization system for providing a synchronous clock signal to the transmitting system and the receiving system; The signal processing system is used for detecting the UAV based on the echo signal, determining the motion state parameters of the UAV, and outputting the motion state parameters of the UAV to the countermeasure execution system; the motion state parameters of the UAV include the distance, direction and speed of the UAV.

[0015] In the embodiment, the transmitting system is used for generating electromagnetic waves in the ku frequency band (12-18 GHz), the wavelength of the ku frequency band is short, high directivity of the small-size antenna array can be realized, and the small UAV can be finely detected; the electromagnetic waves emitted by the transmitting system are reflected by the target, the echo signals reflected back are received by the antenna, amplified, filtered and detected by the receiving system, and sent to the signal processing system; the signal processing system extracts key features based on the echo signals, detects the UAV according to the key features, and calculates the motion state parameters of the UAV, and outputs the motion state parameters of the UAV to the countermeasure execution system for the countermeasure of the UAV.

[0016] The motion state parameters of the UAV include the distance, direction and speed of the UAV. Specifically, the distance of the UAV can be determined based on the time difference between the echo signal and the transmitting signal, and the electromagnetic wave propagation speed; the direction of the UAV can be determined by the pointing angle of the antenna beam; and the speed of the UAV can be calculated by the Doppler shift of the echo signal.

[0017] In the embodiment, the array module is a standard array module, the antenna includes at least one detachable array module, for the near distance, small range countermeasure scene (such as factory, small-scale activity site), only 1-2 array modules can be used to reduce the volume and weight of the radar, and facilitate rapid deployment; for the long distance, large range monitoring scene (such as airport, border line), 3-4 array modules can be stacked to expand the antenna aperture, improve the electromagnetic wave radiation power and echo receiving sensitivity, and thus prolong the detection distance.

[0018] Specifically, the detachable plug-in structure can be designed to realize the installation of the array module, and the guide rails are arranged on both sides of the array module, and the positioning and guidance of the multiple array modules when stacked are realized through the guide rails.

[0019] From the above, it can be concluded that the ku frequency band UAV countermeasure radar of the embodiment can realize high directivity of the small-size antenna array, so as to realize fine detection of the small UAV; at the same time, the antenna includes at least one detachable array module, the antenna aperture and radiation power can be dynamically changed by changing the number of array modules, so as to adapt to the countermeasure requirements of different occasions, and improve the versatility and flexibility of the embodiment.

[0020] Please refer to Figure 2 , Figure 2A flowchart of a target detection method provided by an embodiment of the present application can be executed by a signal processing system in a ku-band unmanned aerial vehicle countermeasure radar, and the method can include the following steps. S101: Obtain a current time and a plurality of time-continuous echo signals reflected back by a target object before the current time.

[0021] In this embodiment, considering that a single-frame echo signal is easily affected by clutter and electromagnetic interference, resulting in strong accidentalness of the detection result, the target detection is performed based on the current time and the plurality of continuous signals before the current time in this embodiment, the difference between the regularity of the target object movement and the randomness of the clutter can be utilized to filter transient interference, thereby improving the reliability of target detection. S102: For each frame of echo signal, obtain point cloud information based on the frame of echo signal, perform spatial clustering on the point cloud information corresponding to each frame of echo signal, and obtain a plurality of candidate target regions corresponding to each frame of echo signal.

[0022] In this embodiment, for each frame of echo signal, the echo signal can be first filtered and denoised to extract spatial coordinate information such as distance, azimuth, and pitch, and obtain a three-dimensional spatial point set corresponding to the frame of echo signal, i.e., point cloud information.

[0023] On this basis, existing DBSCAN, K-Means, and other spatial clustering algorithms can be used to cluster analyze the point cloud information corresponding to each frame of echo signal, aggregate points with similar features and adjacent spatial positions into a region, exclude background clutter points, and preliminarily screen out regions where target objects may exist, i.e., a plurality of candidate target regions corresponding to each frame of echo signal.

