Anti-dense range decoy jamming method based on radar-infrared multi-target correlation

Abstract

This invention discloses a method for resisting dense range decoy interference based on radar-infrared multi-target correlation, belonging to the field of anti-jamming. In the field of anti-ship missile strikes against sea targets, range decoy interference is a common type of interference. Traditional anti-jamming methods are cumbersome, have poor real-time performance, and are difficult to implement in engineering. Furthermore, traditional methods only conduct anti-jamming simulations on a single target during tracking, neglecting the problem of anti-jamming under conditions where multiple targets exist simultaneously. To address this issue, this invention provides a method for resisting dense range decoy interference based on radar-infrared multi-target correlation. First, it uses radar-infrared angle information to construct a test statistic to remove dense range decoy interference. Then, it performs adaptive gate point-to-track correlation, Kalman filter tracking, and track correlation operations on the target tracks after interference removal, realizing a method for resisting dense range decoy interference under conditions of multiple targets and multiple interferences.

Description

Technical Field

[0001] This invention relates to an anti-jamming method, and more particularly to a method for dense-range false target jamming of a radar-infrared composite seeker under conditions of multiple targets. Background Technology

[0002] After intercepting the radar echo, the jammer will... (The sentence is incomplete and requires more context to translate accurately.) Each time the echo is transmitted, the radar detects one more false target with each relay of the radar signal. Because the time delay generated by the jammer is random and variable, the range of the generated false target interference is uncertain, and the number is large. Therefore, the jamming capability of range-multiple false target jamming is very strong. Range-multiple false target jamming can not only produce deception jamming but also suppression jamming. When there are many false targets, the jamming signals superimpose each other, and the radar cannot accurately distinguish the real target from the numerous false targets, thus forming suppression jamming. This makes it difficult for the radar to distinguish the real target, and the missile loses its strike capability.

[0003] Traditional anti-jamming identification algorithms are cumbersome, lack real-time performance, and are difficult to implement in engineering. Furthermore, traditional methods only conduct anti-jamming simulations on a single target during tracking, neglecting the issue of anti-jamming when multiple targets are present simultaneously. This paper addresses practical engineering needs by leveraging the immunity of infrared sensors to distance-based decoys, combining radar-infrared angle information to remove interference, and employing unscented Kalman filtering to ensure real-time missile tracking of the target. This provides extremely important reference value for precise target engagement in complex environments. Summary of the Invention

[0004] The purpose of this invention is to propose a method for resisting dense range false target interference based on radar-infrared multi-target correlation, thereby solving the problems of existing anti-jamming methods that do not consider the simultaneous existence of multiple targets and have complex algorithms that are difficult to implement in engineering.

[0005] The relative positions of the two sensors to the real target and the jamming platform in this embodiment are as follows: Figure 2 As shown, the sensor is located at (0km, 0km, 1km), the radar ranging error is 50 meters, the angle measurement error is 0.2 degrees, the infrared angle measurement error is 0.02 degrees, and the positions and velocities of the real target and the jamming platform are shown in Table 1.

[0006]

[0007] The technical solution of the anti-dense range false target interference method based on radar-infrared multi-target correlation proposed in this invention includes the following steps:

[0008] Step 1: Construct the test statistic; the only difference between the decoy target and the jamming platform is their distance, so their azimuth angles are the same. Since the azimuth angles of the jamming platform and the real target are different, and the decoy target only affects the radar, the azimuth information received by the radar and infrared receivers is combined to construct the test statistic:

[0009]

[0010] in and These are the azimuth angle information of the i-th target at time l of the radar sensor and the j-th target at time l of the infrared sensor, respectively. and These are the azimuth measurement errors of the radar and infrared sensors, respectively.

[0011] Step 2: Set the chi-square test threshold based on the constructed test statistic to distinguish between the real target and interference at the current moment.

[0012]

[0013] As shown in Table 2, there are two targets in the simulation experiment. In order to identify the two targets from the interference and minimize the probability of misjudgment, n=3 in the attached figure of the abstract. If only three or fewer values ​​satisfy the above formula, it is proven that the radar target that is currently constructing the test statistic is the real target. The minimum value that satisfies the above formula is taken as the real target trace. Otherwise, it is proven that the current target is interference. The maximum value among all traces is taken as the real target trace.

[0014] Step 3: Associate multiple real target points with multiple flight paths. This invention uses a nearest neighbor point association algorithm based on adaptive gates.

