A filter-based scanning radiation source tracking method

Abstract

The application discloses a scanning radiation source tracking method based on filtering, which comprises the following steps: firstly, an observation station acquires a received pulse signal of a scanning radiation source and records a sampling time, performs beam fitting on the pulse signal through a beam fitting model, and estimates an interception time of a pulse signal peak value; secondly, a scanning rate is estimated by using a time difference of pulse signal peak values intercepted by the observation station for two adjacent times, and a scanning angle difference is calculated based on scanning time differences of different observation stations; finally, an extended Kalman tracking positioning is performed by taking the scanning angle difference as an observation variable, the scanning angle difference is estimated and compensated by using a tracking result, so that joint estimation of a scanning radiation source position and a scanning rate is realized. The application fully utilizes characteristics of the scanning radiation source and influences of relative motion relations of the observation station and the radiation source on the scanning angle difference, and combines an EKF algorithm to correct the scanning rate, so that tracking precision of the moving radiation source is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of passive positioning and tracking, and in particular to a scanning radiation source tracking method based on filtering. Background Art

[0002] Mechanically scanned radar radiation typically has a fixed scanning rate, with characteristics such as a narrow beam and a high signal-to-noise ratio. For passive positioning of scanning radiation sources, the narrow beam characteristics of these sources make it impossible for multiple observation stations to receive the signal simultaneously, making it impossible to achieve positioning by measuring the mainlobe signal TDOA or FDOA. Positioning methods based on time-of-arrival (TOA) are generally only applicable to radiation sources with fixed PRI characteristics. For non-cooperative scanning radiation sources, FOA-based positioning methods are also difficult to apply to scanning radiation sources, as prior information such as the carrier frequency and modulation parameters of the transmitted signal cannot be accurately obtained.

[0003] Currently, the most in-depth research on scanning emitters is based on the DOA or main lobe signal intercept time (TOI) positioning method. H.Hmam et al. solved the problem by using the geometric constraints introduced by the antenna main lobe when scanning multiple independent observation stations. On this basis, they further studied the joint estimation problem of the scanning rate and the scanning emitter position, and proposed a joint estimator based on nonlinear least squares (NLS). K. A closed-form pseudo-LS solution for scenarios with N > 3 was proposed. The key lies in introducing a rotation angle, evaluating it using the receiving station's position and the scanning angle difference, and then using this rotation angle to calculate the AOA, achieving positioning. He You et al. proposed a maximum likelihood positioning method for the case where the scanning rate of a moving scanning source is unknown. This method projects each time difference measurement into the target state space and achieves maximum likelihood estimation of the scanning rate and target state through correction, iteration, and fusion of the target state.

[0004] Most existing methods assume that both the receiving station and the radiation source are stationary, ignoring the variations in interception time difference caused by relative motion between the two. This in turn leads to deviations in scanning angles, affecting tracking accuracy. Therefore, how to compensate for these differences in scanning angles and improve the tracking accuracy of moving scanning radiation sources is an urgent technical challenge in this field. Summary of the Invention

[0005] The object of the present invention is to provide a scanning radiation source tracking method based on filtering, so as to improve the tracking accuracy of a scanning radiation source in motion.

[0006] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0007] A scanning radiation source tracking method based on filtering, comprising:

[0008] Firstly, the observation station obtains the pulse signal of the received scanning radiation source and records the sampling time. The beam fitting model is used to perform beam fitting on the pulse signal, and the interception time of the pulse signal peak is estimated. Secondly, the scanning rate is estimated by using the time difference between the peak values ​​of two adjacent intercepted pulse signals at the observation station, and the scanning angle difference is calculated based on the scanning time difference of different observation stations. Finally, the scanning angle difference is used as the observation variable to perform extended Kalman tracking and positioning, and the tracking results are used to estimate and compensate for the scanning angle difference, thereby realizing the joint estimation of the scanning radiation source position and scanning rate.

[0009] Furthermore, by sampling the parabola as the beam fitting model, the least square method is used to solve the parameters in the beam fitting model to obtain the parabola equation corresponding to the pulse signal, thereby determining the interception reception time corresponding to the peak value of the pulse signal.