[0024] S103: Calculate the similarity between the region features of each candidate target region corresponding to each frame of echo signal and the region features of each candidate target region corresponding to the previous adjacent frame of echo signal, perform inter-frame matching on the plurality of candidate target regions corresponding to the plurality of time-continuous echo signals based on the similarity calculation result, and obtain a plurality of groups of candidate target regions.

[0025] In this embodiment, the size, shape, center point coordinate, and point cloud density of each candidate target region can be extracted as the region features of the candidate target region, and the region features can represent the physical properties of the corresponding candidate target region.

[0026] On this basis, comparing the region features of each candidate target region corresponding to each frame of echo signal with the region features of each candidate target region corresponding to the previous adjacent frame of echo signal can obtain inter-frame association information.

[0027] Assuming that the multiple frames of time-continuous echo signals include the t-th frame echo signal at the current time t, and the t-1-th frame echo signal, the t-2-th frame echo signal, the t-3-th frame echo signal and the t-4-th frame echo signal before the current time, the similarity between the region features of each candidate target region in the t-th frame echo signal and the region features of each candidate target region in the t-1-th frame echo signal can be calculated first. The higher the similarity between two candidate target regions, the more likely the two candidate target regions belong to the same target object. Any two candidate target regions with a similarity greater than a set threshold are taken as a pair of associated regions corresponding to the same target object. Then the similarity between the region features of each candidate target region in the t-1-th frame echo signal and the region features of each candidate target region in the t-2-th frame echo signal is calculated. Any two candidate target regions with a similarity greater than a set threshold are taken as a pair of associated regions corresponding to the same target object. Similarly, the similarity between the region features of each candidate target region in the t-3-th frame echo signal and the region features of each candidate target region in the t-4-th frame echo signal is calculated. Any two candidate target regions with a similarity greater than a set threshold are taken as a pair of associated regions corresponding to the same target object.

[0028] After obtaining the pair of associated regions corresponding to the same target object, the multiple pairs of associated regions belonging to the same target object are merged to obtain a group of candidate target regions corresponding to the same target object. Using the same method, a group of candidate target regions corresponding to each of the multiple target objects can be obtained, and multiple groups of candidate target regions are obtained.

[0029] S104: For each group of candidate target regions, target object category information corresponding to the group of candidate target regions is determined based on the echo signal corresponding to each candidate target region included in the group of candidate target regions; if the target object category information includes the category information of the unmanned aerial vehicle, the motion state parameter of the unmanned aerial vehicle at the current time is determined based on the echo signal corresponding to each candidate target region included in the group of candidate target regions.

[0030] In this embodiment, for each group of candidate target regions, multiple frames of echo signals included in the group of candidate target regions can be considered comprehensively, that is, the target object category information corresponding to the group of candidate target regions is determined based on the echo signal corresponding to each candidate target region included in the group of candidate target regions. The combination of multiple frames of echo signals can supplement the feature details at different time sequences (such as the periodic signal of the rotation of the propeller of the unmanned aerial vehicle), reduce the misjudgment of a single frame, and make the category determination more reliable.

[0031] If it is judged that the target object corresponding to a group of candidate target regions is an unmanned aerial vehicle, the motion state parameter of the unmanned aerial vehicle at the current time can be further calculated based on the multiple frames of echo signals in the group.

[0032] It can be concluded from the above that, based on the characteristics of dynamic movement of the unmanned aerial vehicle, the target detection of the unmanned aerial vehicle is performed based on the current time and the multiple frames of time-continuous echo signals reflected back by the target object before the current time, and through inter-frame feature matching, the influence of instantaneous clutter on the detection result can be effectively excluded. Meanwhile, the present embodiment preliminarily screens the candidate target regions based on spatial clustering, and then identifies the target object category information for each candidate target region, which can reduce the calculation amount of the target object category information identification, thereby improving the efficiency of target detection.

