Active radar and passive reconnaissance data association method based on amplitude information assistance
By constructing a correlation model between the amplitude measurement of the reconnaissance receiver and the distance of the active radar, and using Kalman filtering and dynamic time warping algorithms, the problem of poor correlation between active radar and reconnaissance receiver data during multi-target tracking was solved, achieving higher correlation accuracy and data fusion effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-03
AI Technical Summary
Existing methods for associating active radar and reconnaissance receiver data based on angle information are ineffective in multi-target tracking, especially when the target azimuth angles are close, leading to decreased association accuracy and data fusion failure.
By constructing a correlation model between the amplitude measurement of the reconnaissance receiver and the distance of the active radar, and using Kalman filtering and dynamic time warping algorithms combined with the Hungarian algorithm to remove outlier data and calculate a multi-feature similarity matrix, accurate correlation between the measurement data of the active radar and the reconnaissance receiver is achieved.
It improves the accuracy of data association in multi-target scenarios, reduces association errors, avoids track splitting and data fusion failure, and enhances the real-time performance and accuracy of multi-target tracking.
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Figure CN121784725A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of multi-sensor data association technology, and particularly relates to a method for associating active radar and passive reconnaissance data based on amplitude information assistance. Background Technology
[0002] As sensor applications become increasingly complex, single-sensor systems can no longer meet the demands of multi-tasking. The measurement information from active radar and reconnaissance receivers is complementary, expanding spatial coverage while reducing data uncertainty and improving the real-time performance and accuracy of data processing. Therefore, their fusion processing has attracted significant attention. Correct correlation between data from different sensors is a prerequisite for information fusion. In the correlation of measurement data from radar and reconnaissance receivers, the radar, as an active sensor, provides three-dimensional (range, azimuth, and elevation) measurement information of the target, while the reconnaissance receiver, as a passive sensor, only provides angular measurement information of the target.
[0003] Therefore, current target trajectory association between active radar and reconnaissance receivers mainly utilizes the azimuth measurement data of the active radar and reconnaissance receiver. Typical trajectory association methods include: Global Nearest Neighbor (GNN), Fuzzy Trajectory Association (FTA), the Jonker Volgenant Castanon (JVC) algorithm, Topological Statistical Distance (TSD), and Variable-Structure Topology (VST). Existing trajectory association methods can achieve good association results under certain conditions, but because the azimuth measurement error of the reconnaissance receiver is relatively large, data association methods that only utilize angle information will face the risk of failure when the azimuth angles of multiple targets are close.
[0004] Therefore, how to improve the correlation accuracy in multi-target and low-detection-precision scenarios, and further enhance the correlation effect of radar and reconnaissance receiver measurement data during multi-target tracking, is a key research focus for scholars in related fields. Summary of the Invention
[0005] The technical problem solved by this invention is as follows: This invention proposes a data association method based on the relationship between amplitude measurement by a reconnaissance receiver and the distance of an active radar. It addresses the issue that existing data association methods based on angle information are ineffective when the azimuth measurement data of the active radar and the reconnaissance receiver are close in multi-target tracking. By analyzing the distance information between the receiver and the electromagnetic target implicit in the amplitude parameter sequence of the signal intercepted by the reconnaissance receiver, and its data association relationship with the target distance information acquired by the active radar, a correlation model between the reconnaissance receiver amplitude and the active radar distance information is constructed, and the correlation between the measurement data of the active radar and the reconnaissance receiver is performed based on this model.
[0006] The technical solution of this invention: a method for correlating active radar and passive reconnaissance data based on amplitude information assistance, the method comprising: Step 1: Acquire active radar measurement data and reconnaissance receiver measurement data; Step 2: Sort the data measured by the active radar and reconnaissance receiver by time, establish a unified time axis, align the data in time, and use cubic spline interpolation to fill in the misaligned time points. Step 3: Remove outliers from the active radar measurement data and the reconnaissance receiver measurement data; Step 4: Use Kalman filtering to smooth the data from the active radar and reconnaissance receiver; Step 5: Calculate the similarity between the data measured by the active radar and the reconnaissance receiver using Dynamic Time Warping (DTW), and construct a multi-feature similarity matrix of the measurement data. Step 6: Obtain the correlation matrix of the data measured by the active radar and the reconnaissance receiver based on the multi-feature similarity matrix.
[0007] Furthermore, Step 1 specifically involves: Active radar measurement data... ,in Indicates the first Active radar measurement data, Indicates the distance between the active radar and the target. Indicates the target azimuth. Indicates the target angular velocity; The reconnaissance receiver measurement data is ,in Indicates the first Section reconnaissance receiver measurement data, This indicates the amplitude of the intercepted enemy radar signal. Indicates the target azimuth. This indicates the rate of change of the amplitude of the intercepted enemy radar signal; and Each contains measurement data for multiple targets.
