A multi-target direction finding cross-location ghost point detection method based on speed variance

Abstract

The application discloses a multi-target direction-finding cross-positioning ghost point detection method based on speed variance, which comprises the following steps: cutting off the direction-finding line sequences measured by observation station A and observation station B respectively by using a rectangular window; performing double-station cross-positioning calculation based on the cut-off two-station direction-finding line sequences to obtain a plurality of intersection position sequences; obtaining the speed sequence of each intersection point based on the position difference of adjacent time points and the direction symbol synthesis, and calculating the standard deviation of each intersection point speed sequence; for each direction-finding line of the observation station A, taking the minimum value of the speed standard deviation corresponding to all intersection points generated by the direction-finding line as a reference, and determining the intersection point with the speed standard deviation greater than the preset threshold coefficient times the reference value as a ghost point and removing the ghost point. The application only relies on double-station observation, does not need multi-platform redundant observation, does not need to establish a target track or a priori motion model, and can effectively suppress ghost points in the scene where a plurality of real targets and two direction-finding stations cannot be associated.

Description

Technical Field

[0001] This invention belongs to the field of passive positioning technology, and specifically relates to a method for detecting ghost points in multi-target direction finding cross-positioning based on velocity variance. Background Technology

[0002] Multi-target direction finding cross-location is a typical passive positioning technique. Its core principle is to use two moving or fixed observation stations to find the directions of multiple radiation source targets within the same spatiotemporal range, and then calculate the target's position by utilizing the intersection of multiple direction finding lines in space. This technique has significant application value in the field of radiation source positioning because it does not rely on active radiation and has strong concealment.

[0003] However, in practical applications, when two observation stations perform direction finding on multiple targets, if it is not possible to accurately establish a one-to-one correspondence between the direction finding lines and the targets (i.e., fuzzy direction finding association), simply performing pairwise cross-location on all the direction finding lines will generate a large number of false cross-points, i.e., ghost points.

[0004] The presence of ghost points severely interferes with target state estimation: on the one hand, they artificially increase the number of radiation source targets, leading to a significant increase in the false alarm rate; on the other hand, a large number of false points are mixed with real points, increasing the difficulty of identifying real targets and thus affecting the output reliability of the positioning system. Therefore, how to effectively detect and suppress ghost points has become a key issue in improving the performance of passive positioning systems.

[0005] Existing methods for ghost point suppression can be mainly categorized into three types: those based on geometric constraints, those based on redundant observations, and those based on motion characteristics. Methods based on geometric constraints utilize the spatial configuration between the observation station and the target, the angle between the direction finding lines, or the residuals of the intersection point relative to the direction finding lines of each observation station for judgment. These methods have low computational complexity, but in dense target scenarios, the geometric differences between real and accidental intersections tend to weaken, leading to a significant decrease in detection performance. Methods based on redundant observations rely on observation data from multiple stations or at multiple time points, using consistency checks or voting mechanisms to eliminate ghost points. Typical examples include algorithms based on Hough transform and multiple hypothesis association. These methods can effectively suppress random intersections, but they require high system synchronization accuracy and communication bandwidth, making engineering implementation difficult. Methods based on motion characteristics attempt to identify ghost points by utilizing the continuity of target motion, but their performance usually depends on accurate motion models and prior assumptions, facing limitations in scenarios with high target mobility or complex motion patterns.

[0006] The limitations of existing methods are mainly reflected in two dimensions: First, methods based on single-time geometric constraints show significant performance degradation in dense target scenes; second, methods relying on multi-station collaboration and prior motion models are limited by the system synchronization accuracy and model generalization ability, making it difficult to adapt to complex and ever-changing observation conditions. Summary of the Invention