[0033] In an embodiment of the present application, the region features of each candidate target region include the center coordinates of the candidate region and the point cloud features of the candidate target region, and the point cloud features of the candidate target region include the number of point clouds and the average reflection intensity of the candidate target region. The similarity between the region features of each candidate target region corresponding to each frame of echo signal and the region features of each candidate target region corresponding to the previous adjacent frame of echo signal is calculated, and based on the similarity calculation result, inter-frame matching is performed on the multiple candidate target regions corresponding to the multiple frames of time-continuous echo signals respectively, to obtain multiple groups of candidate target regions, including: The distance between the center coordinates of each candidate target region corresponding to each frame of echo signal and the center coordinates of each candidate target region corresponding to the previous adjacent frame of echo signal is calculated. The similarity between the point cloud features of each candidate target region corresponding to each frame of echo signal and the point cloud features of each candidate target region corresponding to the previous adjacent frame of echo signal is calculated. The candidate target regions corresponding to a distance less than a distance threshold and a similarity of point cloud features greater than a similarity threshold are determined as the same group of candidate target regions.

[0034] In the present embodiment, when calculating the similarity between the region features of each candidate target region corresponding to each frame of echo signal and the region features of each candidate target region corresponding to the previous adjacent frame of echo signal, the distance between the center coordinates of each candidate target region corresponding to each frame of echo signal and the center coordinates of each candidate target region corresponding to the previous adjacent frame of echo signal can be calculated first. Assuming that the maximum speed of the unmanned aerial vehicle is 20 m / s and the frame interval is 0.1 s, the maximum distance Dmax between the above two center coordinates is 2 m, and therefore the distance threshold can be set to 2 m, and the two candidate target regions corresponding to the distance between the two center coordinates less than the distance threshold are selected as the potential associated region pair. Through this step, the candidate target regions conforming to the movement law of the unmanned aerial vehicle can be screened out, and the interference of irregular clutter or flying birds with discontinuous trajectories is excluded.

[0035] Further, the similarity between the point cloud features of each candidate target region corresponding to each frame of echo signals and the point cloud features of each candidate target region corresponding to the previous adjacent frame of echo signals can be calculated based on cosine similarity or Euclidean distance. If both candidate target regions simultaneously satisfy the distance being less than the distance threshold and the similarity between the point cloud features being greater than the similarity threshold, the two candidate target regions are taken as the associated region pair.

[0036] From the above, it can be concluded that the embodiment filters the associated candidate target regions based on the distance between the center coordinates, which can ensure the continuity of the selected candidate target regions in position, and based on the similarity between the point cloud features, which can ensure the consistency of the selected candidate target regions in attribute, thereby ensuring the accuracy of the candidate target region filtering.

[0037] In an embodiment of the present application, for each group of candidate target regions, the target object category information corresponding to the group of candidate target regions is determined based on the echo signal corresponding to each candidate target region included in the group of candidate target regions, including: extracting the micro-Doppler feature corresponding to each candidate target region included in the group of candidate target regions; inputting the micro-Doppler feature corresponding to each candidate target region into a classification model to obtain the target object category probability of each candidate target region; weighting and summing the target object category probability of each candidate target region included in the group of candidate target regions to obtain the target object category probability corresponding to the group of candidate target regions; determining the target object category information corresponding to the group of candidate target regions based on the target object category probability corresponding to the group of candidate target regions.

[0038] In the embodiment, the movement of target objects such as the rotation of the propeller of the unmanned aerial vehicle and the flapping of the wings of the bird will cause periodic frequency shift of the echo signal. The mode of change of this frequency shift over time is the micro-Doppler feature. The micro-Doppler features of different target objects are different. For example, the micro-Doppler feature of the unmanned aerial vehicle is a periodic strong peak, and that of the bird is irregular fluctuation. Therefore, by extracting the micro-Doppler feature corresponding to each candidate target region and inputting the micro-Doppler feature corresponding to each candidate target region into a classification model, the target object category probability of each candidate target region can be obtained. The existing short-time Fourier transform (STFT) or wavelet transform method can be used to extract the micro-Doppler feature corresponding to each candidate target region, and the existing convolutional neural network, support vector machine model or decision tree model can be used to implement the classification model.