[0010] Furthermore, the estimating of the scanning rate by using the time difference between two adjacent peak values ​​of the pulse signal intercepted by the observation station includes:

[0011] The difference in interception reception time corresponding to the peak value of the pulse signal generated by two adjacent scans of the scanning radiation source intercepted by the observation station is used as the estimated value T of the scanning period of the scanning radiation source p ; That is, for two adjacent intercepted scanning signals, calculate the corresponding interception reception time and calculate the difference; the scan rate estimate is: The estimated value of the scanning period T is obtained by using the relative motion relationship between the scanning radiation source and the observation station. p Compensation, namely:

[0012]

[0013] Where m is the number of scans of the scanning radiation source, are the estimated peak interception times of the pulse signals received for the mth and m-1th scans, respectively. c is the speed of light, and Δr is the distance change caused by the movement of the scanning radiation source, that is:

[0014]

[0015] where p i =[x,y], i≥2 is the location of observation station i, u i (m) = [x i (m),y i (m)]、u i (m-1)=[x i (m-1),y i(m-1)] are the x and y coordinate positions of the scanning radiation source when it is scanned for the mth and m-1th time by observation station i, respectively; ||·|| represents the Euclidean distance of the solution vector, δt is the error caused by scanning fluctuation and its noise, and Δβ is the angular change caused by the movement of the scanning radiation source.

[0016] Furthermore, the calculating of the scanning angle difference based on the scanning time difference of different observation stations includes:

[0017] Estimated value based on compensated scan period The interception time of the peak value estimated by combining the pulse signal received by different observation stations i and j during the mth scanning of the scanning radiation source Get the scanning time difference between different observation stations i and j The scanning angle difference between observation stations i and j is calculated using the following formula:

[0018]

[0019] Among them, tdoa=ΔR / c, ΔR=||p i -u i (m)||-||p j -u j (m)|| is the distance difference between the scanning radiation source and observation stations i and j, tdoa is the time difference caused by the distance difference, u j (m) is the coordinate position of the scanning radiation source when it is scanned by observation station j, p j is the coordinate position of observation station j.

[0020] Furthermore, the extended Kalman tracking and positioning is performed using the scanning angle difference as an observation variable, including:

[0021] The first step is to establish the model and define the state variables as:

[0022] X=[x,y,v,a,θ] T (10)

[0023] Wherein, x, y are the position coordinates of the scanning radiation source, v is the velocity component of the scanning radiation source, a is the acceleration component of the scanning radiation source, and θ is the steering angle of the scanning radiation source;

[0024] Combined with the mapping relationship between the motion characteristics of the scanning radiation source and the scanning angle difference, the scanning angle difference is used to is the observed variable, and the observed variable is used as the nonlinear observation function h(x,y) of the radiation source position x,y, that is:

[0025]

[0026] In the above formula, (xi ,y i )、(x j ,y j ) are the position coordinates of observation stations i and j respectively;

[0027] Therefore, the observation model can be written as:

[0028] z k =h(X k )+λ k (12)

[0029] Where k represents the number of Kalman update iterations, z k is the observation model at the kth iteration, X k =[x k ,y k ,v k ,a k ,θ k ] T are the state variables x, y, v, a, θ of the scanning radiation source at the kth iteration; λ k Represents the observation noise error, which satisfies the zero-mean Gaussian distribution;

[0030] For each observation, the Jacobian matrix of the corresponding state variable needs to be calculated, that is:

[0031]

[0032] Furthermore, the estimating and compensating the scanning angle difference using the tracking result includes:

[0033] Update the state vector and covariance matrix, and the state transfer equation is:

[0034]

[0035] Among them, X k-1 is the state vector of the scanning radiation source at the k-1th iteration, f(X k-1 ) is the state transfer matrix, that is:

[0036]

[0037] in is the peak intercept time at the kth iteration and the peak intercept time at the k-1th iteration The difference, is the acceleration, is the steering speed, which is calculated by updating the result of each iteration;

[0038] is the state variable of the scanning radiation source predicted at the kth iteration, w k-1 is the process noise, which is assumed to be zero-mean Gaussian noise;

[0039] By calculating the Jacobian matrix F of the state vector of the state transfer function f() k To achieve linearization:

