A Near-Field Passive Target Tracking Method Based on Feedback Mechanism for Towed Wire Subarrays

Abstract

This invention discloses a near-field passive target tracking method based on a feedback mechanism using a towed array subarray. The method includes the following steps: S1, subarray division: Based on the required detection distance, the number of elements in the towed array, and the element spacing, the number of elements in the first and last subarrays is selected. With fixed towed array parameters, to ensure spatial non-correlation of the incoming wave signal, the further the detection distance, the larger the spacing between the two subarrays, and the smaller the number of elements in the subarrays. S2, beamforming: Based on the judgment result of the detector, a suitable beamforming algorithm is selected to solve for the target's azimuth information. S3, parameter setting: Based on the beamforming algorithm selected in S2 and combined with actual sea trial conditions, the measurement noise variance is adjusted. This invention, using only a single towed array, leverages the spatial non-correlation of the first and last subarrays of a long towed array, and utilizes focused beamforming and particle filtering algorithms under a feedback mechanism to achieve passive target direction finding, positioning, and tracking.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of passive sonar target direction finding and tracking processing technology, and particularly relates to a near-field passive target tracking method for a subarray of a towed line array based on a feedback mechanism. BACKGROUND

[0002] In recent years, various sonar devices have emerged in an endless stream. The towed line array sonar is widely used in the detection field due to its advantages of large aperture, far away from platform noise, and the like, without being limited by the space of a platform. In the detection of passive targets, a single towed line array can usually only obtain the azimuth information of the target, and cannot determine the position of the target. The common passive target positioning currently considers that multiple arrays are arranged in space, and the unique determination of the position of the target is realized by the distributed direction finding results. However, in actual situations, the arrangement of multiple arrays has certain limitations. At the same time, since the distance information of the passive target cannot be obtained, it is usually assumed that the array received signals meet the far-field condition, and the plane wave beam forming algorithm is used for the direction of arrival estimation. However, for a large-aperture towed line array, the received signals usually meet the spherical wave model, and there is a large direction finding error in the use of the plane wave beam forming to process the near-field signals, which is not conducive to the accurate positioning and tracking of the target. SUMMARY

[0003] The technical problem to be solved by the application is to provide a near-field passive target tracking method for a subarray of a towed line array based on a feedback mechanism, which can realize the direction finding, positioning and tracking of passive targets by using the spatial non-correlation of the head and tail subarrays of a long towed line array under the feedback mechanism on the premise of using only one towed line array.

[0004] The technical solution of the application is to provide a near-field passive target tracking method for a subarray of a towed line array based on a feedback mechanism, including the following steps,

[0005] S1, subarray division, the number of array elements of the head and tail subarrays is selected according to the required detection distance, the number of array elements of the towed line array and the array element spacing; in the case of fixed parameters of the towed line array, in order to meet the spatial non-correlation of the incoming wave signals, the farther the detection distance is, the larger the spacing of the two subarrays is, and the smaller the number of array elements of the subarray is;

[0006] S2, beam forming, the azimuth information of the target is solved by selecting a suitable beam forming algorithm according to the judgment result of the judge;

[0007] S3, parameter setting, the measurement noise variance is adjusted in combination with the actual sea trial situation according to the beam forming algorithm selected in S2, wherein the measurement noise variance should be smaller than the noise variance of the plane wave beam forming since the direction finding accuracy of the focused beam forming is higher;

[0008] S4. Tracking update: Send the DOA estimation results of the two sub-arrays to the particle filter tracker. In the Bayesian framework, combine the prior information of the target and the current measurement to complete the target positioning.

[0009] S5. Distance feedback: Calculate the distances between the target and the two sub-arrays based on the tracking results of S4 and feedback them to the beamformer.

[0010] Preferably, in S2, the judgment basis of the judge is as follows:

[0011] a. The current frame number N s When < N1, considering that the particle filter algorithm has not converged and the target distance error returned in the previous frame is large, the plane wave beamforming algorithm is used. Here, N1 is the number of frames for initially executing the plane wave beamforming and is set to be greater than the number of frames required for the particle filter algorithm to converge.

