Distributed unmanned aerial vehicle radar plot fusion method based on fuzzy close degree

Abstract

The invention particularly relates to a distributed unmanned aerial vehicle radar plot fusion method based on fuzzy close degree. The method comprises the following steps: acquiring plot data of a plurality of radars; performing space-time registration on the trace point data of the plurality of radars; calculating the fuzzy close degree of each radar plot after space-time registration; the fusion weight of each radar is calculated based on the fuzzy close degree, and weighted fusion is carried out on the plots of the multiple radars at the same moment; and performing tracking filtering on the fused trace point to form a track. According to the method provided by the invention, the point trace quality of each radar is evaluated by using the fuzzy closeness function, and adaptive distribution and point trace fusion of weights are carried out on the basis, so that the point trace fusion quality and efficiency are improved, and the effects of distributed unmanned aerial vehicle radar point trace fusion and filtering tracking are improved.

Description

Technical Field

[0001] The present invention relates to the field of radar point trace fusion technology, and in particular to a distributed unmanned aerial vehicle-borne radar point trace fusion method based on fuzzy proximity. Background Art

[0002] In recent years, with the development of information technology such as communication networks and artificial intelligence, and advanced manufacturing technologies such as integrated circuits and materials science, UAV systems have gradually developed towards miniaturization, low cost, high flexibility and clustering. Through multi-radar networking and data fusion processing, multi-level and comprehensive processing of observation data can be achieved, which can not only make up for the inherent defects of low performance indicators of single radars, but also provide observers with a more comprehensive and accurate description of the environment. It can be said that the information fusion of distributed UAV airborne radar networks will become an indispensable technical force in future high-tech wars.

[0003] Most traditional point-trace fusion methods use methods based on statistical mathematics, which are mainly divided into two categories according to whether there is correlation between the measurements of different radar stations. When the target measurements are independent, weighted fusion methods can be used, including weighted least squares fusion, simple convex combination fusion and other methods. The weight selection of such methods depends largely on the designer's experience and subjective judgment, or the variance parameters of the radar itself are used, and fixed weights are usually used. Although the structure is simple, the computational complexity is low, and the efficiency is high, it lacks the ability to dynamically adjust the weights. At the same time, it is sensitive to noise and outliers and has poor robustness. When the target measurements are not independent, covariance fusion methods are usually used. Common methods include the bar-sharom-campo method, the covariance cross fusion (CI) method, and the generalized covariance cross fusion method. Although these methods take into account the correlation between local estimates, the calculation of the cross-covariance matrix requires a lot of information and has high computational complexity. In practice, it is often difficult to obtain the covariance matrix, which limits the engineering implementation and application of these methods. Therefore, it is necessary to propose a fusion method with low computational complexity and strong robustness to meet the needs of practical engineering applications.

[0004] Fuzzy theory can effectively deal with the uncertainty and noise caused by complex factors such as radar measurement errors, target distribution and motion patterns, navigation, sensor calibration and conversion, and delay errors, thereby improving the robustness of point-trace fusion. It also has strong flexibility and can adapt to different radar environments and target characteristics, improve the accuracy of fusion results, and simplify some complex mathematical operations and reduce computational complexity.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to solve the problems of high computational complexity, low fusion accuracy and poor robustness of existing point trace fusion methods, the present invention provides a distributed unmanned aerial vehicle radar point trace fusion method based on fuzzy proximity, which evaluates the point trace quality of each radar by using fuzzy proximity function, and performs adaptive weight allocation and point trace fusion accordingly, thereby improving the point trace fusion quality and efficiency, and further improving the effect of distributed unmanned aerial vehicle radar point trace fusion and filter tracking.

[0007] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0008] According to a first aspect of the present invention, a distributed UAV-borne radar point trace fusion method based on fuzzy proximity is provided, comprising:

[0009] Get the point trace data of multiple radars;

[0010] Perform time-space registration on the point data of multiple radars;

[0011] Calculate the fuzzy closeness of each radar point after time-space registration;

[0012] The fusion weight of each radar is calculated based on the fuzzy proximity, and the traces of multiple radars at the same time are weightedly fused;

[0013] The fused points are tracked and filtered to form a track.