[0039] On the basis of obtaining the target object category probability of each candidate target region, the target object category probability of each candidate target region contained in each group of candidate target regions is weighted and summed to obtain the target object category probability corresponding to the group of candidate target regions. Further, based on the target object category probability corresponding to each group of candidate target regions, for example, the maximum target object category probability can be selected, and if the maximum target object category probability is greater than a preset probability threshold (for example, 0.8), the target object category corresponding to the maximum target object category probability is taken as the target object category information.

[0040] Wherein, when the target object category probability of each candidate target region contained in each group of candidate target regions is weighted and summed, the determination manner of the weight corresponding to each candidate target region comprises: The standard deviation of the plurality of average reflection intensities in the point cloud corresponding to each candidate target region is calculated as the stability of each candidate target region. The weight corresponding to each candidate target region is determined based on the stability of each candidate target region; wherein the stability of each candidate target region and the weight corresponding to each candidate target region are in a negative correlation relationship.

[0041] In the embodiment, the smaller the standard deviation of the plurality of average reflection intensities in the point cloud corresponding to a certain candidate target region, the fewer the interference signals of the candidate target region, and the more accurate the target object category information obtained based on the echo signal corresponding to the candidate target region, so the weight corresponding to the candidate target region is set to a larger weight.

[0042] From the above, it can be concluded that the embodiment weights and sums the target object category probability of each candidate target region contained in each group of candidate target regions to obtain the target object category probability corresponding to the group of candidate target regions, which can avoid target object category misjudgment caused by single-frame echo signal distortion, thereby improving the recognition accuracy of target objects.

[0043] In an embodiment of the present application, the classification model specifically adopts a support vector machine model, and the kernel function of the support vector machine model is obtained by weighted summing a nonlinear kernel function and a linear kernel function.

[0044] In the embodiment, a support vector machine model can be used for target object classification. The support vector machine model finds the optimal hyperplane in the feature space to realize classification of different target categories. The kernel function can map the original low-dimensional linearly inseparable features to a high-dimensional feature space, so that the data has the possibility of linear separability in the high-dimensional space.

[0045] Considering that there are a large number of nonlinear correlations in the micro-Doppler features, the embodiment is based on a nonlinear kernel function (for example, an RBF kernel function) to fit the nonlinear correlations in the micro-Doppler features, so as to improve the distinguishing ability of the model for complex targets. Meanwhile, considering that the high complexity of the nonlinear kernel function can cause the model to excessively fit the details of the training data, and even learn noise features, therefore, on the basis of the nonlinear kernel function, a linear kernel function (for example, a standard linear kernel function) is introduced. The low complexity of the linear kernel function can "flatten" the excessively complex decision boundary of part of the nonlinear kernel function, reduce the dependence of the model on the noise of the training data, and thus improve the generalization ability.

[0046] As can be seen from the above, the embodiment sums the nonlinear kernel function and the linear kernel function by weighting, constructs the kernel function of the support vector machine model, can combine the advantages of the two types of kernel functions, and balance the fitting ability and generalization performance of the model for complex data.

[0047] In an embodiment of the present application, the target detection method further comprises: If the signal-to-noise ratio of the echo signal is greater than the first threshold, the weight of the nonlinear kernel is increased by a first step, and the weight of the linear kernel is decreased by the first step; If the signal-to-noise ratio of the echo signal is less than or equal to the first threshold, the weight of the nonlinear kernel is decreased by a first step, and the weight of the linear kernel is increased by the first step.

[0048] In the embodiment, when the signal-to-noise ratio of the echo signal is greater than the first threshold (for example, 8 dB), it indicates that the signal-to-noise ratio is high, the effective features (such as the micro-Doppler details of the propeller of the unmanned aerial vehicle) in the echo signal are clear, the noise interference is small, and the nonlinear correlation information is complete. At this time, the weight of the nonlinear kernel can be increased to fully utilize its ability to fit complex features, accurately distinguish subtle differences (such as the motion patterns of the unmanned aerial vehicle and similar size birds), and improve the classification accuracy.