[0012] b. The current frame number N s = N1, the focused beamforming algorithm is used.

[0013] c. The current frame number N s > N1 and N s % N2 ≠ 0, the beamforming algorithm used in the previous frame is used, where N2 is the detection interval.

[0014] d. The current frame number N s > N1 and N s % N2 = 0, if the absolute value of the difference between the direction-finding angles of the plane wave and the focused beamforming is less than the judgment threshold, that is, |θ FCBF –θ PCBF |< △θ, the focused beamforming is used, otherwise the plane wave beamforming is used. Here, θ FCBF 、θ PCBF respectively represent the direction-finding angles of the focused beamforming and the plane wave beamforming, and △θ represents the angle judgment threshold.

[0015] In judgment criteria c and d, the setting of the detection interval can effectively ensure that when the target loses tracking, the plane wave beamforming algorithm is used for correction, avoiding the error amplification caused by negative feedback.

[0016] Preferably, in S4, the tracking process includes:

[0017] i. Initialization of particles and weights;

[0018] ii. Sequential importance sampling;

[0019] iii. Calculation of particle weights;

[0020] iv. Resampling;

[0021] v. Output and return data (process ii).

[0022] Compared with the prior art, the present invention has the following advantages after adopting the above solution:

[0023] This invention provides a high-precision direction finding, localization, and tracking method for near-field passive targets. Using only a single towed array, it leverages the spatial non-correlation of the first and last subarrays within the long towed array, employing focused beamforming and particle filtering algorithms under a feedback mechanism to achieve direction finding, localization, and tracking of passive targets. Compared to other passive target direction finding and localization methods, firstly, this technology eliminates the need for multiple arrays, obtaining multiple azimuth information and achieving target localization by dividing the array into subarrays; secondly, it utilizes target position information provided by the tracker, using focused beamforming to improve the direction finding accuracy of near-field targets, while the accurate direction finding results further enhance tracking performance, creating a positive feedback loop; finally, by setting a judgment criterion, the technology ensures correction of trajectory deviations when the target loses tracking, avoiding negative feedback. This technology can achieve stable and high-precision localization and tracking of near-field passive targets. Attached image description:

[0024] Figure 1 This is a schematic diagram of the feedback tracking process of the present invention.

[0025] Figure 2 This is a schematic diagram of the decision-maker.

[0026] Figure 3 This is a motion diagram of the target and the two subarrays of the dragline array in this embodiment.

[0027] Figure 4 During the tracking process in this embodiment, the azimuth history diagrams of beamforming using the feedback mechanism and plane wave beamforming for the two arrays are shown respectively. (a) Azimuth history diagram of beamforming using the feedback mechanism, and (b) Azimuth history diagram of plane wave beamforming.

[0028] Figure 5 The diagram illustrates the direction finding errors of beamforming with feedback mechanism and plane wave beamforming, with (a) beamforming with feedback mechanism and (b) plane wave beamforming.

[0029] Figure 6 The diagram illustrates the particle filter tracking trajectory and error formed using feedback mechanisms and plane wave beams, respectively.

[0030] Figure 7 This is a schematic diagram showing the relationship between particle filter tracking error and target distance.

[0031] Figure 8 This is a diagram showing the orientation history of the two arrays during the sea trial experiment in this embodiment.