[0040]

[0041] According to the linearized Jacobian matrix, the covariance matrix of the state prediction error is predicted:

[0042] P k|k-1 =F k P k-1 F k H +Q k-1 (17)

[0043] Among them, P k|k-1 is the covariance matrix of the state prediction error of the kth iteration, P k-1 is the covariance matrix of the state prediction error of k-1 iterations, Q k-1 is the process noise covariance matrix, which can be expressed as in(·) H Indicates the conjugate transpose operation on the matrix;

[0044] Use the measured values ​​to update the state estimate, that is:

[0045]

[0046] Among them, X k is the state variable of the scanning radiation source at the kth iteration, is the predicted value of the kth iteration

[0047] The state variable of the scanning radiation source, z k is the observation model at the kth iteration, h(X k|k-1 ) is the state variable^

[0048] X k|k-1 The predicted observation value calculated by the nonlinear observation function, K k is the Kalman gain, which is used to balance the weights between the predicted value and the measured value. It is calculated as follows:

[0049]

[0050] Among them, P k|k-1 is the covariance matrix of the state prediction error predicted at the kth iteration, H k is the Jacobian matrix of the state variables, R kis the covariance matrix of the observation noise, that is

[0051] The covariance matrix of the state prediction error is updated as:

[0052] P k =(IK k H k )P k|k-1 (20)

[0053] Here, I is the identity matrix with all diagonal elements being 1 and all other elements being 0.

[0054] Furthermore, the joint estimation of the scanning radiation source position and the scanning rate includes:

[0055] Based on the state variable X of the scanning radiation source at the kth and k-1th iterations k and X k-1 , X k and X k-1 The position coordinates of the scanning radiation source in are respectively taken as u i (m),u i (m-1) is substituted into Equation 7 to calculate the k-th iteration Δr, and then the estimated value of the scan period after compensation is calculated based on Equation 6 use The scanning rate is estimated, and the scanning angle difference is calculated using Equation 9, which is used as the value of the nonlinear observation function of the k+1 iteration to calculate the state variable X of the scanning radiation source of the k+1 iteration. k+1 ; where X k+1 x in k+1 ,y k+1 That is, the position tracking result of the scanning radiation source. The scanning radiation source can be tracked through several consecutive iterations.

[0056] An observation station uses a filtering-based scanning radiation source tracking method to track a scanning radiation source.

[0057] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor is executed by a computer, the filtering-based scanning radiation source tracking method is implemented.

[0058] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the filtering-based scanning radiation source tracking method is implemented.

[0059] Compared with the prior art, the present invention has the following technical features:

[0060] The present invention makes full use of the characteristics of the scanning radiation source and the influence of the relative motion relationship between the observation station and the radiation source on the scanning angle difference, and combines the EKF algorithm to correct the scanning rate, thereby improving the tracking accuracy of the moving radiation source. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the scenario of scanning radiation source tracking when three observation stations are used;

[0062] Figure 2 is a method flow chart of an embodiment of the present invention;

[0063] Figure 3 (a) is the tracking effect of the example of the present invention when the scanning radiation source makes a maneuvering motion and its comparison with the least squares algorithm (LS); (b) is the tracking error of the example of the present invention and its LS algorithm when the scanning radiation source makes a maneuvering motion. DETAILED DESCRIPTION

[0064] The existing tracking and positioning method of scanning radiation sources is only suitable for static targets. For moving scanning radiation sources, there are problems of scanning angle deviation and poor tracking accuracy. Based on this, the present invention provides a scanning radiation source tracking method based on filtering. First, the observation station obtains the pulse signal of the received scanning radiation source and records the sampling time. By fitting the scanning radiation source beam, the interception time of the pulse signal peak is estimated; secondly, the scanning rate is estimated by using the time difference between the peak values ​​of the pulse signals intercepted by two adjacent observation stations, and the scanning angle difference is calculated based on the scanning time difference of different observation stations; finally, the scanning angle difference is used as the observation variable to perform extended Kalman (EKF) tracking and positioning, and the tracking result is used to estimate and compensate for the scanning angle difference, thereby achieving a joint and accurate estimation of the scanning radiation source position and scanning rate, thereby improving tracking accuracy.