[0008] Furthermore, In step 3, Using speed constraints: Acceleration constraints: Angular velocity constraints: Active radar measurement data that significantly deviates from the reasonable range will be removed. To set the target's maximum flight speed, To set the target maximum acceleration, To set the target maximum angular velocity; Furthermore, In step 3, Based on the azimuth data measured by the reconnaissance receiver Calculate the rate of change of angle And using the rate of change of angle as a constraint: Remove azimuth angle data from reconnaissance receivers that are significantly outside the reasonable range.
[0009] Furthermore, In step 5, the similarity between the data measured by the active radar and the reconnaissance receiver is calculated using Dynamic Time Warping (DTW), specifically as follows: Calculate the reciprocal of the radar range and take its derivative to obtain the rate of change of the reciprocal of the range. and to Normalization yields Radar measures azimuth angle Normalization yields ; The azimuth angle measured by the reconnaissance receiver in the reconnaissance receiver measurement data Normalization yields Rate of change of intercepted radar signal amplitude Normalization yields ; Calculate the normalized first... The radar measures the azimuth angle and the normalized first azimuth angle. Minimum cumulative distance for measuring azimuth angle with a segment reconnaissance receiver And the normalized first The reciprocal rate of change of the segment distance and the normalized first digit Minimum cumulative range of the rate of change of amplitude of the intercepted radar signal Convert minimum cumulative distance to similarity. , ,in, , These are the weight parameters of the similarity matrix. To adjust the parameters.
[0010] Furthermore, In step 5, a multi-feature similarity matrix of the measurement data is constructed, specifically as follows: Based on similarity and The multi-feature similarity matrix of the measurement data is constructed as follows: .
[0011] Furthermore, Step 6 specifically involves: A multi-feature similarity matrix measures the matching relationship between radar and reconnaissance receiver measurement data; the Hungarian algorithm is used to solve the optimal matching problem between radar and reconnaissance receiver measurements, thereby obtaining the correlation matrix between radar and reconnaissance receiver measurement data; and the data association between radar and reconnaissance receiver measurements is completed based on the correlation matrix.
[0012] This invention discloses a data association method based on the relationship between reconnaissance receiver amplitude measurement and active radar range. This method is applicable to data association scenarios involving multiple equipment and multiple target platforms. By constructing a joint association model of radar range characteristics and electronic reconnaissance receiver pulse amplitude characteristics, it can achieve spatiotemporal synchronization of measurement data from two types of heterogeneous sensors. This solves the association ambiguity problem of heterogeneous sensors in the target azimuth ambiguity area, improves the accuracy of data association when multiple targets have similar azimuth angles, reduces target data association errors, and effectively avoids problems such as track splitting and data fusion failure caused by association errors. Attached Figure Description
[0013] Figure 1 A schematic diagram of the target's flight trajectory; Figure 2 A schematic diagram illustrating the reception of azimuth data by an active radar and the reception of azimuth data by a reconnaissance receiver. Figure 3 This is a schematic diagram of the reciprocal transform rate of active radar range measurement. Figure 4 A schematic diagram illustrating the rate of change of signal amplitude intercepted by a reconnaissance receiver; Figure 5 This is a schematic diagram of the process for correlating active radar and passive reconnaissance data based on amplitude information. Detailed Implementation
[0014] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
[0015] The core technical content of this invention lies in the following: Utilizing the characteristic that the radar signal energy intercepted by the reconnaissance receiver is related to the relative distance between the receiver and the radar radiation source, and that the amplitude variation characteristics of the intercepted signal carry information about the platform's relative motion, a data association method based on the amplitude measurement of the reconnaissance receiver and the distance of the active radar is proposed. This method extracts the time-varying patterns of the intercepted radiation source signal amplitude and the measured target azimuth angle, as well as the changing patterns of the target distance and the measured target azimuth angle acquired by the active radar. Based on this, Kalman filtering is used to reduce the impact of noise and frequency abrupt changes on the association results. Abnormal data is eliminated through spatiotemporal conflict constraints, maneuverability constraints, and angle continuity constraints. A dynamic time warping algorithm is then used to calculate a multi-feature similarity matrix. Finally, the association matrix of the active radar and reconnaissance receiver measurement data is calculated using the Hungarian algorithm to achieve accurate association of heterogeneous data.