[0014] In some exemplary embodiments, performing spatiotemporal registration on the point trace data of multiple radars includes:

[0015] Perform spatial registration on the point data of multiple radars and unify them into the same coordinate system;

[0016] Time alignment is performed on the point trace data of multiple radars to unify them to the same time reference.

[0017] In some exemplary embodiments, the spatial registration of the point trace data of multiple radars includes:

[0018] The point data is transformed from the NED coordinate system to the earth rectangular coordinate system, where the transformation relationship between the NED coordinate system and the earth rectangular coordinate system is:

[0019] X g =TX l +X 0

[0020] T=R x (-L)R z (B)R y (A)

[0021]

[0022] Where, X l is the coordinate in the NED coordinate system, X g is the coordinate in the earth's rectangular coordinate system, X 0 It is the position coordinate of the origin of the NED coordinate system in the earth's rectangular coordinate system. L, B, and A are the longitude, latitude, and geodetic azimuth of the airborne radar, respectively. θ is the rotation angle of the X-axis and Y-axis of the NED coordinate system relative to the X-axis and Y-axis of the earth's rectangular coordinate system around the Z-axis.

[0023] In some exemplary embodiments, the time registration of the point trace data of the plurality of radars includes time axis alignment and time calibration;

[0024] The time axis alignment adopts a method combining Beidou and system timing;

[0025] The time calibration uses the Lagrange three-point interpolation method to align the radar traces to the same time reference.

[0026] In some exemplary embodiments, the calculation of the fuzzy closeness of each radar point trace after spatiotemporal registration uses the following formula:

[0027]

[0028] In the formula, Z i With Z 0 denote the fuzzy sets of the i-th radar measurement value and estimated value, σ i and σ 0 Respectively represent Z i With Z 0 The standard deviation of .

[0029] In some exemplary embodiments, the fusion weight of each radar is calculated based on the fuzzy proximity, and the point traces of multiple radars at the same time are weightedly fused, specifically:

[0030] Normalize the n radar measurements to get their relative weights:

[0031]

[0032] The final fusion result is

[0033]

[0034] Where Z is the fusion point trace.

[0035] In some exemplary embodiments, performing tracking filtering on the fused point traces to form a track includes:

[0036] Using the measurements at time k and before to make a one-step prediction of the state and covariance matrix at time k+1, we get:

[0037]

[0038] P(k+1|k)=F(k)P(k|k)F'(k)+Q(k)

[0039] Where Z(k) is the state vector at time k, which contains the position and velocity of the target in the x direction and the position and velocity in the y direction. G(k)u(k) is the target state change caused by the correctable input or control signal. is the one-step prediction value of the target state, Q(k) is the noise covariance, P(k|k) is the target state error covariance matrix, and F(k) is the state transfer matrix;

[0040] The measurement Z formed by fusion of k+1 time traces k+1 Update the target state and covariance:

[0041]

[0042] P(k+1|k+1)=[IK(k+1)H(k+1)]P(k+1|k)

[0043] Where H(k+1) is the measurement matrix, which is related to the target motion model, and K(k+1) is the Kalman filter gain.

[0044] According to a second aspect of the present invention, there is provided a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the distributed unmanned aerial vehicle-borne radar point trace fusion method based on fuzzy proximity described in the first aspect is implemented.

[0045] According to a third aspect of the present invention, there is provided a computer program product having a computer program stored thereon, and when the computer program is executed by a processor, the distributed UAV-borne radar point trace fusion method based on fuzzy proximity described in the first aspect is implemented.

[0046] According to a fourth aspect of the present invention, there is provided an electronic device, comprising:

[0047] Processor; and

[0048] A memory, configured to store executable instructions of the processor;

[0049] Among them, the processor is configured to implement the distributed unmanned aerial vehicle radar point trace fusion method based on fuzzy proximity described in the first aspect above by executing the executable instructions.