[0032] Figure 9 Schematic diagram of the particle filter tracking trajectory and cross-location based on the feedback mechanism in the sea trial experiment. Specific implementation method:

[0033] The following further describes the present invention in conjunction with the accompanying drawings for the specific implementation method:

[0034] A near-field passive target tracking method for a towed line subarray based on a feedback mechanism, as Figure 1 shown. First, take the data received by the towed line array as the input and divide the subarray; then perform beamforming processing according to the result of the discriminator (decision maker) to estimate the angle of the incoming wave; then set the tracking parameters according to the selected beamforming algorithm; then take the angle estimation values of the two arrays as the input, and combine the prior information of the target historical track to perform particle filter tracking to update the target position; finally, based on the position information of the target and the array, calculate the distance between the target and the array and feedback it to the beamformer. The specific steps include

[0035] 1. Subarray division. According to the detection distance required by the experiment, the number of array elements and the element spacing of the towed line array, select the number of array elements of the head and tail subarrays. When the parameters of the towed line array are fixed, in order to meet the spatial non-correlation of the incoming wave signal, it is required that the farther the detection distance, the larger the spacing between the two subarrays and the smaller the number of array elements of the subarray.

[0036] 2. Beamforming. According to the judgment result of the discriminator, select a suitable beamforming algorithm to solve the azimuth information of the target.

[0037] As Figure 2 shown, the judgment basis of the discriminator is as follows: [[ID=2—4]]

[0038] a. The current frame number N s When < N1, considering that the particle filter algorithm has not converged and the target distance error returned by the previous frame is relatively large, the plane wave beamforming algorithm is used. Among them, N1 is the number of frames for initially performing plane wave beamforming and needs to be set to be greater than the number of frames required for the particle filter algorithm to converge.

[0039] b. The current frame number N s = N1, the focused beamforming algorithm is used.

[0040] c. The current frame number N s > N1 and N s % N2 ≠ 0, the beamforming algorithm used in the previous frame is used, where N2 is the detection interval.

[0041] d. The current frame number N s > N1 and N s % N2 = [0], if the absolute value of the difference between the direction finding angles of the plane wave and the focused beamforming is less than the judgment threshold, that is, |θ FCBF –θPCBF If |<△θ, then focused beamforming is used; otherwise, plane wave beamforming is used. Where θ FCBF θ PCBF Δθ represents the direction finding angles for focused beamforming and plane wave beamforming, respectively, and Δθ represents the angle judgment threshold.

[0042] In judgment criteria c and d, the setting of the detection interval can effectively ensure that when the target is lost, the plane wave beamforming algorithm is used for correction, avoiding the error amplification caused by negative feedback.

[0043] In other words, based on the current frame number and the data received by the subarray, either focused beamforming or plane wave beamforming algorithms are selected for direction finding.

[0044] 3. Parameter settings. Based on the beamforming method selected in step 2 and the actual sea trial conditions, adjust the measurement noise variance. Since focused beamforming has higher direction finding accuracy, its measurement noise variance should be smaller than that of plane wave beamforming.

[0045] 4. Tracking and Update. The DOA estimation results of the two subarrays are sent to the particle filter tracker. Under the Bayesian framework, the target's prior information and current measurements are combined to complete the target localization. The tracking process includes:

[0046] i. Initialization of particles and weights.

[0047] ii. Sequential importance sampling.

[0048] iii. Calculate particle weights.

[0049] iv. Resampling.

[0050] v. Output and return data (process ii).

[0051] 5. Distance Feedback. Based on the tracking results from step 4, the distance between the target and the two subarrays is calculated and fed back to the beamformer.

[0052] like Figure 3 This embodiment provides a motion situation diagram of the target and the two subarrays of the towed array. The towed array consists of 512 isotropic array elements with an element spacing of 1.2m. 128 array elements from the head and tail of the array are taken to form two base arrays, each monitoring the target's azimuth. The first array element of each base array is used as the reference array element position. In a straight line state, the distance between the two base arrays is 460.8m. Figure 4 The following diagrams show the azimuth history of the two arrays during the tracking process, using beamforming with feedback mechanism (a) and plane wave beamforming (b), respectively, where N1=20 and N2=20, meaning that the first 20 frames of data use plane wave beamforming.

