An active true and false target identification method based on target motion analysis

Abstract

The application discloses an active true and false target identification method based on target motion analysis, which comprises the following steps: 1. Multi-cycle detection and data acquisition are carried out on the suspicious target to obtain observation data; 2. TMA geometric positioning is carried out based on the azimuth information; 3. The active ranging and TMA geometric positioning are comprehensively matched; and 4. Multi-cycle detection is performed to complete the true and false target identification. The application has the following advantages: 1. The defects of single or small amount of observation discrimination being easily disturbed are overcome; 2. The identification efficiency and accuracy of the active false target are improved, and the false trajectory is quickly removed under multi-cycle TMA analysis; and 3. A general active true and false target identification framework is provided, which is suitable for various confrontation or monitoring situations and is no longer limited to static analysis of signal characteristics.

Description

Technical Field

[0001] This invention belongs to the fields of underwater acoustics and underwater acoustic signal processing, and in particular relates to an active method for identifying real and false targets based on target motion analysis. Background Technology

[0002] In conventional underwater acoustic detection, active sonar transmits acoustic pulse signals into the water and calculates the distance to the target based on the time delay of the received echo. It can also estimate the target's azimuth by detecting the beam pointing or phase difference of the receiving array. If the frequency shift of the echo relative to the transmitted signal (i.e., the Doppler effect) can be detected, the radial velocity component of the target relative to the sonar platform can be inferred. Range, azimuth, and Doppler shift constitute the main basis for target localization and velocity estimation using active sonar in traditional scenarios.

[0003] However, in typical active sonar applications, systems often assume that the "target echo signal" is real and possesses definite physical characteristics. The primary focus is on overcoming the effects of noise and ocean multipath propagation in the underwater acoustic environment to ensure the accuracy of target detection and tracking. For normal ocean monitoring missions, methods based on single or multiple "range-azimuth-Doppler" measurements are relatively mature and, in most cases, yield acceptable tracking results. If the target's motion pattern is simple (e.g., uniform linear motion), common TMA algorithms can be used to continuously estimate the target's heading and speed.

[0004] Meanwhile, target motion analysis (TMA) is also very common in the field of passive sonar: by combining the azimuth information obtained from multiple measurements with the maneuver trajectory of the user platform, the target's position and velocity are calculated. The steps of the TMA-based active detection process are as follows:

[0005] 1. The detection platform periodically emits active acoustic pulses;

[0006] 2. Based on the echo time, sound speed, and propagation path, the target distance is obtained;

[0007] 3. Obtain the target's azimuth based on the direction of arrival of the received signal on the array or the beam power direction;

[0008] 4. Combining the known position and motion information of our own platform at each transmission and reception time, fit the target trajectory through calculation or filtering algorithms, and update the target position, speed, heading and other elements.

[0009] Because deliberate decoys or spoofing signals can occur in adversarial environments, some existing technologies have begun to focus on the problem of "decoy target identification," and there are currently three approaches:

[0010] 1. Identification based on echo amplitude or energy characteristics:

[0011] This method primarily monitors the amplitude, envelope characteristics, or short-term energy distribution of the echo. If certain echo signals are found to deviate from the general characteristics of a real target (such as stability or multipath mode), they may be identified as false targets. However, some current decoy devices perform precise simulations in terms of intensity and utilize pulse compression or simulated multipath effects, making it difficult to completely eliminate false targets based solely on simple energy characteristics.

[0012] 2. Identification based on echo spectrum or modulation characteristics:

[0013] This method analyzes the detailed temporal and frequency characteristics of the echo, including Doppler shift and possible modulation modes, and compares them with typical target scattering models. If the echo is found to exhibit significant violations of physical laws in terms of phase continuity and Doppler micro-features (such as modulation side frequencies), it is identified as a decoy echo. However, since advanced active decoy equipment often possesses high-precision simulations of Doppler and other characteristics, the success rate of using the spectral comparison method with only a limited number of observation periods remains limited.