[0050] The distributed UAV-mounted radar point track fusion method based on fuzzy proximity provided in the embodiment of the present invention calculates the proximity of two radar tracks, allocates fusion weights according to the comprehensive fuzzy proximity, fuses the radar tracks in space and time, and combines the Kalman filtering method for tracking filtering. On this basis, a point track fusion processing flow is designed, which effectively solves the problems of high computational complexity, low fusion accuracy and poor robustness of traditional methods.

[0051] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the specification are used to explain the principles of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0053] Figure 1 A block diagram of point trace fusion and filtering processing according to an exemplary embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of the process of a distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to an exemplary embodiment of the present invention;

[0055] Figure 3 A schematic diagram of a distributed UAV-mounted radar target tracking geometry according to an exemplary embodiment of the present invention;

[0056] Figure 4 It is a schematic diagram of the transformation relationship between the NED coordinate system and the earth rectangular coordinate system according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0057] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0058] In view of the shortcomings and deficiencies of the prior art, this example implementation provides a distributed UAV radar track fusion method based on fuzzy theory, which uses a grid proximity function to calculate the proximity of two radar tracks, assigns fusion weights based on the comprehensive fuzzy proximity, and fuses the radar tracks in space and time. While making full use of the original position information in the radar track, the advantages of fuzzy theory are fully utilized, the fusion weights are adaptively adjusted, the fusion accuracy of the track is improved, the algorithm complexity of the fusion process is reduced, and the robustness of the algorithm is improved, thereby improving the tracking accuracy.

[0059] refer to Figure 2 As shown, the distributed UAV radar point trace fusion method based on fuzzy theory can specifically include the following steps:

[0060] Step S11, establishing a distributed UAV-mounted radar target tracking model to obtain measurement data of multiple radars;

[0061] Step S12, spatially aligning the measurement data of multiple radars to unify them into the same coordinate system;

[0062] Step S13, time-aligning the measurement data of multiple radars to unify them to the same time reference;

[0063] Step S14, performing correlation processing on the measured points after the time-space registration, and calculating the fuzzy closeness of the related points, using this as a fusion weight to fuse the related points to obtain a fused point;

[0064] Step S15, using the fused point tracks to perform track association, filtering, and updating to obtain track information.

[0065] Below, each step of the distributed UAV radar point trace fusion method based on fuzzy theory in this example implementation will be described in more detail in combination with the accompanying drawings and embodiments.

[0066] In step S11, a distributed UAV-mounted radar target tracking model is established to obtain measurement data of multiple radars.

[0067] For example, Figure 3 The figure shows the geometric configuration of the radar network system consisting of two drone-mounted radars. The two drones are traveling at speeds v 1 and v 2 Flying in the same direction as the target on both sides of the target, the two airborne radars detect and track the target with right front vision and left front vision respectively, where (X g , Y g , Z g ) is the geocentric coordinate system, (X 1 , Y 1 , Z 1 ) and (X 2, Y 2 , Z 2 ) are the NED coordinate systems of the first radar and the second radar respectively. It is assumed that both radars are modern radars, that is, they can provide not only target state estimation but also estimated error covariance. Commonly used fusion architectures are distributed and centralized. The present invention considers the use of centralized fusion, that is, sending the target state and estimated error covariance of the radar to the fusion center for fusion.

[0068] Assume that the i-th radar makes m measurements of the true value X, and the measurement vector is Z i,m (k), then the measurement equation of radar i in the rectangular coordinate system at time k is

[0069] Z i,m (k) = H(k)X i,m (k)+W i,m (k) (1)

[0070] Where H(k) is the measurement matrix, W i,m (k) is a zero-mean white Gaussian measurement noise matrix. It is assumed that the process noise matrix is ​​independent of the measurement noise matrix and the initial state of the target. The measurement standard deviations of the position and velocity of the target in the x direction and the position and velocity in the y direction are respectively denoted as

[0071] In step S12, the measurement data of multiple radars are spatially aligned and unified into the same coordinate system.