[0007] To address the technical problem in the background art where multiple real targets cannot be correlated in the direction finding of two stations, and where estimating the target position using the intersection of direction finding lines will generate a large number of ghost points, this invention proposes a ghost point detection method for multi-target direction finding cross-positioning based on velocity variance.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for detecting ghost points in multi-target direction-finding cross-positioning based on velocity variance is characterized by its application to a bi-station direction-finding cross-positioning system consisting of observation station A and observation station B, wherein the coordinates of observation station A are... The coordinates of observation station B are This includes the following steps: Step 1: Use a rectangular window to observe the direction finding sequence at observation station A. The direction finding line sequence is truncated to obtain the truncated sequence; the direction finding line sequence at observation station B is truncated using the rectangular window described above to obtain the truncated sequence; where i=1,2,…, , Here are the number of direction finding lines measured at observation station A; j=1,2,…, , is the number of direction finding lines measured by observation station B; n is the discrete time sequence number; Step 2: Based on the truncated direction finding line sequence of observation station A and the direction finding line sequence of observation station B from Step 1, perform pairwise intersection positioning calculations for each direction finding line of observation station A and each direction finding line of observation station B to obtain the intersection point position sequence corresponding to each group (i, j). ,common A sequence of intersection points; Step 3: For each intersection point position sequence obtained in Step 2, synthesize the velocity sequence of that intersection point based on the position difference between adjacent time points and with direction signs; Step 4: Calculate the standard deviation of the velocity sequence at each intersection point obtained in Step 3. ; Step 5: For each direction finding line i at observation station A, the intersection of that direction finding line and observation station B... The minimum value among the velocity standard deviations corresponding to all intersection points obtained by the pairwise intersection of the direction finding lines is taken as the reference value; the velocity standard deviation is then used as the reference value. Greater than The intersection point of the reference value is determined to be a ghost point, where For the preset threshold coefficient and Greater than 1.

[0009] Furthermore, the rectangular window used in step one... For a piecewise function, when ≤n≤ When +N-1, =1; when n < or n> When +N-1, =0; where The initial moment of the capture is N, where N is the length of the rectangular window.

[0010] Furthermore, the sequence of intersection points in step two Solve using the following formula: Furthermore, the position difference between adjacent time points in step three is defined by the following formula: Furthermore, the synthesis of the directional symbol in step three is performed according to the following formula: in It is a symbolic function.

[0011] Furthermore, the preset threshold coefficient The value range is from 1.5 to 5.

[0012] Furthermore, it also includes step six: dividing the intersection points identified as ghost points in step five from the... Remove the remaining intersection points from the sequence of intersection points and output the remaining intersection points as the true target locations.

[0013] Furthermore, the window length N of the rectangular window ranges from 50 to 500.

[0014] The ghost point detection method for multi-target direction finding cross-positioning based on velocity variance provided by this invention has the following beneficial effects: First, ghost point detection can be completed using only two observation stations (dual-station), eliminating the need for redundant observations on multiple platforms, significantly reducing system deployment costs and inter-station synchronization requirements; Second, it does not require establishing target tracks or prior motion models, but directly performs statistical decisions based on the standard deviation of the intersection velocity sequence, resulting in a simple algorithm structure and low computational overhead; Third, it uses a multiple of the minimum standard deviation of the velocity of all intersection points on the same direction finding line as the decision threshold, which is a relative threshold decision, insensitive to the magnitude of the real target velocity and noise level, and has good scene adaptability; Fourth, it effectively solves the problem of generating a large number of ghost points when multiple real targets cannot be correlated with two direction finding stations, relying solely on the intersection of direction finding lines, breaking through the resolution bottleneck of instantaneous geometric intersection in congested spatial scenarios, and significantly improving the detection purity of real targets and the robustness of data correlation in complex electromagnetic environments. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the ghost point detection method for multi-target direction finding cross-location based on velocity variance provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0017] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "Station A" and "Station B" refer to two independent observation stations, the relative positions of which are uniquely determined by their coordinates.

[0018] This invention provides a method for detecting ghost points in multi-target direction finding cross-location based on velocity variance. The application scenario is as follows: In a planar coordinate system, two passive direction finding stations, observation station A and observation station B, are set up. The coordinates of observation station A are... The coordinates of observation station B are There are multiple radiation source targets in space, each radiating electromagnetic signals outward; observation station A performs direction finding on all radiation sources within its observation range at each discrete time n, and obtains a total of... There are 10 direction finding lines, and the sequence of the i-th direction finding line is denoted as . i=1,2,…, Observation station B performs direction finding on all radiation sources within its observation range at each discrete time n, and obtains a total of [number missing] directions. There are 1, 2, ... , ... .

[0019] Observation station A and observation station B synchronously determine the direction at discrete time n, that is, the two stations determine the direction of the radiation source in their respective fields of view at the same time. The two stations can achieve clock alignment through GPS time synchronization, wired synchronization or other known clock synchronization methods to ensure that the direction finding data of the two stations on which the cross-positioning solution is based are strictly corresponding in time.