[0014] 3. Judgment based on short-time trajectory or single ranging and azimuth matching:

[0015] This method performs simple coordinate transformations on the measured distance and azimuth in a single or a few measurements, attempting to verify whether the target's current observations are consistent with those from previous times. However, if this verification is limited to a few detection cycles and does not fully utilize multiple measurements of the target from different azimuths and times by the platform, then the false target can easily fool the system with "limited forgery accuracy" in a short period of time and may not be detected by this single or short-term method.

[0016] While the aforementioned existing technologies have made some progress in improving the identification rate of active false targets, they still cannot confirm with high confidence that "a suspicious target is indeed a decoy echo" in a short period of time. Their main drawback is that they do not fully utilize the comprehensive information from "multi-period, multi-azimuth, and multi-maneuver" scenarios in Target Motion Analysis (TMA), only performing feature or model comparisons within a small timeframe and observation range. Especially when a false target can disguise itself as a seemingly real range-azimuth-Doppler feature over several observation periods, the above methods may only rely on a single or a few measurements, insufficient to reveal flaws in the overall motion model of the false target.

[0017] From the perspective of active sonar detection and decoy identification, existing technologies generally lack comprehensive analysis of multi-period, multi-azimuth measurement information. Once the adversary uses decoy equipment with fine time delay and Doppler simulation capabilities, discrimination based on short-term or single observations is often insufficient to effectively identify decoys. Specifically, existing technologies mainly suffer from the following defects and shortcomings:

[0018] First, existing methods for identifying false targets mostly focus on "single or limited observation features"—for example, comparing the signal energy and spectral distribution of one or two echoes. Once a false target has successfully simulated or forged these indicators, the detection system will find it difficult to make an accurate judgment in a short time. Such single or short-term detection often lacks sufficient spatiotemporal redundancy, resulting in a high false judgment rate and making it easy to mistake carefully disguised false echoes for real targets.

[0019] Secondly, some traditional methods attempt to observe target echoes multiple times over a longer period, but often employ relatively independent, shallow matching or correlation metrics to determine whether the echoes match the scattering model of the real target. Limited by this, even if the system collects azimuth and range data at multiple times, it fails to perform complete geometric and kinematic verification of the target motion at the overall trajectory level, thus failing to reveal the "self-contradictory" weakness of false targets in multi-period measurements. Even if some algorithms possess certain motion analysis capabilities, they are often limited to the precise positioning of real targets, without focusing on "authenticity" judgment at multiple periods—resulting in insufficient efficiency and accuracy in identifying false targets, and potentially making false echoes more deceptive due to error accumulation. These shortcomings ultimately lead to the following consequences:

[0020] 1. The detection system lacks the means to quickly identify false targets with a certain degree of complex camouflage capabilities;

[0021] 2. While long-term tracking solutions may offer greater accuracy, they are not fast enough to meet the needs of rapid response or monitoring.

[0022] 3. When a false target simulates some physical characteristics, existing methods tend to waste a lot of resources on the false target’s erroneous tracking, while the real threat may be ignored.

[0023] Therefore, this invention aims to systematically incorporate the azimuth and distance information obtained from multiple measurements into a unified kinematic trajectory analysis, based on existing active sonar detection and TMA algorithms. By detecting whether there is a motion model consistent with the real target, it can quickly identify the decoy echo actively simulated by the false target. This is different from the aforementioned conventional short-term or single-shot discrimination schemes and is a more comprehensive and robust technical approach. Summary of the Invention

[0024] To address the shortcomings of existing technologies, the fundamental objectives of this invention are: 1. To overcome the vulnerability of single or limited observations to interference; 2. To improve the efficiency and accuracy of identifying active false targets and to quickly eliminate false trajectories under multi-cycle TMA analysis; 3. To provide a universal framework for identifying active true and false targets, applicable to various adversarial or monitoring scenarios, and no longer limited to static analysis of signal features.

[0025] Based on this, the present invention provides an active real and false target identification method based on target motion analysis, which can significantly enhance the anti-interference capability of active sonar systems in adversarial environments and improve the efficiency of capturing and tracking real targets.

[0026] The technical solution of this invention is as follows:

[0027] An active method for identifying real and false targets based on target motion analysis includes:

[0028] Step 1: Conduct multi-cycle detection and data acquisition on suspicious targets to obtain observations;

[0029] Step 2: Perform TMA geometric positioning based on orientation information;

[0030] Step 3: Achieve integrated matching between active ranging and TMA geometric positioning;

[0031] Step 4: Perform multi-cycle detection to complete the identification of real and fake targets.