[0016] This invention addresses the problem that existing methods for associating active radar and reconnaissance receiver data based on angle information are ineffective when the azimuth measurement data of active radar and reconnaissance receiver are close during multi-target tracking. It proposes a data association method based on the relationship between the amplitude measurement of the reconnaissance receiver and the distance of the active radar.
[0017] This invention constructs a correlation model between reconnaissance receiver amplitude and active radar range information by analyzing the distance information between the receiver and the electromagnetic target implicit in the amplitude parameter sequence of the intercepted signal, and its data correlation with the target range information acquired by active radar. Based on this model, the measurement data of active radar and reconnaissance receiver are correlated. The specific implementation steps are as follows: Step 1: Active radar measurement data is ,in Indicates the first Active radar measurement data, Indicates the distance to the target. Indicates the target azimuth. This represents the target's angular velocity; the data measured by the reconnaissance receiver is... ,in Indicates the first Section reconnaissance receiver measurement data, This indicates the amplitude of the intercepted enemy radar signal. Indicates the target azimuth. This indicates the rate of change of the amplitude of the intercepted enemy radar signal. and Each contains measurement data for multiple targets.
[0018] Step 2: Sort the data measured by the active radar and reconnaissance receiver by time, establish a unified time axis, align the data in time, and use cubic spline interpolation to fill in any misaligned time points. Then, utilize velocity constraints: Acceleration constraints: Angular velocity constraints: Remove active radar measurement data that significantly deviates from the reasonable range; based on the azimuth data measured by the reconnaissance receiver... Calculate the rate of change of angle And using the rate of change of angle as a constraint: Data on azimuth angles measured by reconnaissance receivers that significantly deviate from a reasonable range are excluded. Among them, Indicates the first Active radar measurement data, Indicates the first Section reconnaissance receiver measurement data, To set the target's maximum flight speed, To set the target maximum acceleration, To set the target maximum angular velocity, set it according to the actual situation. This is the maximum distance from the target, calculated based on measurement data.
[0019] Then, Kalman filtering is used to smooth the data from the active radar and reconnaissance receiver. Next, the inverse of the radar range is calculated and differentiated to obtain the rate of change of the inverse of the range. and to Normalization yields Radar measures azimuth angle Normalization yields The azimuth angle measured by the reconnaissance receiver in the reconnaissance receiver measurement data. Normalization yields Rate of change of intercepted radar signal amplitude Normalization yields .
[0020] Step 3: Dynamic Time Warping (DTW) is an algorithm used to measure the similarity between two time series. DTW calculates the similarity between data measured by active radar and reconnaissance receivers. A higher similarity indicates a greater likelihood that the data measured by the active radar and reconnaissance receivers in this segment originates from the same target. The dynamic time warping algorithm is used to calculate the normalized [time series]. The radar measures the azimuth angle and the normalized first azimuth angle. Minimum cumulative distance for measuring azimuth angle with a segment reconnaissance receiver And the normalized first The reciprocal rate of change of the segment distance and the normalized first digit Minimum cumulative range of the rate of change of amplitude of the intercepted radar signal Convert minimum cumulative distance to similarity. , ,in, , These are the weight parameters of the similarity matrix. To adjust the parameters. Based on this, according to similarity and The multi-feature similarity matrix of the measurement data is constructed as follows:
[0021] Step four: The multi-feature similarity matrix measures the matching relationship between radar and reconnaissance receiver measurement data. Based on this, the Hungarian algorithm is used to solve the optimal matching problem between radar and reconnaissance receiver measurements, thereby obtaining the correlation matrix between the two sensors. The data correlation between radar and reconnaissance receiver measurements can then be completed based on the correlation matrix.
[0022] The implementation process of this invention is as follows: To more clearly illustrate the claimed method, this application embodiment uses simulation experiments to explain the process and demonstrate the effects, but this does not limit the scope of this application embodiment. In the experiment, the local machine is used as the origin for modeling, equipped with an active radar and a reconnaissance receiver. Three flying targets are detected within the detection area, moving relative to the local machine and performing maneuvers. The target trajectories are as follows: Figure 1 As shown in Table 1, the target flight parameters are given, and the target maneuver equations are as follows. The initial position of target 1 relative to the aircraft's coordinates is... The starting position of flight target 2 relative to the coordinates of the aircraft is The starting position of flight target 3 relative to the coordinates of the aircraft is .