[0020] Since observation stations A and B cannot accurately associate each direction finding line with a specific target in advance, simply performing cross-location calculations on any direction finding line of observation station A and any direction finding line of observation station B will yield a sequence of multiple intersection points containing the real target position and the ghost point position. The method in this embodiment of the invention is used to identify and remove ghost points from these intersection points.

[0021] like Figure 1 As shown, the method of this embodiment of the invention specifically includes the following steps.

[0022] Step 1: Use a rectangular window to truncate the direction finding sequence of observation station A and the direction finding sequence of observation station B respectively.

[0023] Specifically, the truncation of the i-th direction-finding line at observation station A is shown in the following formula: Similarly, the same rectangular window treatment is used for the truncation of the j-th direction-finding line at observation station B. Wherein The rectangular window function is defined as follows: in This indicates the initial time of the cutoff, and N represents the length of the rectangular window. In this embodiment, we take... =0, window length N=100 (as a preferred embodiment, the typical value range of window length N is 50 to 500).

[0024] Step 2: Based on the truncated direction finding line sequences of observation station A and observation station B obtained in Step 1, perform pairwise cross-location calculations for each direction finding line of observation station A and each direction finding line of observation station B to obtain the sequence of intersection points corresponding to each group (i, j). The specific solution formula is shown below: in Let A be the coordinates of observation station A. Here are the coordinates of observation station B. For observation station A... The direction finding line and the observation station B By performing pairwise intersection calculations on each of the direction finding lines, a total of [number] results can be obtained. A sequence of intersection points.

[0025] Step 3: For each intersection point position sequence The velocity sequence of the intersection point is obtained by synthesizing the position difference between adjacent time points with direction signs.

[0026] Specifically, the position difference between adjacent time points is defined as follows: Based on the above difference, a velocity sequence is obtained by synthesizing with directional symbols: in The sign function is defined as follows: when the product of the horizontal and vertical differences is positive, the sign function is +1; when the product is negative, the sign function is -1; and when the product is zero, the sign function is 0. This sign function effectively preserves the directional information of the intersection point motion, ensuring that reciprocating motion or back-and-forth jitter can be correctly represented in the velocity sequence.

[0027] Step 4: Calculate the standard deviation of the velocity sequence at each intersection point. As shown in the following formula: in This is the standard deviation calculation function, which calculates the sample standard deviation for each intersection velocity sequence within a window length N.

[0028] Specifically, since the position difference between adjacent time moments requires position values ​​at two time points, n and n-1, the velocity sample corresponding to the first discrete time point n0 does not exist; the input sample sequence for the standard deviation is V. ij (n0+1), V ij (n0+2),……, V ij (n0+N-1), a total of N-1 samples.

[0029] Step 5: Ghost Point Detection. For each direction finding line i (i=1,2,…) at observation station A... ), at the intersection of the direction finding line and observation station B The minimum value among the standard deviations of velocities corresponding to all intersection points obtained from the pairwise intersections of the direction finding lines is taken as the reference value; intersection points that satisfy the following formula are determined as ghost points: in For the preset threshold coefficient and Greater than 1. In this embodiment, it is taken as... =2.0 (as a preferred embodiment, The typical value range is 1.5 to 5.

[0030] The technical principle behind the above-mentioned ghost point determination is as follows: For the same direction finding line (such as the i-th direction finding line of observation station A), this direction finding line and the direction finding line of observation station B... By intersecting each direction finding line in pairs, we can obtain... A sequence of intersection points; at most one of these intersection points corresponds to the real target, and the rest are ghost points. The real target's position sequence is smooth and continuous in time, resulting in a velocity sequence with small fluctuations and a small velocity standard deviation; while the ghost point position sequence is a random drift trajectory formed by the intersection of two independent direction finding lines, exhibiting significant temporal fluctuations and a large velocity standard deviation. Therefore, for the same direction finding line... indivual Among the values, the minimum value most likely corresponds to the actual target; with this minimum value... of The threshold is set at 100 times; intersections exceeding this threshold are considered ghost points, which can effectively identify and eliminate ghost points.

[0031] This method is applicable to scenarios where each direction finding line of observation station A corresponds to at most one real target within the time window under investigation. When the same direction finding line of observation station A corresponds to multiple real targets simultaneously within the time window under investigation (i.e., the direction finding line passes through the positions corresponding to multiple real targets at the same time), the criteria can be further refined by reducing the rectangular window length N or increasing the preset threshold coefficient p_th, or secondary identification can be performed by combining the multi-time correlation between the direction finding line and the corresponding direction finding line of observation station B.