[0032] In step one, the detection platform transmits active detection pulses underwater at different times, and simultaneously records the detection platform's pulses at each time t. i coordinates (S) x (t) i ), S y (t) i (), i=1, 2, ..., N, where N is the number of measurements. After each transmission cycle, the echo signal actively detected in that cycle is received. The azimuth β(t) of the suspected target is obtained by preprocessing the echo signal. i ), distance R (t) i ) Observations.

[0033] In step two, starting with the azimuth angle, multiple azimuth measurements are used to obtain the positional changes of the suspicious target and the detection platform, and the coordinates of the suspicious target are calculated.

[0034] In step three, while acquiring azimuth information, active ranging is performed on the target. The ranging residual is calculated based on the target distance obtained from active ranging and the target distance obtained from TMA geometric positioning. Then, the parameter vector to be estimated is set, and the partial derivative with respect to the parameter is calculated for each residual term to form the Jacobian matrix. row i From the initial guess Iterative calculations begin. When the target converges to a set value within a set number of iterations, it is determined to be a real target; otherwise, it is determined to be a suspicious target or directly as a false target.

[0035] In step three, the target distance is obtained using TMA geometric positioning. The expression is:

[0036] ,

[0037] Ranging residual r i The expression is:

[0038] .

[0039] In step three, the expression for the parameter vector p to be estimated is set as follows:

[0040] ,

[0041] Where x0 and y0 are the coordinates of the target's starting position, V T θ T These represent the target speed and heading, respectively.

[0042] The expression for the i-th row of the Jacobian matrix is:

[0043] ,

[0044] The formula for iterative calculation is:

[0045] ,

[0046] The iteration converges to the set value. stop.

[0047] In step four, the azimuth and distance information are repeatedly measured over several consecutive cycles to conduct a comprehensive analysis of suspicious targets. Based on the analysis results, the judgment result of true and false targets is obtained and output.

[0048] The comprehensive analysis in step four includes short-term consistency analysis. The process of short-term consistency analysis is as follows: within the same period, the target can be judged to have motion consistency by comprehensive matching of distance residuals. Within this period, the azimuth and distance information are measured, the standard deviation of the distance residuals is calculated, the distance residuals are normalized and squared to obtain the comprehensive index J, the confidence level is selected and the critical value is obtained by looking up the table, and the real target or the suspicious target is determined according to the magnitude of the comprehensive index J.

[0049] The comprehensive analysis in step four also includes long-term trend observation, and then judging whether it is a real target or a false target based on the number of cumulative cycles and the consistency of short-term consistency analysis results. Specifically, at least 10 consecutive detection cycles are carried out, and the azimuth and distance information is measured more than 20 times in each cycle. Then, a comprehensive judgment is made based on the short-term consistency analysis results of each cycle. For example, a real target is judged to be consistent with the short-term consistency analysis results of 5 adjacent cycles, and a false target is judged to be consistent with the short-term consistency analysis results of 3 adjacent cycles.

[0050] In addressing the challenge of "false targets created by active decoy devices," this invention does not rely solely on simple energy and spectral characteristic analysis of single echo signals. Instead, it employs multi-cycle global verification based on Target Motion Analysis (TMA), proposing a method that utilizes multiple measurements of azimuth and range to comprehensively assess the consistency of the target's motion trajectory. Therefore, this invention offers the following significant advantages:

[0051] 1. Establishing and verifying target trajectories through multi-cycle measurement information can overcome the deceptive nature of false targets that can "pass for the real" in a short period of time. If differentiation is only made in 1-2 measurements, decoy equipment is sufficient to confuse the detection platform in terms of time delay simulation; however, once the measurement cycle is extended, especially with data collected after the detection platform maneuvers, false targets find it difficult to maintain a continuous or reasonable trajectory in the same spatiotemporal coordinate system, thus exposing their false nature more quickly and accurately. This "spatiotemporal joint analysis" greatly improves the accuracy and efficiency of identification.