[0065] See attached Figure 1 In this scheme, at least two stationary observation stations are set up. The antenna type of the observation station is omnidirectional. The scanning radiation source is in motion and performs circular scanning motion, and the scanning beam is a narrowband beam. The observation station intercepts the signal emitted by the scanning radiation source and measures the arrival time difference. It combines the scanning time difference of the two observation stations and the periodic characteristics of the scanning radiation source for analysis, and finally realizes the tracking of the moving scanning radiation source.

[0066] Step 1: The pulse signals generated by the scanning radiation source in motion during the circular scanning are captured by multiple observation stations of our side. For each observation station, the scanning radiation source captures N pulse signals each time the observation station scans, and the corresponding time information of the received pulse signals is t = [t1, t2, ..., t N ], the sampling parabola is used as the beam fitting model, and the expression is:

[0067] y(t)=at 2 +bt+c (1)

[0068] Where y(t) represents the power value of the pulse signal received at time t, and a, b, and c are the coefficients of the beam model to be solved.

[0069] Therefore, for the pulse signals of N scanning radiation sources, the beam fitting solution equation can be expressed as:

[0070]

[0071] The equation can be simplified to:

[0072] Y=KX (3)

[0073] in,

[0074] In the above formula, y(t N ) represents t N The beam fitting model corresponding to the time instant.

[0075] Using the least squares method to solve the parameter X = [a, b, c], the result is:

[0076]

[0077] The superscript T represents the transpose operation. are the estimated values ​​of the parameters a, b, and c of the beam fitting model. Substituting them into Equation 1 yields a parabolic equation. Therefore, the beam fitting model can be used to estimate the interception reception time corresponding to the peak value of the pulse signal obtained in this scan:

[0078]

[0079] Step 2: For any observation station, the difference in interception reception time corresponding to the peak value of the pulse signal generated by two adjacent scans of the scanning radiation source intercepted by the observation station is used as the estimated value T of the scanning period of the scanning radiation source. p ; That is, for the two adjacent intercepted scanning signals, use the method in step 1 to calculate the corresponding interception reception time and calculate the difference; the scan rate estimate is: However, due to the relative position relationship between the moving scanning radiation source and the stationary observation station, the time difference between the two intercepted pulse signals does not necessarily cover an angle of 2π, which leads to a deviation in the estimation of the scanning rate. Therefore, the relative motion relationship between the scanning radiation source and the observation station is used to estimate the scanning period T. p Compensation, namely:

[0080]

[0081] Where m is the number of scans of the scanning radiation source, are the estimated peak interception times of the pulse signals received for the mth and m-1th scans, respectively. c is the speed of light, and Δr is the distance change caused by the movement of the scanning radiation source, that is:

[0082] Δr=||p i -u i (m)||-||p i -u i (m-1)|| (7)

[0083] where p i =[x,y], i≥2 is the location of observation station i, u i (m) = [x i (m),y i (m)]、u i (m-1)=[x i (m-1),y i (m-1)] are the x and y coordinate positions of the scanning radiation source when it is scanned for the mth and m-1th times by observation station i, respectively; in this scheme, the coordinates refer to the coordinates in the two-dimensional coordinate system established in the plane where the scanning radiation source is located; ||·|| represents the Euclidean distance of the solution vector, δt is the error caused by scanning fluctuation and its noise, and Δβ is the angular change caused by the movement of the scanning radiation source, that is:

[0084]

[0085] Step 3, based on the estimated value of the scan period after compensation The interception time of the peak value estimated by combining the pulse signal received by different observation stations i and j during the mth scanning of the scanning radiation source (m) Get the scanning time difference between different observation stations i and j The scanning angle difference between observation stations i and j is calculated using the following formula:

[0086]

[0087] Among them, tdoa=ΔR / c, ΔR=||p i -u i (m)||-||p j -u j (m)|| is the distance difference between the scanning radiation source and observation stations i and j, tdoa is the time difference caused by the distance difference, u j (m) is the coordinate position of the scanning radiation source when it is scanned by observation station j, p j is the coordinate position of observation station j.

[0088] Step 4: Scan angle difference As the observed variables, the EKF algorithm is used to predict and update the state variables of the scanning radiation source.