[0015] (1) First, a tracking gate is set. The points obtained from the initial screening by the tracking gate become candidate points to limit the number of targets participating in the relevant discrimination. The target measurement points must satisfy the following formula to be judged. These are candidate points.

[0016]

[0017] in, This is the predicted value at time k+1. To predict the covariance matrix, To test the threshold, W follows a chi-square distribution with degrees of freedom equal to the dimension of the prediction vector.

[0018] (2) If only one measurement falls within the relevant gate, that measurement can be directly used for track update; if more than one point falls within the gate of the tracked target, the candidate point with the smallest statistical distance is taken as the target associated point. That is, in the nearest neighbor standard filter, the measurement that minimizes the innovation weighted norm is used to update the target state in the filter. The innovation weighted norm is:

[0019]

[0020] Due to inherent measurement errors in the sensor, there may be situations where, after determining the decision threshold, no measurement value falls within the gate. Therefore, an adaptive tracking gate was designed to address this issue and achieve more stable point navigation correlation. The specific operation involves appropriately adjusting the salience level to enlarge the tracking gate. If no measurement value falls within the gate after one enlargement, the above steps are repeated. If no measurement value falls within the gate even after enlarging it to its limit, the predicted value at this moment is used as the new measurement and updated.

[0021] Step 4: Unscented Kalman Filter Initialization. After associating target points with flight paths in Step 3, the relationship between multiple predicted target points and multiple flight paths is determined. However, the obtained flight paths will deviate significantly from the actual target flight paths. Therefore, it is necessary to use the information from both to process the data through a Kalman filter to obtain the target position and state information, thereby reducing tracking errors.

[0022] (1) The target state equation is:

[0023]

[0024] in, Here is the state transition matrix. This is process noise.

[0025] (2) The observation equation is:

[0026]

[0027] in For measuring noise.

[0028] (3) The initial covariance matrix of the target can be expressed as:

[0029]

[0030] in, , , Furthermore, each element in the matrix is ​​a block matrix, which can be represented as:

[0031]

[0032] in It is derived from the radar error covariance matrix and the coordinate transformation matrix, and its expression is as follows:

[0033]

[0034] It is the radar measurement error covariance matrix. It is the coordinate transformation matrix from the polar coordinate system to the ECEF coordinate system.

[0035] Step 5: System state prediction.

[0036] (1) Construct the sigma point set using the initialized target state vector and covariance matrix:

[0037]

[0038] (2) Calculate the weights corresponding to the sampling points:

[0039]

[0040] in The weights corresponding to the mean, The weights corresponding to the covariance, This is the scaling parameter, used to control the error, and its expression is as follows:

[0041]

[0042] Used to determine the distribution of sampling points This is the scale parameter.

[0043] (3) Based on the above sigma points, the following one-step prediction is obtained:

[0044]

[0045] Where F is the state transition matrix and B is the radar coordinate at time k+1.

[0046] (4) Obtain the state prediction estimate and prediction covariance:

[0047]

[0048] in, , It is the process noise covariance matrix.

[0049] Step 6: Calculate the measurement prediction model.

[0050] (1) Based on the obtained and Find the new sigma point set:

[0051]

[0052] (2) Measurement prediction can be obtained from the measurement equation, which can be expressed as:

[0053]

[0054] (3) Measurement prediction is obtained through weighted summation:

[0055]

[0056] in To measure the noise covariance, , .

[0057] Step 7: Status update.

[0058] The Kalman gain matrix is:

[0059]

[0060] The state vector and covariance matrix are updated as follows:

[0061]

[0062] Steps 4 to 7 constitute the iterative model of the unscented Kalman filter to achieve accurate tracking of the target.

[0063] Step 8: Associate the tracked multiple target tracks with the angle information of multiple targets obtained from the infrared sensor to ensure that the track information of the two sensors belongs to the same target.

[0064] (1) Construct the test statistic:

[0065]

[0066]

[0067]

[0068] in and These are the azimuth angle information of the i-th target at time l of the radar sensor and the j-th target at time l of the infrared sensor, respectively. and These are the azimuth measurement errors of the radar and infrared sensors, respectively. and These are the elevation angle information of the i-th target at time l using the radar sensor and the j-th target at time l using the infrared sensor, respectively. and These are the elevation angle measurement errors of the radar and infrared sensors, respectively; It is the constructed test statistic.

[0069] (2) Set the chi-square test threshold according to the constructed test statistic as follows:

[0070]

[0071] Now take two positive integers and ,in , , This is the total number of correlation tests. This refers to the number of successful associations in this invention. , If in In the secondary correlation test The condition is satisfied once.