[0052] 2. The multi-period joint TMA method not only distinguishes between real and false targets but also improves the accuracy of localization and velocity estimation for real targets. If a suspicious echo is indeed generated by a real target, the trajectory fitting and self-consistency interpretation of this invention can provide a more accurate estimate of the target's motion parameters in a shorter time. Conversely, false targets are quickly eliminated or marked, significantly reducing system resource waste and the risk of false tracking. Clearly, this has significant practical implications for situations requiring efficient detection and decision-making in adversarial environments.

[0053] It can be seen that the present invention is more practical and adaptable than the existing technical solutions, especially in dealing with active decoy devices, and can effectively improve the overall anti-interference capability and countermeasure efficiency of active sonar systems. Attached Figure Description

[0054] Figure 1 This is a flowchart of the process of the present invention;

[0055] Figure 2 This is a comparison chart of the results of TMA (Target Position Manipulation) and active range estimation based on simulation data to deceive the target location;

[0056] Figure 3 A deviation diagram of the target TMA and active detection range based on simulation data;

[0057] Figure 4 This is a consistency process diagram of the target TMA and active range motion based on simulation data. Detailed Implementation

[0058] The present invention will be further described below with reference to specific embodiments and accompanying drawings:

[0059] Existing active sonar detection technology faces a key challenge in adversarial environments: after a detection signal is emitted, if the adversary uses a decoy device capable of actively simulating echoes (such as artificially set jamming decoys), this device can rapidly generate and emit false echoes with corresponding time delays, Doppler shifts, and other characteristics based on the received detection pulses. Thus, from the sonar processing system's perspective, this false signal is extremely similar to the echo of the real target. Since traditional methods often rely on only a few single-cycle measurements of "echo intensity and distance" for judgment, if the decoy target is cleverly designed, it can easily interfere with the detection platform through falsified echo characteristics, making it difficult for the detection platform to distinguish the genuine signal from the false one in a short time, and even leading to incorrect tracking or attack decisions.

[0060] The technical problem this invention aims to solve is how to effectively identify and eliminate such "active false targets" in a short time and in complex underwater acoustic environments. This invention needs to overcome the following main difficulties:

[0061] On the one hand, decoy echoes are sufficiently misleading for traditional single physical quantities (such as single distance measurements); on the other hand, when the detection platform collects more spatiotemporal information in multiple transmission and reception cycles, if it lacks a sound motion analysis algorithm and geometric verification mechanism, it may still be unable to quickly identify false targets from seemingly reasonable but actually contradictory echo data.

[0062] Therefore, this invention aims to combine Target Motion Analysis (TMA) with active ranging. By continuously acquiring azimuth and time delay information, it establishes and verifies the self-consistency of the target's trajectory, enabling rapid determination of whether a suspicious echo geometrically and kinematically matches the real target. This fundamentally solves the problem of insufficient accuracy and speed in identifying "actively simulated false targets" in existing technologies. This provides a more robust and operable method for identifying true and false targets in scenarios such as ocean monitoring, signal countermeasures, and underwater safety.

[0063] The purpose of this invention is to utilize azimuth and range data acquired through multi-cycle active measurements, combined with the geometric constraints of Target Motion Analysis (TMA), to quickly verify the consistency of the position and motion model of a suspected target across multiple detection cycles. Specifically, by establishing a unified equation of motion from multiple echo data, if a target is detected as not forming a coherent and reasonable trajectory kinematically, it can be quickly identified as a false target. Simultaneously, if a series of observations show a high degree of fit to the same physical trajectory, it indicates that it is more likely to be a real target. This approach balances speed and accuracy, enabling the detection of flaws in false echoes in a relatively short time.