[0089] The first step is to establish the model and define the state variables as:

[0090] X=[x,y,v,a,θ] T (10)

[0091] Among them, x, y are the position coordinates of the scanning radiation source, v is the velocity component of the scanning radiation source, a is the acceleration component of the scanning radiation source, and θ is the steering angle of the scanning radiation source.

[0092] Combined with the mapping relationship between the motion characteristics of the scanning radiation source and the scanning angle difference, the scanning angle difference is used to is the observed variable, and the observed variable is used as the nonlinear observation function h(x,y) of the radiation source position x,y, that is:

[0093]

[0094] In the above formula, (x i ,y i )、(x j ,y j ) are the position coordinates of observation stations i and j respectively.

[0095] Therefore, the observation model can be written as:

[0096] z k =h(X k )+λ k (12)

[0097] Where k represents the number of Kalman update iterations, z k is the observation model at the kth iteration, X k =[x k ,y k ,v k ,a k ,θ k ] T are the state variables x, y, v, a, θ of the scanning radiation source at the kth iteration; λ k represents the observation noise error, which satisfies the zero-mean Gaussian distribution; where the nonlinear observation function h(X k ) is only related to the parameters (x,y), while the values ​​of v,a,θ can be calculated by estimating (x,y) twice in a row.

[0098] In the EKF algorithm, the Jacobian matrix of the observation function needs to be calculated for linearization. For each observation, the Jacobian matrix of the corresponding state variable needs to be calculated, that is:

[0099]

[0100] Step 5: Update the state vector and covariance matrix. The state transfer equation is:

[0101]

[0102] Among them, X k-1 is the state vector of the scanning radiation source at the k-1th iteration, f(X k-1 ) is the state transfer matrix, that is:

[0103]

[0104] in is the peak intercept time at the kth iteration and the peak intercept time at the k-1th iteration The difference, is the acceleration, is the steering speed, which is calculated by updating the result of each iteration; is the state variable of the scanning radiation source predicted at the kth iteration, w k-1 is the process noise, which is assumed to be zero-mean Gaussian noise.

[0105] The purpose of covariance prediction is to estimate the covariance matrix of the system state prediction error, reflecting the uncertainty of the predicted state. Since the state transfer equation f() is nonlinear, it needs to be linearized in the EKF; linearization is achieved by calculating the Jacobian matrix F of the state vector of the state transfer function f(). k accomplish:

[0106]

[0107] in, It represents the partial derivative of the state transfer equation.

[0108] According to the linearized Jacobian matrix, the covariance matrix of the state prediction error is predicted:

[0109] P k|k-1 =F k P k-1 F k H +Q k-1 (17)

[0110] Among them, P k|k-1is the covariance matrix of the state prediction error of the kth iteration, P k-1 is the covariance matrix of the state prediction error of k-1 iterations, Q k-1 is the process noise covariance matrix, which can be expressed as in(·) H Indicates the conjugate transpose operation on the matrix.

[0111] Step 6: Update the state estimate using the measured value, that is:

[0112]

[0113] Among them, X k is the state variable of the scanning radiation source at the kth iteration, is the state variable of the scanning radiation source predicted at the kth iteration, z k is the observation model at the kth iteration, is a state variable The predicted observation value calculated by the nonlinear observation function, K k is the Kalman gain, which is used to balance the weights between the predicted value and the measured value. It is calculated as follows:

[0114]

[0115] Among them, P k|k-1 is the covariance matrix of the state prediction error predicted at the kth iteration, H k is the Jacobian matrix of the state variables, R k is the covariance matrix of the observation noise, that is

[0116] The covariance matrix of the state prediction error is updated as:

[0117] P k =(IK k H k )P k|k -1 (20)

[0118] Here, I is the identity matrix with all diagonal elements being 1 and all other elements being 0.