[0072] For example, the preprocessing of the point traces first requires spatial registration, which is mainly to convert the coordinates of the measurement data of multiple radars. For radars installed on different types of platforms, the appropriate coordinate system will be selected according to the usage. For airborne radars, the coordinate system usually used for target measurement is the NED (North East Down) coordinate system, so the proposed method mainly considers the target fusion under the NED coordinate. The origin of the NED (North East Down) coordinate system is set at the center of mass of the carrier, N is the geographic north, E is the tangent direction of the earth's rotation, and D is the direction from the center of mass of the carrier to the center of the earth. For the aircraft platform, the NED coordinate system is an approximate inertial coordinate. The earth rectangular coordinate system is an inertial coordinate system, and the coordinates of the target in this coordinate system are usually expressed in longitude and latitude. In radar data processing, filtering, interpolation, extrapolation and other processes are easier to perform in the rectangular coordinate system. Therefore, in the data processing of airborne radar networking, the measurement information of the target is usually processed from the NED coordinate system to the earth rectangular coordinate system.

[0073] The transformation relationship between the NED coordinate system and the earth rectangular coordinate system is as follows Figure 4Assume that L, B, H, and A are the longitude, latitude, altitude, and azimuth of the airborne radar, respectively. The coordinate of the target in the NED coordinate system is X l =(x l ,y l ,z l ), whose coordinate in the earth's rectangular coordinate system is X g =(x g ,y g ,z g ). According to, the transformation relationship between the target coordinates in the NED coordinate system and the earth rectangular coordinate system is:

[0074] X g =TX l +X 0 (2)

[0075] In the formula

[0076] T=R x (-L)R z (B)R y (A) (3)

[0077] In the formula

[0078]

[0079] In the formula, X 0 is the position coordinate of the origin of the NED coordinate system in the earth's rectangular coordinate system, and θ is the angle of rotation of the X-axis and Y-axis of the NED coordinate system around the Z-axis relative to the X-axis and Y-axis of the earth's rectangular coordinate system.

[0080] In step S13, the measurement data of multiple radars are time-aligned and unified to the same time reference.

[0081] Exemplarily, time alignment mainly includes time axis alignment and time calibration. Time axis alignment is to align the working time of each radar to a unified time base. The main methods include atomic clock, system timing, Beidou, GPS, etc. At present, the Beidou timing accuracy can reach nanosecond level, which can meet the requirements of point-track fusion. Therefore, the method of combining Beidou with system timing is usually adopted. Time calibration is to align the points with similar detection time of each radar to the same time point. Interpolation / extrapolation method, polynomial interpolation method, least squares curve fitting method and other methods can be used. The Lagrange three-point interpolation method in the polynomial interpolation method is simpler and easier to implement than the other two methods, so the Lagrange three-point interpolation method is used to align the radar points to the same time base.

[0082] Assume that radar i is at t i,k-1 ,t i,k ,t i,k+1 The measured values ​​at each moment are Zi,k-1 , Z i,k , Z i,k+1 , we can get approximately i,m The measured value Z at the moment i,m for:

[0083]

[0084] In step S14, the measurement points after the time-space registration are correlated, and the fuzzy closeness of the related points is calculated, which is used as a fusion weight to fuse the related points to obtain a fused point.

[0085] For example, most traditional point trace fusion methods use methods based on statistical mathematics, which can be divided into two categories according to whether there is a correlation between the measurements of different radar stations. When the target measurements are independent, weighted fusion methods can be used, including weighted least squares fusion, simple convex combination fusion and other methods. They mainly assign a weight to each radar according to the performance indicators of each radar, and then perform weighted fusion on the point traces measured by each radar according to the assigned weight. The selection of weights in this type of method depends largely on the designer's experience and subjective judgment, or the variance parameters of the radar itself are used, and fixed weights are usually used. Although this type of method has a simple structure, low computational complexity and high efficiency, it lacks the ability to dynamically adjust weights. It is also sensitive to noise and outliers and has poor robustness. When the target measurements are not independent, covariance fusion methods are usually used. Common methods include the bar-shalom-campo method, the covariance cross fusion (CI) method, and the generalized covariance cross fusion (GCI) method. Although this type of method takes into account the correlation between local estimates, the calculation of the cross-covariance matrix requires a lot of information and has high computational complexity. In addition, the cross-covariance matrix is ​​often difficult to obtain in practice, which limits the engineering implementation and application of these methods.