[0049] When the signal-to-noise ratio of the echo signal is less than or equal to the first threshold, it indicates that the signal-to-noise ratio is high, and the nonlinear details in the echo signal are easy to be submerged by noise. Excessive dependence on the nonlinear kernel can fit the noise, causing overfitting. Therefore, the weight of the linear kernel can be increased to utilize its advantages of low complexity and strong anti-noise interference ability, focus on stable linear information in the signal, avoid noise interference, and ensure the classification stability. The first step is a preset constant, and a person skilled in the art can flexibly design the specific value of the first step according to actual needs. For example, the first step can be 0.1, 0.2, etc.

[0050] In an embodiment of the present application, considering that the wavelength of the ku-band radar is short (1.7-2.5 cm), close to the size of raindrops and snowflakes, strong reflection clutter is easy to be generated in rainy and snowy weather, thereby causing a large number of abnormal samples in the echo signal. The SVM is sensitive to abnormal samples and is easy to misclassify. In order to reduce the misclassification caused by abnormal samples, in the training stage of the support vector machine model, the objective function of the support vector machine model can be improved as follows: ; wherein, represents the normal vector of the classification hyperplane, b represents the bias of the hyperplane, represents the slack variable, C represents the penalty weight, represents the sample confidence, when the signal-to-noise ratio of the training sample is less than or equal to the first threshold, , normal penalty ; when the signal-to-noise ratio of the training sample is greater than the first threshold, , reduce the penalty weight, reduce the interference of abnormal samples on the model.

[0051] In an embodiment of the present application, for each group of candidate target regions, the motion state parameters of the unmanned aerial vehicle at the current moment are determined based on the echo signal corresponding to each candidate target region included in the group of candidate target regions, including: determining the single-frame estimation value of the motion state parameter of the unmanned aerial vehicle based on the echo signal corresponding to each candidate target region included in the group of candidate target regions; fusing the multiple single-frame estimation values corresponding to each motion state parameter of the unmanned aerial vehicle to obtain the motion state parameter of the unmanned aerial vehicle at the current moment.

[0052] In the present embodiment, for each group of candidate target regions, the single-frame estimation value of the motion state parameter (including distance, azimuth and speed) of the unmanned aerial vehicle can be first determined based on the echo signal corresponding to each candidate target region included in the group of candidate target regions by using the method of the above embodiment.

[0053] Then, the multiple single-frame estimation values corresponding to the distance of the unmanned aerial vehicle are fused to obtain the distance of the unmanned aerial vehicle at the current moment; the multiple single-frame estimation values corresponding to the azimuth of the unmanned aerial vehicle are fused to obtain the azimuth of the unmanned aerial vehicle at the current moment; and the multiple single-frame estimation values corresponding to the speed of the unmanned aerial vehicle are fused to obtain the speed of the unmanned aerial vehicle at the current moment.

[0054] Through the fusion of multiple single-frame estimation values, the random error of each single-frame estimation value can be offset, and the fused result is closer to the true value.

[0055] In an embodiment of the present application, the plurality of single-frame estimation values corresponding to each motion state parameter of the UAV are fused to obtain the motion state parameter of the UAV at the current time, including: The plurality of single-frame estimation values corresponding to the distance of the UAV are weighted and summed to obtain the distance of the UAV at the current time; The plurality of single-frame estimation values corresponding to the orientation of the UAV are Kalman filtered to obtain the orientation of the UAV at the current time; The plurality of single-frame estimation values corresponding to the velocity of the UAV are Kalman filtered to obtain the velocity of the UAV at the current time.

[0056] In the embodiment, considering that the distance parameter has strong static stability and the error is mainly random noise, the plurality of single-frame estimation values corresponding to the distance of the UAV are weighted and summed, so that the single-frame estimation value corresponding to the echo signal with high signal-to-noise ratio can be effectively utilized, noise interference can be suppressed, and the absolute accuracy of distance estimation can be improved.