[0119] Step 7: Repeat steps 4-6, and scan the state variable X of the radiation source based on the kth and k-1th iterations. k and X k-1 , X k and X k-1 The position coordinates of the scanning radiation source in are respectively taken as u i (m),u i (m-1) is brought into

[0120] The Δr of the kth iteration is calculated in Equation 7, and then the estimated value of the scan period after compensation is calculated based on Equation 6 use The scanning rate is estimated, and the scanning angle difference is calculated using Equation 9, which is used as the value of the nonlinear observation function of the k+1 iteration to calculate the state variable X of the scanning radiation source of the k+1 iteration. k+1 ; where X k+1 x in k+1 ,y k+1 That is, the position tracking result of the scanning radiation source. The scanning radiation source can be tracked through several consecutive iterations.

[0121] Example:

[0122] See attached Figure 1 In one embodiment of the present invention, the positioning scenario of the established scanning radiation source is as follows: Figure 1 As shown, the number of observation stations is 3, and the coordinate positions are [6×10 4 ,6×10 4 ]、[5.7×10 4 ,5.4×10 4 ]、[6.4×10 4 ,6.7×10 4 ], the starting position of the simulated scanning radiation source trajectory is [0, 0], and the scanning radiation source performs maneuvering motion with a maximum speed not exceeding 10 m / s, a maximum acceleration not exceeding 5 m / s, and a maximum steering angular velocity not exceeding 50° / s.

[0123] In addition, the radiation source performs circular scanning motion, the scanning rate is set to 3s, the main lobe beam width is 5°, and the pulse repetition interval is set to 2000ns.

[0124] In a stationary scenario, the main lobe time that each observation station can receive is 5 / 360*3=0.0417s, and the number of pulses that can be received in a single scan is 20850 pulses. However, since the relative motion direction of the scanning radiation source and the observation station is consistent with the scanning direction, the number of received pulses varies. Assuming that a single scan passes through the observation station, the number of pulses received by the observation station is N.

[0125] The results of this example are as follows Figure 3 shown; through Figure 3 It can be seen that the method of the present invention has high tracking accuracy for the trajectory of the scanning radiation source and small tracking error.

[0126] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A scanning radiation source tracking method based on filtering, characterized in that: include: First, the observation station receives the pulse signal of the scanning radiation source and records the sampling time. The beam fitting model is used to perform beam fitting on the pulse signal and estimate the interception time of the pulse signal peak. Secondly, the scanning rate is estimated using the time difference between the peak values ​​of the pulse signals intercepted twice by the observation station, and the scanning angle difference is calculated based on the scanning time difference between different observation stations. Finally, the extended Kalman tracking and positioning is performed with the scanning angle difference as the observation variable, and the tracking results are used to estimate and compensate the scanning angle difference, thereby realizing the joint estimation of the scanning radiation source position and scanning rate.

2. The filtering-based scanning radiation source tracking method according to claim 1, characterized in that: By taking the sampling parabola as the beam fitting model and solving the parameters in the beam fitting model using the least square method, the parabola equation corresponding to the pulse signal is obtained, thereby determining the interception reception time corresponding to the peak value of the pulse signal. .

3. The filtering-based scanning radiation source tracking method according to claim 1, characterized in that: The method of estimating the scanning rate by using the time difference between two adjacent peak values ​​of the pulse signal intercepted by the observation station includes: The difference in interception reception time corresponding to the peak value of the pulse signal generated by two adjacent scans of the scanning radiation source intercepted by the observation station is used as the estimated value of the scanning period of the scanning radiation source. ; That is, for two adjacent intercepted scanning signals, calculate the corresponding interception reception time and calculate the difference; the scan rate estimate is: ; Estimation of the scanning period using the relative motion relationship between the scanning radiation source and the observation station Perform compensation and obtain compensation value : (6) in, is the number of scans of the scanning radiation source, 、 For the first Second-rate, m -1 scan received pulse signal estimated peak intercept time, is the speed of light, The distance change of the scanning radiation source caused by movement is: (7) in For the observatory i location, 、 The scanning radiation source is at the observed station No. Second-rate, m -1 scan time x 、 y Coordinate location; represents the Euclidean distance of the solution vector, is the error caused by scanning fluctuation and its noise, The angle change caused by the movement of the scanning radiation source.