[0032] Step Six: Ghost Node Removal. Remove the intersections identified as ghost nodes in Step Five from... Remove the remaining intersection points from the sequence of intersection points and output the remaining intersection points as the true target locations.

[0033] This invention provides a typical numerical simulation verification of the method. The simulation scenario is set as follows: the coordinates of observation station A... = (0, 0) km; coordinates of observation station B = (50, 0) km; There exists within this space. =5 real radiation source targets, therefore observation station A measured =5 direction finding lines, measured by observation station B =5 direction finding lines; perform pairwise cross-calculation on the 5 direction finding lines of observation station A and the 5 direction finding lines of observation station B, and obtain a total of =25 intersection point position sequence; rectangular window length N=100, initial time =0, threshold coefficient =2.0. According to the method of this embodiment of the invention, it is expected that about 20 (i.e., 25-5=20) intersection points will be identified as ghost points and removed, and the remaining 5 intersection points will be output as the real target positions. The ghost point removal rate is expected to be greater than 90%.

[0034] The beneficial technical effects of the method in this invention can be further illustrated by comparison with existing technologies. Compared to existing methods that employ multi-platform information redundancy for static ghost point removal and virtual track tracking for dynamic ghost point removal (which require more than three observation stations and complex track management), this invention only requires two stations to complete ghost point detection, without the need to establish target tracks and motion models. Compared to methods based on geometric constraints (whose detection performance significantly degrades in dense target scenarios), this invention makes decisions based on the statistical characteristics of intersection velocity sequences, exhibiting low sensitivity to target density and a wider range of applications.

[0035] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for detecting ghost points in multi-target direction finding cross-location based on velocity variance, characterized in that, This is applied to a dual-station direction-finding cross-positioning system consisting of observation station A and observation station B, where the coordinates of observation station A are: The coordinates of observation station B are This includes the following steps: Step 1: Use a rectangular window to observe the direction finding sequence at observation station A. The direction finding line sequence is truncated to obtain the truncated sequence; the direction finding line sequence at observation station B is truncated using the rectangular window described above to obtain the truncated sequence; where i=1,2,…, , Here are the number of direction finding lines measured at observation station A; j=1,2,…, , is the number of direction finding lines measured by observation station B; n is the discrete time sequence number; Step 2: Based on the truncated direction finding line sequence of observation station A and the direction finding line sequence of observation station B from Step 1, perform pairwise intersection positioning calculations for each direction finding line of observation station A and each direction finding line of observation station B to obtain the intersection point position sequence corresponding to each group (i, j). ,common A sequence of intersection points; Step 3: For each intersection point position sequence obtained in Step 2, synthesize the velocity sequence of that intersection point based on the position difference between adjacent time points and with direction signs; Step 4: Calculate the standard deviation of the velocity sequence at each intersection point obtained in Step 3. ; Step 5: For each direction finding line i at observation station A, the intersection of that direction finding line and observation station B... The minimum value among the velocity standard deviations corresponding to all intersection points obtained by the pairwise intersection of the direction finding lines is taken as the reference value; the velocity standard deviation is then used as the reference value. Greater than The intersection point of the reference value is determined to be a ghost point, where For the preset threshold coefficient and Greater than 1.

2. The method according to claim 1, characterized in that, The rectangular window used in step one For a piecewise function, when ≤n≤ When +N-1, =1; when n < or n> When +N-1, =0; where The initial moment of the capture is N, where N is the length of the rectangular window.

3. The method according to claim 1, characterized in that, The sequence of intersection points in step two Solve using the following formula: 。 4. The method according to claim 1, characterized in that, The position difference between adjacent time points in step three is defined by the following formula: 。 5. The method according to claim 4, characterized in that, The synthesis of directional symbols in step three is performed according to the following formula: ； in It is a symbolic function.

6. The method according to claim 1, characterized in that, The preset threshold coefficient The value range is from 1.5 to 5.

7. The method according to claim 1, characterized in that, It also includes step six: The intersection points identified as ghost points in step five are then... Remove the remaining intersection points from the sequence of intersection points and output the remaining intersection points as the true target locations.

8. The method according to claim 2, characterized in that, The window length N of the rectangular window ranges from 50 to 500.

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