[0057] Meanwhile, considering that the orientation and velocity are both dynamic and continuous parameters, the error contains system deviation and dynamic noise, and the Kalman filter is used to perform a "prediction - update" closed loop, so that the observation value of the current frame can be corrected by using the echo signal of the previous frame, the time continuity and smoothness of the parameter can be ensured, and the demand of the countermeasure system for trajectory stability estimation can be met.

[0058] corresponding to the above embodiment, Figure 3 A structural block diagram of a target detection device provided in an embodiment of the present application is shown. For ease of illustration, only parts related to the embodiments of the present application are shown. For parts not related to the embodiments of the present application, reference can be made to the description of the related art. Figure 3 The target detection device 20 includes a signal acquisition module 21, a region screening module 22, an inter-frame matching module 23, and a target recognition module 24. The signal acquisition module 21 is configured to acquire a plurality of time-continuous echo signals reflected by a target object at a current time and before the current time. The region screening module 22 is configured to, for each frame of echo signal, based on point cloud information obtained from the frame of echo signal, perform spatial clustering on the point cloud information corresponding to each frame of echo signal to obtain a plurality of candidate target regions corresponding to each frame of echo signal. The inter-frame matching module 23 is configured to calculate the similarity between the region features of each candidate target region corresponding to each frame of echo signal and the region features of each candidate target region corresponding to the previous adjacent frame of echo signal, and perform inter-frame matching on the plurality of candidate target regions corresponding to the plurality of time-continuous echo signals based on the similarity calculation results to obtain a plurality of groups of candidate target regions. The target recognition module 24 is configured to determine target class information corresponding to each group of candidate target regions based on echo signals corresponding to each candidate target region included in the group of candidate target regions; and determine a motion state parameter of the unmanned aerial vehicle at the current moment based on the echo signals corresponding to each candidate target region included in the group of candidate target regions, if the target class information includes class information of the unmanned aerial vehicle.

[0059] In an embodiment of the present application, the region feature of each candidate target region includes a center coordinate of the candidate region and point cloud features of the candidate target region, and the point cloud features of the candidate target region include a point cloud quantity and an average reflection intensity of the candidate target region; and the inter-frame matching module 23 is specifically configured to: calculate a distance between the center coordinate of each candidate target region corresponding to each frame of echo signals and the center coordinate of each candidate target region corresponding to a previous adjacent frame of echo signals; calculate a similarity between the point cloud features of each candidate target region corresponding to each frame of echo signals and the point cloud features of each candidate target region corresponding to a previous adjacent frame of echo signals; determine a candidate target region as a same group of candidate target regions if the corresponding distance is less than a distance threshold and the similarity of the corresponding point cloud features is greater than a similarity threshold.

[0060] In an embodiment of the present application, the target recognition module 24 is specifically configured to: perform feature extraction on the echo signals corresponding to each candidate target region included in the group of candidate target regions to obtain micro-Doppler features corresponding to each candidate target region; input the micro-Doppler features corresponding to each candidate target region into a classification model to obtain a target class probability of each candidate target region; perform weighted summation on the target class probabilities of each candidate target region included in the group of candidate target regions to obtain a target class probability corresponding to the group of candidate target regions; determine target class information corresponding to the group of candidate target regions based on the target class probability corresponding to the group of candidate target regions.

[0061] In an embodiment of the present application, the classification model specifically adopts a support vector machine model, and a kernel function of the support vector machine model is obtained by weighted summation of a nonlinear kernel function and a linear kernel function.

[0062] In an embodiment of the present application, the target recognition module 24 is specifically further configured to: if the signal-to-noise ratio of the echo signals is greater than a first threshold, increase a weight of the nonlinear kernel by a first step and decrease a weight of the linear kernel by the first step; If the signal-to-noise ratio of the echo signal is less than or equal to the first threshold value, the weight of the nonlinear kernel is reduced by a first step size, and the weight of the linear kernel is increased by the first step size.

[0063] In an embodiment of the present application, the target identification module 24 is specifically configured to: determine a single-frame estimated value of a motion state parameter of the UAV based on the echo signal corresponding to each candidate target region in the set of candidate target regions; fuse a plurality of single-frame estimated values corresponding to each motion state parameter of the UAV to obtain the motion state parameter of the UAV at the current time.