[0064] In this invention, multi-cycle, multi-azimuth active sonar observations are conducted, comprehensively utilizing azimuth and distance information as well as the known motion trajectory of our platform to verify the global motion consistency of a suspicious target. Once it is determined that a true physical trajectory cannot be formed in multiple measurements, the target can be identified as a false target generated by decoy equipment. The main steps are as follows:

[0065] In this invention, multi-cycle, multi-azimuth active sonar observations are conducted, comprehensively utilizing azimuth and distance information as well as the known motion trajectory of our platform to verify the global motion consistency of a suspicious target. Once it is determined that a true physical trajectory cannot be formed in multiple measurements, the target can be identified as a false target generated by decoy equipment. The main steps are as follows:

[0066] Step 1: Conduct multi-cycle detection and data acquisition on suspicious targets to obtain observations:

[0067] The probe platform moves along a certain trajectory, at different times t1, t2, ..., t N Active detection pulses are emitted underwater, while simultaneously recording the activity of our platform at various times (t). i coordinates (S) x (t) i ), S y (t) i (), i=1,2,…,N, where N is the number of measurements; after each transmission cycle, the echo signal actively detected in that cycle is received, and the azimuth β(t) of the suspected target is obtained through preprocessing of the echo signal. i ), distance R (t) i ) Observations.

[0068] Assuming the array is a uniform linear array and the target is at a distance, the incident wave is approximately a plane wave. The azimuth R(t) of the suspected target can be obtained through preprocessing of the echo signal. i ), distance R (t) i For the observed quantity, the formula is as follows:

[0069] (1)

[0070] (2)

[0071] in, The speed of sound under current hydrological conditions. For the platform in t i The time delay between transmitting a pulse and receiving the echo. The arrival time difference of the echo signal between two adjacent array elements is measured, and d is the spacing between the array elements of the uniform linear array.

[0072] Step 2: Perform TMA geometric positioning based on orientation information:

[0073] Starting with only the azimuth angle, multiple azimuth measurements were used to obtain the positional changes of the suspicious target and our platform, and the coordinates of the suspicious target were initially calculated.

[0074] As a decoy target, its core purpose is to mislead our detection with a pre-set echo signal. To ensure that the signal received by our side is strictly consistent with its preset value, it must guarantee absolutely reliable signal quality. If the decoy target itself is moving, the decoy signal will generate an additional Doppler frequency shift, making it easier to expose its deceptive nature. Therefore, within one detection cycle, the coordinates of the decoy target can be approximated as stationary coordinates (T...). X (t2), T Y (t2)).

[0075] When the positioning coordinates are measured twice, assuming the detection platform measures the orientation β1 of the suspicious target at time t1, position A1 (coordinates (X(t1), Y(t1))), and measures the orientation β2 of the suspicious target at time t2, position A2 (coordinates (X(t2), Y(t2))), then the following conditions are met:

[0076] (3)

[0077] (4)

[0078] By combining the above equations, we can obtain the target coordinates (T). X T Y ).

[0079] When the positioning coordinates are measured n times (n≥3), the location β1 of the suspicious target is obtained by the detection platform at time t1, position A1 (coordinates (X(t1), Y(t1))), and the location β2 of the suspicious target is obtained at time t2, position A2 (coordinates (X(t2), Y(t2))). This process continues until the location β1 is reached. n Time, A n Position (coordinates (X (t)) n ), Y(t) n The location β of the suspected target was measured at time )))n Then the target coordinates (T) X T Y )satisfy:

[0080] , (5)

[0081] in, , , , , .

[0082] Step 3: Achieve integrated matching of active ranging and TMA geometric positioning:

[0083] While acquiring orientation information, active ranging is performed on the target, and the target distance is obtained using TMA geometric positioning. ,conform to:

[0084] (6)

[0085] The values ​​obtained from the i-th active ranging Target distance obtained with TMA geometric positioning Calculate the distance measurement residual:

[0086] (7)

[0087] When the detected target is a decoy, considering the Doppler shift phenomenon, the target's position should remain stationary. Assume the target's initial coordinates are x0, y0, and its velocity is V. T With heading θ T The value is 0; when the detected target is a real target, it should be assumed that the target is moving in uniform linear motion within one detection cycle, and the equation of motion is as follows:

[0088] (8)

[0089] (9)

[0090] Then set the vector of parameters to be estimated:

[0091] (10)

[0092] Where x0 and y0 are the coordinates of the target's starting position, V T θ T These represent the target speed and heading, respectively.