[0053] Figure 5 A schematic diagram illustrating the direction-finding errors of beamforming with a feedback mechanism and plane wave beamforming is presented. The comparison shows that, under the feedback mechanism, the focusing beamforming algorithm effectively reduces the direction-finding error for near-field targets. Furthermore, Figure 6 The particle filter tracking trajectories and errors formed using feedback mechanisms and plane wave beamforming are presented respectively. The comparison shows that the particle filter tracking technique based on feedback mechanisms can effectively reduce the tracking error of the target in the x-axis and y-axis directions.

[0054] Figure 7 The relationship between particle filter tracking error and target distance is presented. The figure keeps the target trajectory constant, only changing the distance between the target and the array, and uses 50 Monte Carlo experiments to solve for the average tracking error. The farther the target is from the array, the smaller the difference in direction finding between the two arrays, and the greater the impact of direction finding error on positioning; therefore, the tracking error increases with increasing target distance. When the distance is less than 14km, i.e., when the near-field condition is met, the beamforming of the feedback mechanism has better performance than plane wave beamforming. When the distance is greater than 14km, according to the parameters of the towed array, the far-field condition is met, and the tracking effect of the beamforming of the feedback mechanism is similar to that of plane wave beamforming. Figure 8 The diagram shows the orientation history of the two arrays during a sea trial experiment, with N1 = 100. Figure 9 The results show the tracking trajectory and cross-positioning results based on the feedback mechanism of particle filtering during sea trials. The comparison reveals that the tracking results using particle filtering provide a more stable target trajectory compared to cross-positioning.

[0055] The above description only illustrates preferred embodiments of the present invention and should not be construed as limiting the scope of the claims. Any equivalent structural or procedural modifications made using this specification are included within the patent protection scope of the present invention.

Claims

1. A method for passive target tracking of a near field of a linear array based on a feedback mechanism, characterized in that: The method comprises the following steps: S1, subarray division, according to the required detection distance, the number of array elements and the array element spacing of the towed line array, the number of array elements of the first and last subarrays is selected; S2, beam forming, according to the judgment result of the judging device, a suitable beam forming algorithm is selected to solve the azimuth information of the target; S3, parameter setting, according to the beam forming algorithm selected in S2, the measurement noise variance is adjusted in combination with the actual sea trial situation, wherein the measurement noise variance should be smaller than the noise variance of the plane wave beam forming; S4, tracking update, the DOA estimation results of the two subarrays are sent to the particle filter tracker, in the Bayesian framework, the positioning of the target is completed in combination with the prior information of the target and the current measurement; S5, distance feedback, the distances between the target and the two subarrays are calculated through the tracking results of S4, and are fed back to the beam former; In S2, the judgment basis of the judging device is as follows: a. current frame number N s <N1, a plane wave beamforming algorithm is used, wherein N1 is a frame number at which the plane wave beamforming is initially performed, and is set to be greater than a frame number required for the particle filter algorithm to converge; b. Current frame number N s = N1, use focused beamforming algorithm; Current frame number N s N1 and N s When N2=0, if the absolute value of the difference between the direction finding angle of the plane wave and the direction finding angle of the focused beam forming is less than the judgment threshold, that is, |θ FCBF -θ PCBF | <△θ, the focused beam forming is used, otherwise the plane wave beam forming is used; wherein, θ FCBF , θ PCBF respectively represent the direction finding angle of the focused beam forming and the plane wave beam forming, and△θ represents the angle judgment threshold. Current frame number N s >N1 and N s % When N2≠0, use the beamforming algorithm used in the previous frame, where N2 is the detection interval.

2. The feedback mechanism based near-field passive target tracking method of a linear array of claim 1, wherein: In S1, under the condition that the parameters of the towed line array are fixed, the farther the required detection distance is, the larger the spacing of the two subarrays is, and the smaller the number of array elements of the subarrays is.

3. The method of claim 1, wherein the method is a feedback-mechanism-based passive target tracking method for a linear array of dipoles. In S4, the tracking process comprises: i. initialization of particles and weights; ii. sequential importance sampling; iii. calculation of particle weights; iv. resampling; v. data output and return to process ii.

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