[0064] In an embodiment of the present application, the target identification module 24 is specifically further configured to: perform weighted summation on a plurality of single-frame estimated values corresponding to the distance of the UAV to obtain the distance of the UAV at the current time; perform Kalman filtering on a plurality of single-frame estimated values corresponding to the orientation of the UAV to obtain the orientation of the UAV at the current time; perform Kalman filtering on a plurality of single-frame estimated values corresponding to the speed of the UAV to obtain the speed of the UAV at the current time.

[0065] In another embodiment of the present application, a computer readable storage medium is provided, which stores a computer program. The computer program includes program instructions, which are executed by a processor to implement all or part of the processes of the above-mentioned embodiments. The computer program can also instruct related hardware to complete the processes by the computer program. The computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0066] The computer readable storage medium can be an internal storage unit of the electronic device of any of the preceding embodiments, such as a hard disk or a memory of the electronic device. The computer readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, or the like. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the electronic device. The computer readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0067] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0068] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic device and the units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0069] In several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, the modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces or units, and can also be electrical, mechanical or other forms of connection.

[0070] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0071] In addition, each function module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0072] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A ku-band drone counter-radar, characterized in that, include: A transmitting system used to output electromagnetic waves in the Ku band; An antenna is used to directionally radiate electromagnetic waves in the Ku band and receive echo signals reflected back from a target object; the antenna includes at least one array module, which is detachable. A receiving system is used to transmit the echo signal received by the antenna to a signal processing system; A timing synchronization system is used to provide a synchronization clock signal to the transmitting system and the receiving system; The signal processing system is used to detect the UAV based on the echo signal, determine the motion state parameters of the UAV, and output the motion state parameters of the UAV to the countermeasure execution system; the motion state parameters of the UAV include the distance, orientation, and speed of the UAV.

2. A target detection method applied to the ku-band unmanned aerial vehicle countermeasure radar of claim 1, characterized in that, include: Acquire multiple frames of time-continuous echo signals reflected back by the target object at the current moment and before the current moment; For each frame of echo signal, point cloud information is obtained based on that frame of echo signal. Spatial clustering is performed on the point cloud information corresponding to each frame of echo signal to obtain multiple candidate target regions corresponding to each frame of echo signal. Calculate the similarity between the regional features of each candidate target region corresponding to each frame of echo signal and the regional features of each candidate target region corresponding to the echo signal of the previous adjacent frame. Based on the similarity calculation results, perform inter-frame matching on multiple candidate target regions corresponding to each of the time-continuous echo signals of multiple frames to obtain multiple sets of candidate target regions. For each group of candidate target regions, the target object category information corresponding to the group of candidate target regions is determined based on the echo signal corresponding to each candidate target region contained in the group of candidate target regions. If the target category information includes UAV category information, the motion state parameters of the UAV at the current moment are determined based on the echo signal corresponding to each candidate target region contained in the group of candidate target regions.

3. The object detection method of claim 2, wherein, The regional features of each candidate target region include the center coordinates of the candidate region and the point cloud features of the candidate target region, wherein the point cloud features of the candidate target region include the number of points in the candidate target region and the average reflection intensity; The similarity between the regional features of each candidate target region corresponding to each frame of echo signal and the regional features of each candidate target region corresponding to the echo signal of the previous adjacent frame is calculated. Based on the similarity calculation results, inter-frame matching is performed on multiple candidate target regions corresponding to each of the multiple time-continuous echo signals to obtain multiple sets of candidate target regions, including: Calculate the distance between the center coordinates of each candidate target region corresponding to each frame of echo signal and the center coordinates of each candidate target region corresponding to the echo signal of the previous adjacent frame; Calculate the similarity between the point cloud features of each candidate target region corresponding to each frame of echo signal and the point cloud features of each candidate target region corresponding to the echo signal of the previous adjacent frame; Candidate target regions whose corresponding distance is less than the distance threshold and whose corresponding point cloud feature similarity is greater than the similarity threshold are identified as the same group of candidate target regions.