[0093] Calculate the partial derivatives with respect to the parameters for each residual term to construct the Jacobian matrix. Its i-th line is:

[0094] (11)

[0095] From the initial guess Begin by iterating as follows until convergence:

[0096] (12)

[0097] The iteration converges to the set value. Stop, and Less than the preset threshold 10e 3 If the calculation stops at a certain time, and ||·|2 represents the L2 norm calculation, then the azimuth and distance measurement data are considered to be consistent, and the target is a real target. If the calculation stops after the set number of iterations or the residual is too large, it indicates that the observed distance is physically contradictory and the motion consistency is low, and the target is judged to be a suspicious target.

[0098] Step 4: Perform long-term, multi-cycle detection to complete the identification of real and fake targets:

[0099] To improve the robustness against errors and interference, this invention does not rely on a single or a few observations, but emphasizes measuring azimuth, distance, and other information more than 20 times in each of at least 10 consecutive cycles, and performing the following comprehensive analysis on suspicious targets:

[0100] (1) Short-term consistency check: Within the same period, motion consistency is determined by comprehensive matching of distance residuals. Assume that within this period, information such as azimuth and distance is measured N times, r i Let be the distance residual of the i-th measurement, and let δ be the standard deviation of the distance residuals of the Nth measurement. R Normalize and sum the squared distance residuals from all N measurements to obtain the comprehensive index:

[0101] (13)

[0102] The rules for determining whether a target is real or suspicious are as follows:

[0103] (14)

[0104] Where N is the number of measurements, k is the number of parameters to be estimated, and Nk is the degree of freedom. Under the assumption of "true target", k = 4 (x0, y0, V). T θ T Then the normalized sum of squared residuals J approximately follows an x-degree-of-freedom system with N-4 degrees of freedom. 2 The distribution is determined by choosing a confidence level of 1-α, where α=0.05, and then obtaining x from the chi-square distribution table. 2 critical value .

[0105] (2) Long-term trend observation: After accumulating multiple cycles, if the target is a real target, its motion trajectory can achieve high motion consistency under a relatively stable physical model, such as uniform linear motion; while the false target will have difficulty maintaining coordinate continuity or velocity smoothness globally as the deception device periodically changes parameters such as time delay and Doppler, which will eventually lead to jumps or "false drifts" in multiple calculations and fail to meet the motion model.

[0106] (3) Judgment output: Once a target is judged as a suspicious target within 3 adjacent periods, the observation data of the target is deemed to be inconsistent with the physical trajectory, and a "false target" label is output; if a target is judged as a real target within 5 adjacent periods, the "real target" judgment is maintained, and subsequent countermeasures are executed.

[0107] In summary, the "Active Real / False Target Identification Method Based on Target Motion Analysis" provided by this invention fundamentally overcomes the limitations of relying solely on single echo characteristics or short-term observations to identify false targets. It aims to rapidly determine the authenticity of targets by comprehensively considering multi-cycle azimuth and distance, using kinematic and geometric self-consistency as the criterion. The main innovation of this invention lies in:

[0108] 1. Multi-period active measurement and TMA fusion discrimination mechanism: Traditional schemes mostly only identify targets based on single or limited observations. This invention, however, acquires azimuth, distance, and other information across multiple detection periods, and checks the target trajectory through complete TMA calculation or fitting. If a target cannot form a uniform and smooth trajectory kinematically, it is quickly identified as a false target. This method based on "multi-period global consistency discrimination" is the core innovation of this invention.

[0109] 2. Logic for identifying false echoes using active decoy equipment: The decoy equipment simulates the time delay of the received detection pulse before transmitting the false target. The method proposed in this invention, "examining the inconsistencies between echoes over multiple cycles," can quickly reveal the essential defect of false targets in maintaining continuity in global kinematics. Specifically, if the distances corresponding to multiple measurements cannot be fitted to the same motion model, indicating inconsistency in motion, it can be inferred that the target is false.

[0110] It should be noted that the above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Equivalent modifications made based on the above embodiments are all within the scope of protection of the present invention.