[0086] The fuzzy proximity function has dealt with measurement uncertainty and ambiguity to some extent. The correlation between measurements does not need to be explicitly considered during the calculation process, which greatly reduces the complexity of the calculation. At the same time, the fusion weight is dynamically adjusted according to the size of the proximity function, which makes it more robust.

[0087] Assume that n radars are used in the actual measurement work, and the i-th radar measures the true value X m times. The measured value after space-time registration is Z i,1 ,Z i,2 ,...,Z i,m , let the measurement mean be Z i , with standard deviation σ i , the estimated value is Z 0 , with standard deviation σ 0 .in

[0088]

[0089] Let the normal fuzzy set Z i With Z 0 They represent the fuzzy sets of the i-th radar measurement value and estimated value respectively, and the corresponding membership functions are shown in Equation (12) and Equation (13) respectively.

[0090]

[0091] For a distributed radar network system composed of N radars, since the measurement accuracy, error, and interference of each radar are different, the degree of fuzziness of each radar measurement set is also different. The difference in fuzziness can be used to characterize the weight of a certain radar in the fusion system. The smaller the difference between the radar measurement value and the true value, the closer the measurement is to the target, and the greater the weight of the corresponding radar during fusion. According to the definition of fuzzy set, Z i With Z 0 The closeness is:

[0092]

[0093] Normalize the n radar measurements to get their relative weights:

[0094]

[0095] The final fusion result is

[0096]

[0097] In step S15, the fused point tracks are used to perform track association, filtering, and updating to obtain track information.

[0098] Exemplarily, the post-fusion point trace filtering uses Kalman filtering. First, the state and covariance matrix at time k+1 are predicted using the measurements at time k and before, and the result is:

[0099]

[0100] P(k+1|k)=F(k)P(k|k)F'(k)+Q(k) (18)

[0101] Where Z(k) is the state vector at time k, including the position and velocity of the target in the x direction and the position and velocity in the y direction, G(k)u(k) is the target state change caused by the correctable input or control signal, is the one-step prediction value of the target state, Q(k) is the noise covariance, P(k|k) is the target state error covariance matrix, and F(k) is the state transfer matrix.

[0102]

[0103] The measurement Z formed by fusion of k+1 time traces k+1 The target state and covariance are updated, and the update equations are shown in Equation (20) and Equation (21):

[0104]

[0105] P(k+1|k+1)=[IK(k+1)H(k+1)]P(k+1|k) (21)

[0106] Where H(k+1) is the measurement matrix, which is related to the target motion model, and K(k+1) is the Kalman filter gain:

[0107] K(k+1)=P(k+1|k)H'(k+1)[H(k+1)P(k+1|k)H'(k+1)+R(k+1)] -1 (twenty two)

[0108] Where R(k+1) is the measurement noise covariance.

[0109] Continue to use equations (17) to (21) to continuously predict the state and covariance matrix of the target at the next moment, and then obtain the target observation track.

[0110] An embodiment of the present invention provides a distributed unmanned aerial vehicle radar point trace fusion method based on fuzzy theory. First, point trace data preprocessing, that is, time-space alignment, is performed to unify the measurement point traces of two radars from each radar to the same geocentric coordinate system and the same time reference; the fuzzy closeness function of each radar point trace is calculated; the comprehensive fuzzy closeness is used as a measure of the fusion weight, and the point traces of the two radars at the same time are weightedly fused. Since in fuzzy theory, the closeness function has already processed the measurement uncertainty and ambiguity to a certain extent, the correlation between the measurements does not need to be explicitly considered; the fused point trace is tracked and filtered to form a track.