[0072]

[0073] Then, it is determined that the i-th target of the radar sensor is associated with the j-th target of the infrared sensor. If more than two targets of the infrared sensor are determined to be associated with the radar track i, the target with the smallest test statistic is selected as the associated track. Once the track is successfully associated, no further association test is performed.

[0074] Beneficial effects:

[0075] (1) Based on the characteristics of range false targets, this invention uses radar / infrared angle information to construct a test statistic, which can effectively remove range false target interference. The algorithm is simple and effective and is applicable to engineering practice.

[0076] (2) This invention simulates multi-target scenarios and uses adaptive gate point navigation association algorithm, unscented Kalman filter tracking algorithm and K-nearest neighbor trajectory association algorithm to achieve accurate tracking and association of multiple target trajectories of radar / infrared sensors.

[0077] This invention can effectively remove false targets generated by jamming platforms and accurately track multiple real targets, significantly reducing computation time. It is easy to implement in engineering and has widespread application value. It can be applied to the field of radar-infrared composite seeker anti-jamming, enabling precise strikes against targets under jamming conditions. Attached Figure Description

[0078] Figure 1 The accompanying drawings are for the abstract of this invention;

[0079] Figure 2 The positional relationship between the target of this invention and the interference platform;

[0080] Figure 3 This is a radar measurement trajectory diagram of the true and false targets in this invention;

[0081] Figure 4 This is an overall image of the tracking trajectory after interference removal according to the present invention;

[0082] Figure 5 This is an enlarged view of the tracking trajectory after interference removal in this invention;

[0083] Figure 6 The target location estimation error of this invention;

[0084] Figure 7 This refers to the target velocity estimation error of this invention. Detailed Implementation

[0085] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] In the description of this invention, it should be understood that the terms "upper," "middle," "outer," "inner," etc., which indicate orientation or positional relationship, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting this invention.

[0087] (1) Set simulation parameters, where the radar and infrared sensors are in the same position. The radar can measure range, azimuth, and elevation information, while the infrared sensors can measure azimuth and elevation information.

[0088] (2) Set the parameters for the target and the interference platform as shown in Table 1. Simulate two real trajectories. Each real trajectory has an interference platform nearby. Each interference platform releases 8 false target interferences at distances randomly generated between 100-1000m. Figure 3 As shown;

[0089] (3) Sample every 5 seconds and construct a test statistic using radar-infrared information from the sampling points;

[0090] (4) Use the chi-square test to make a real-time judgment on the constructed test statistic using formula (2);

[0091] (5) Save the sampling point information of the target, and perform adaptive gate nearest neighbor correlation on the tracks of the two real targets and the points obtained from each sampling;

[0092] (6) Use steps 4-7 to perform Kalman filtering tracking on the two targets, such as... Figure 4 As shown;

[0093] (7) The track information of multiple targets after tracking is associated with the angle information of multiple targets obtained by the infrared sensor to ensure that the track information of the two sensors belongs to the same target.

[0094] Through the above steps, this invention obtains the true target tracking trajectory after removing interference. Figure 5 , Figure 6 This invention addresses the tracking position estimation error and velocity estimation error.

[0095] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0096] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for resisting dense range false target interference based on radar-infrared multi-target correlation, characterized in that: Includes the following steps: Step 1: Construct a test statistic by combining the azimuth information received from radar and infrared sensors. Step 2: Set the chi-square test threshold based on the constructed test statistic to distinguish between the real target and interference at the current moment; Step 3: Associate multiple real target points with multiple flight paths using a nearest neighbor point association algorithm based on adaptive gates; Step 4: Unscented Kalman filter initialization; After associating the target points with the flight paths in Step 3, the Kalman filter is used to process the predicted target points and multiple flight paths to obtain the target position and status information, thereby reducing tracking errors; Step 5: System state prediction; Step 6: Calculate the measurement prediction model; Step 7: State update. Steps 4 to 7 constitute the iterative model of the unscented Kalman filter, realizing target tracking. Step 8: Associate the tracked target tracks with the angle information of multiple targets obtained from the infrared sensor to ensure that the track information from the two sensors belongs to the same target.

2. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 1, characterized in that: Step 1 specifically involves: constructing a test statistic by comprehensively utilizing the azimuth information received from radar and infrared. ； in and These are the azimuth angle information of the i-th target at time l of the radar sensor and the j-th target at time l of the infrared sensor, respectively. and These are the azimuth measurement errors of the radar and infrared sensors, respectively.

3. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 1, characterized in that: Step 2 specifically involves setting a chi-square test threshold based on the constructed test statistic to distinguish between the real target and interference at the current moment. ； The simulation experiment constructed two targets and two jamming platforms. The jamming platforms released eight false targets at different ranges. In order to identify the two targets from the jamming and minimize the probability of misjudgment, if only three or fewer values ​​satisfy the above formula, it is proven that the radar target currently constructing the test statistic is the real target, and the minimum value satisfying the above formula is taken as the real target trace; otherwise, it is proven that the current target is jamming, and the maximum value among all traces is taken as the real target trace.

4. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 1, characterized in that: Step 3 specifically involves: (1) Set a tracking gate, and the points obtained by the initial screening by the tracking gate become candidate points to limit the number of targets participating in the relevant discrimination; The target measurement point must satisfy the following formula to be judged. Candidate points; ； in, This is the predicted value at time k+1. To predict the covariance matrix, To test the threshold, W follows a chi-square distribution with degrees of freedom equal to the dimension of the prediction vector; (2) If there is only one measurement value that falls into the relevant gate, the measurement value is directly used for track update; if there is more than one point that falls into the gate of the tracked target, the candidate point with the smallest statistical distance is taken as the target associated point, that is, in the nearest neighbor standard filter, the measurement that makes the innovation weighted norm reach a minimum is used to update the target state in the filter. The new interest weighted norm is: ； Point navigation association is performed using an adaptive tracking gate; Adjust the significance level to enlarge the tracking gate. If no measurement value falls into the gate after one enlargement, repeat the above steps. If no measurement value falls into the gate after enlarging the gate to its limit, update the predicted value at this moment as a new measurement.

5. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 4, characterized in that: Step 4 specifically involves: Unscented Kalman filter initialization; (1) The target state equation is: ； in, Here is the state transition matrix. This is process noise; (2) The observation equation is: ； in For measuring noise; (3) The initial covariance matrix of the target is: ； in, , , And each element in the matrix is ​​a block matrix: ； in, It is derived from the radar error covariance matrix and the coordinate transformation matrix. ； in, It is the radar measurement error covariance matrix. It is the coordinate transformation matrix from the polar coordinate system to the ECEF coordinate system.

6. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 5, characterized in that: Step 5 specifically involves: System state prediction; (1) Construct the sigma point set using the initialized target state vector and covariance matrix: ； (2) Calculate the weights corresponding to the sampling points: ； in, The weights corresponding to the mean, The weights corresponding to the covariance, This is the scaling parameter, used to control the error, and its expression is as follows: ； in, Used to determine the distribution of sampling points For scale parameters; (3) Based on the above sigma points, the following one-step prediction is obtained: ； Where F is the state transition matrix and B is the radar coordinate at time k+1; (4) Obtain the state prediction estimate and prediction covariance: ； in, , It is the process noise covariance matrix.

7. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 5, characterized in that: Step 6 specifically involves: (1) Based on the obtained and Find the new sigma point set: ； (2) Measurement predictions are obtained based on the measurement equation, which is expressed as: ； (3) Measurement prediction is obtained through weighted summation: ； in, To measure the noise covariance, , .

8. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 7, characterized in that: Step 7 specifically involves: The Kalman gain matrix is: ； The state vector and covariance matrix are updated as follows: 。 9. The method for resisting dense range false target interference based on radar-infrared multi-target correlation according to claim 8, characterized in that: Step 8 specifically involves: (1) Construct the test statistic: ； ； ； in, and These are the azimuth angle information of the i-th target at time l of the radar sensor and the j-th target at time l of the infrared sensor, respectively. and These are the azimuth measurement errors of the radar and infrared sensors, respectively. and These are the elevation angle information of the i-th target at time l using the radar sensor and the j-th target at time l using the infrared sensor, respectively. and These are the elevation angle measurement errors of the radar and infrared sensors, respectively; It is the constructed test statistic; (2) Set the chi-square test threshold according to the constructed test statistic as follows: ； Take two positive integers and ,in , , This is the total number of correlation tests. It represents the number of successful associations. , If in In the secondary correlation test The condition is satisfied once: ； Then, it is determined that the i-th target of the radar sensor is associated with the j-th target of the infrared sensor. If more than two targets of the infrared sensor are determined to be associated with the radar track i, the target with the smallest test statistic is selected as the associated track. Once the track is successfully associated, no further association test is performed.

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