Claims

1. An active method for identifying true and false targets based on target motion analysis, characterized in that, include: Step 1: Conduct multi-cycle detection and data acquisition on suspicious targets to obtain observations; Step 2: Perform TMA geometric positioning based on orientation information; Step 3: Achieve integrated matching between active ranging and TMA geometric positioning; Step 4: Perform multi-cycle detection to complete the identification of real and fake targets; In step three, while acquiring azimuth information, active ranging is performed on the target. The ranging residual is calculated based on the target distance obtained from active ranging and the target distance obtained from TMA geometric positioning. Then, the parameter vector to be estimated is set, and the partial derivative with respect to the parameter is calculated for each residual term to form the Jacobian matrix. row i From the initial guess Iterative calculations begin. When the target converges to a set value within a set number of iterations, it is determined to be a real target; otherwise, it is determined to be a suspicious target or directly as a false target.

2. The active real / false target identification method based on target motion analysis according to claim 1, characterized in that: In step one, the detection platform transmits active detection pulses underwater at different times, and simultaneously records the detection platform's pulses at each time t. i The coordinates are determined, and after each transmission cycle, the echo signal actively detected in that cycle is received. The azimuth β(t) of the suspected target is obtained through preprocessing of the echo signal. i ), distance R (t) i ) Observations.

3. The active real / false target identification method based on target motion analysis according to claim 2, characterized in that: In step two, starting with the azimuth angle, multiple azimuth measurements are used to obtain the positional changes of the suspicious target and the detection platform, and the coordinates of the suspicious target are calculated. Specifically: When performing two measurements to determine the positioning coordinates, the location β1 of the suspected target measured by the detection platform at time t1 and position A1, and the location β2 of the suspected target measured at time t2 and position A2 are obtained. The coordinates of the detection platform at position A1 are (X(t1), Y(t1)) and the coordinates at position A2 are (X(t2), Y(t2)). Within one detection cycle, the coordinates of the suspected target are calculated using stationary coordinates, i.e., T X (t2) = T X (t1), T Y (t2) = T Y (t1), then the following is satisfied: ， ， By combining the above equations, we can obtain the coordinates (T) of the suspected target. X T Y ).

4. The active real / false target identification method based on target motion analysis according to claim 3, characterized in that: In step two, when the positioning coordinates are measured more than three times, the detection platform is obtained at t. n Time, A n The location of the suspected target β was measured at the time of positioning. n , n≥3, A n The position coordinates are (X (t) n ), Y(t) n Then the target coordinates (T) X T Y )satisfy: ， ， in, , , , , .

5. The active real / false target identification method based on target motion analysis according to claim 4, characterized in that: In step three, the target distance is obtained using TMA geometric positioning. The expression is: ， Distance residual r i The expression is: 。 6. The active real / false target identification method based on target motion analysis according to claim 5, characterized in that: In step three, the expression for the parameter vector p to be estimated is set as follows: ， Where x0 and y0 are the coordinates of the target's starting position, V T θ T These represent the target speed and heading, respectively. The expression for the i-th row of the Jacobian matrix is: ， The formula for iterative calculation is: ， The iteration converges to the set value. stop.

7. The active real / false target identification method based on target motion analysis according to claim 1, characterized in that: In step four, the azimuth and distance information are repeatedly measured over several consecutive cycles to conduct a comprehensive analysis of suspicious targets. Based on the analysis results, the judgment result of true and false targets is obtained and output.

8. The active real / false target identification method based on target motion analysis according to claim 7, characterized in that: The comprehensive analysis in step four includes short-term consistency analysis. The process of short-term consistency analysis is as follows: within the same period, the target can be judged to have motion consistency by comprehensive matching of distance residuals. Within this period, the azimuth and distance information are measured, the standard deviation of the distance residuals is calculated, the distance residuals are normalized and squared to obtain the comprehensive index J, the confidence level is selected and the critical value is obtained by looking up the table, and the real target or the suspicious target is determined according to the magnitude of the comprehensive index J.

9. The active real / false target identification method based on target motion analysis according to claim 8, characterized in that: The comprehensive analysis in step four also includes long-term trend observation, and then judging whether it is a real target or a false target based on the number of cumulative cycles and the consistency of short-term consistency analysis results. Specifically, at least 10 consecutive detection cycles are carried out, and the azimuth and distance information is measured more than 20 times in each cycle. Then, a comprehensive judgment is made based on the short-term consistency analysis results of each cycle.

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