[0111] It should be noted that, as another aspect, the present application also provides a storage medium, which may be included in an electronic device; or may exist independently without being assembled into the electronic device. The above storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiments.

[0112] In one embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0113] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0114] Other embodiments of the invention will readily occur to those skilled in the art after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0115] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A distributed UAV-borne radar point trace fusion method based on fuzzy proximity, characterized in that: The method comprises: Get the point trace data of multiple radars; Perform time-space registration on the point data of multiple radars; Calculate the fuzzy closeness of each radar point after time-space registration; The fusion weight of each radar is calculated based on the fuzzy proximity, and the traces of multiple radars at the same time are weightedly fused; The fused points are tracked and filtered to form a track.

2. The distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to claim 1 is characterized in that: The time-space registration of the point trace data of multiple radars includes: Perform spatial registration on the point data of multiple radars and unify them into the same coordinate system; Time alignment is performed on the point trace data of multiple radars to unify them to the same time reference.

3. The distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to claim 2 is characterized in that: The spatial registration of the point trace data of multiple radars includes: The point data is transformed from the NED coordinate system to the earth rectangular coordinate system, where the transformation relationship between the NED coordinate system and the earth rectangular coordinate system is: X g =TX l +X0 T=R x (-L)R z (B)R y (A) Where, X l is the coordinate in the NED coordinate system, X g is the coordinate in the earth's rectangular coordinate system, X0 is the position coordinate of the origin of the NED coordinate system in the earth's rectangular coordinate system, L, B, A are the longitude, latitude, and geodetic azimuth of the airborne radar, respectively, and θ is the rotation angle of the X-axis and Y-axis of the NED coordinate system relative to the X-axis and Y-axis of the earth's rectangular coordinate system around the Z-axis.

4. The distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to claim 2 is characterized in that: The time alignment of the point trace data of the multiple radars includes time axis alignment and time calibration; The time axis alignment adopts a method combining Beidou and system timing; The time calibration uses the Lagrange three-point interpolation method to align the radar traces to the same time reference.

5. The distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to claim 1 is characterized in that: The fuzzy closeness of each radar point trace after the time-space registration is calculated using the following formula: In the formula, Z i and Z0 represent the fuzzy sets of the i-th radar measurement value and estimated value, respectively, σ i and σ0 represent Z i The standard deviation from Z0.

6. The distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to claim 5 is characterized in that: The fusion weight of each radar is calculated based on the fuzzy proximity, and the point traces of multiple radars at the same time are weightedly fused. Specifically, Normalize the n radar measurements to get their relative weights: The final fusion result is Where Z is the fusion point trace.

7. The distributed UAV-borne radar point trace fusion method based on fuzzy proximity according to claim 6 is characterized in that: The step of tracking and filtering the fused point traces to form a track includes: Using the measurements at time k and before to make a one-step prediction of the state and covariance matrix at time k+1, we get: P(k+1|k)=F(k)P(k|k)F'(k)+Q(k) Where Z(k) is the state vector at time k, which contains the position and velocity of the target in the x direction and the position and velocity in the y direction. G(k)u(k) is the target state change caused by the correctable input or control signal. is the one-step prediction value of the target state, Q(k) is the noise covariance, P(k|k) is the target state error covariance matrix, and F(k) is the state transfer matrix; The measurement Z formed by fusion of k+1 time traces k+1 Update the target state and covariance: P(k+1|k+1)=[IK(k+1)H(k+1)]P(k+1|k) Where H(k+1) is the measurement matrix, which is related to the target motion model, and K(k+1) is the Kalman filter gain.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed UAV-borne radar point-trace fusion method based on fuzzy proximity as described in any one of claims 1 to 7 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the distributed unmanned aerial vehicle radar point trace fusion method based on fuzzy proximity according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the distributed unmanned aerial vehicle radar point trace fusion method based on fuzzy proximity described in any one of claims 1 to 7 by executing the executable instructions.

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