4. The filtering-based scanning radiation source tracking method according to claim 3, characterized in that: The calculating of the scanning angle difference based on the scanning time difference of different observation stations includes: Compensation value based on estimated value of scan period , combined with different observation stations i 、 j No. m The estimated peak intercept time of the pulse signal received during the scanning radiation source scanning 、 Get different observation stations i 、 j Scan time difference between , the observation station is calculated using the following formula i 、 j The scanning angle difference between them is: (9) in, , To scan radiation sources and observation stations i 、 j The distance difference, The time difference caused by the distance difference, To scan the radiation source, the observation station j The coordinate position at the time of scanning, Observation station for j The coordinate position of .

5. The filtering-based scanning radiation source tracking method according to claim 1, characterized in that: The extended Kalman tracking and positioning is performed using the scanning angle difference as an observation variable, including: The first step is to establish the model and define the state variables as: (10) in, is the position coordinate of the scanning radiation source, is the velocity component of the scanning radiation source, is the acceleration component of the scanning radiation source, is the steering angle of the scanning radiation source; Combined with the mapping relationship between the motion characteristics of the scanning radiation source and the scanning angle difference, the scanning angle difference is used to is the observed variable, and the observed variable is used as the radiation source position The nonlinear observation function ,Right now: (11) In the above formula, 、 Observation stations i 、 j The location coordinates of Therefore, the observation model can be written as: (12) in, represents the number of Kalman update iterations, For the k The observation model at the iteration, For the The state variables of the radiation source are scanned at the iteration ; Represents the observation noise error, which satisfies the zero-mean Gaussian distribution; For each observation, the Jacobian matrix of the corresponding state variable needs to be calculated, that is: (13)。 6. The filtering-based scanning radiation source tracking method according to claim 1, characterized in that: The method of estimating and compensating the scanning angle difference by using the tracking result includes: Update the state vector and covariance matrix, and the state transfer equation is: (14) in, For the The state vector of the scanning radiation source of the iteration, is the state transfer matrix, that is: (15) in , for the first Peak intercept time at iteration Hedi Peak intercept time during iteration The difference, , , , is the acceleration, is the steering speed, which is calculated by updating the result of each iteration; For the Iteratively predicted state variables of the scanning radiation source, is the process noise, which is assumed to be zero-mean Gaussian noise; By calculating the state transfer function The Jacobian matrix of the state vector To achieve linearization: (16) According to the linearized Jacobian matrix, the covariance matrix of the state prediction error is predicted: (17) in, For the The covariance matrix of the state prediction error of the iteration, for The covariance matrix of the iterative state prediction error, is the process noise covariance matrix, which can be expressed as ,in Indicates the conjugate transpose operation on the matrix; Use the measured values ​​to update the state estimate, that is: (18) in, For the The state variables of the radiation source are scanned at the iteration, For the Iteratively predicted state variables of the scanning radiation source, For the k The observation model at the iteration, is a state variable The predicted observation value calculated by the nonlinear observation function, is the Kalman gain, which is used to balance the weights between the predicted value and the measured value. It is calculated as follows: (19) in, For the The covariance matrix of the state prediction error predicted by the iteration, is the Jacobian matrix of the state variables, is the covariance matrix of the observation noise, that is ; The covariance matrix of the state prediction error is updated as: (20) in, It is an identity matrix with all diagonal elements being 1 and all other elements being 0.

7. The filtering-based scanning radiation source tracking method according to claim 1, characterized in that: The joint estimation of the scanning radiation source position and the scanning rate includes: Based on the 、 k -1 iteration to scan the state variables of the radiation source and ,use and The position coordinates of the scanning radiation source in the calculation k The distance change of the scanning radiation source due to movement of the iteration , then calculate the compensation value of the estimated value of the scanning period , calculate the scan rate estimate , and then calculate the scanning angle difference as The value of the nonlinear observation function of the iteration is used to calculate the The state variables of the scanning radiation source of the iteration ;in Contains the position coordinates , That is, the position tracking result of the scanning radiation source. The scanning radiation source can be tracked through several consecutive iterations.

8. An observation station, characterized in that: The observation station tracks the scanning radiation source using the filtering-based scanning radiation source tracking method according to any one of claims 1 to 7.

9. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor is executed by a computer, the filtering-based scanning radiation source tracking method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the filtering-based scanning radiation source tracking method according to any one of claims 1 to 7 is implemented.

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