4. The object detection method of claim 2, wherein, For each group of candidate target regions, the target object category information corresponding to that group of candidate target regions is determined based on the echo signal corresponding to each candidate target region contained in that group, including: extracting a micro-Doppler feature corresponding to each candidate target region in the set of candidate target regions; inputting the micro-Doppler feature corresponding to each candidate target region into a classification model to obtain a target class probability of each candidate target region; performing weighted summation on the target class probability of each candidate target region in the set of candidate target regions to obtain a target class probability corresponding to the set of candidate target regions; determining target class information corresponding to the set of candidate target regions based on the target class probability corresponding to the set of candidate target regions.

5. The object detection method of claim 4, wherein, The classification model specifically adopts a support vector machine model, and a kernel function of the support vector machine model is obtained by performing weighted summation on a nonlinear kernel function and a linear kernel function.

6. The object detection method of claim 5, wherein, Further comprising: if the signal-to-noise ratio of the echo signal is greater than a first threshold, increasing the weight of the nonlinear kernel by a first step size and decreasing the weight of the linear kernel by the first step size; if the signal-to-noise ratio of the echo signal is less than or equal to the first threshold, decreasing the weight of the nonlinear kernel by the first step size and increasing the weight of the linear kernel by the first step size.

7. The object detection method of claim 2, wherein, For each set of candidate target regions, determining a motion state parameter of the unmanned aerial vehicle at the current time based on the echo signal corresponding to each candidate target region in the set of candidate target regions, comprising: determining a single-frame estimation value of the motion state parameter of the unmanned aerial vehicle based on the echo signal corresponding to each candidate target region in the set of candidate target regions; fusing a plurality of single-frame estimation values corresponding to each motion state parameter of the unmanned aerial vehicle to obtain the motion state parameter of the unmanned aerial vehicle at the current time.

8. The object detection method of claim 7, wherein, The fusing a plurality of single-frame estimation values corresponding to each motion state parameter of the unmanned aerial vehicle to obtain the motion state parameter of the unmanned aerial vehicle at the current time, comprising: performing weighted summation on a plurality of single-frame estimation values corresponding to the distance of the unmanned aerial vehicle to obtain the distance of the unmanned aerial vehicle at the current time; performing Kalman filtering on a plurality of single-frame estimation values corresponding to the orientation of the unmanned aerial vehicle to obtain the orientation of the unmanned aerial vehicle at the current time; performing Kalman filtering on a plurality of single-frame estimation values corresponding to the speed of the unmanned aerial vehicle to obtain the speed of the unmanned aerial vehicle at the current time.

9. A target detection apparatus characterized by comprising: comprising: a signal acquisition module configured to acquire a plurality of time-continuous echo signals reflected by a target object at a current time and before the current time; a region screening module configured to, for each echo signal, determine point cloud information based on the echo signal, perform spatial clustering on the point cloud information corresponding to each echo signal to obtain a plurality of candidate target regions corresponding to each echo signal; an inter-frame matching module configured to calculate a similarity between a region feature of each candidate target region corresponding to each echo signal and a region feature of each candidate target region corresponding to a previous adjacent echo signal, and perform inter-frame matching on the plurality of candidate target regions corresponding to the plurality of time-continuous echo signals based on a similarity calculation result to obtain a plurality of sets of candidate target regions; a target identification module configured to, for each set of candidate target regions, determine target class information corresponding to the set of candidate target regions based on an echo signal corresponding to each candidate target region in the set of candidate target regions; and a target identification module configured to, for each set of candidate target regions, determine target class information corresponding to the set of candidate target regions based on an echo signal corresponding to each candidate target region in the set of candidate target regions; and If the target category information contains category information of a UAV, a motion state parameter of the UAV at the current time is determined based on echo signals corresponding to each candidate target region contained in the group of candidate target regions.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, when executed by a processor, implements the steps of the method of any one of claims 2 to 8.

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