[0023] Table 1 Target Flight Parameters (a) Flight parameters of target 1
[0024] (b) Flight parameters of target 2
[0025] (c) Flight parameters of target 3
[0026] Target maneuver equations: Target maneuvers include: oblique acceleration, normal speed cruising, large-radius turning, and diving. In oblique acceleration motion, the time-domain model is: for each time step The time-domain model of the oblique acceleration sprint motion is
[0027] in, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, The acceleration of the target will fluctuate due to noise, and the process noise... It follows a pattern with a mean of zero and a variance of . The Gaussian distribution. Indicates the azimuth angle of acceleration. Indicates acceleration pitch angle, Indicates the sampling time interval.
[0028] The target position of the oblique acceleration sprint motion model is:
[0029] in, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates.
[0030] During constant-speed cruise motion, the time-domain model is as follows: for each time step The time-domain model of constant-speed cruise motion is
[0031] in, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, The acceleration of the target will fluctuate due to noise, and the process noise... It follows a pattern with a mean of zero and a variance of . The Gaussian distribution.
[0032] Normal speed cruise position is
[0033] in, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates.
[0034] During large-radius turning motion, for each time step The time-domain model of the large-radius turning motion model is
[0035] in, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, Indicates the initial turning speed of the flying target. For the goal Heading angle at any time.
[0036] The expression is:
[0037] Indicates flight target Initial velocity in the axial direction, Indicates flight target time Initial velocity in the axial direction, The expression is:
[0038] in, The initial heading angle of the flight target, Turning radius of the flight target, process noise It follows a pattern with a mean of zero and a variance of . The Gaussian distribution.
[0039] The target position for large-radius turning motion is
[0040] in, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates.
[0041] During the dive, for each time step The time-domain model of the diving motion model is
[0042] in, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, Indicates flight target time Axial velocity, The acceleration of the target will fluctuate due to noise, and the process noise... It follows a pattern with a mean of zero and a variance of . The Gaussian distribution.
[0043] The target position during the dive is:
[0044] in, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates, Indicates flight target time Axial coordinates.
[0045] To simulate real-world scenarios where target loss occurs due to environmental interference, the active radar and reconnaissance receiver are configured to experience 1-2 random target loss events during each target tracking process. When a target is lost, the active radar or reconnaissance receiver cannot receive valid data until the target is rediscovered. The active radar receives 8 valid azimuth angle data segments (each segment lasting 26 ± 3.2 seconds). Figure 2 As shown, the eight colored lines represent eight segments of active radar azimuth data. The reconnaissance receiver receives a total of seven valid azimuth data segments (each segment lasting 30 ± 4.1 seconds), as follows... Figure 2As shown, the seven colored lines represent seven segments of azimuth angle data received by the reconnaissance receiver. Furthermore, to simulate a realistic battlefield, the aircraft's flight attitude is set to change after 20 seconds of simulation. Specifically, the pitch angle rotates counter-clockwise by 29 degrees, the yaw angle rotates clockwise by 32 degrees, and the roll angle rotates clockwise by 14 degrees. The time taken for the aircraft's flight attitude to change is 20 seconds. The formula for changing the flight attitude is: ,in To change the flight attitude of the target aircraft The original position coordinates at that moment. For the target to change the flight attitude of the machine Position coordinates at that moment For the angular velocity of the yaw angle change, For pitch angle variation angular velocity, The angular velocity varies with the roll angle.
[0046] The target range reciprocal rate of change data obtained by active radar is as follows: Figure 3 As shown, the eight colored lines represent eight segments of target range reciprocal rate of change data obtained by the active radar. The reconnaissance receiver intercepted signal amplitude rate of change data is shown below. Figure 4 As shown in the figure, the seven colored lines represent the rate of change of the intercepted signal amplitude of the seven segments of the reconnaissance receiver. It can be seen from the graph that as the relative distance to the target increases, the intercepted signal power decreases significantly, and when the relative distance to the target decreases, the intercepted signal power increases again.
[0047] Depend on Figure 1-4 It can be seen that although the target's flight distance and maneuvering actions differ, they may still reach similar or even overlapping azimuth angles, leading to azimuth-based data association failure due to feature space overlap. However, the target distance reciprocal rate of change data and the intercepted signal amplitude rate of change data show a significant difference and do not overlap, exhibiting clear data characteristics. Therefore, using the target distance reciprocal rate of change and the intercepted signal amplitude rate of change as auxiliary information in the association process can effectively improve the association accuracy and solve the problem of identifying multiple targets in the same direction.
[0048] Figure 5 This is a principle block diagram of the method described in this invention, which includes: S110. Receive measurement data from active radar and reconnaissance receiver, as follows: Radar measurement data is ,in Indicates the first Active radar measurement data, Indicates the distance to the target. Indicates the target azimuth. This indicates the target angular velocity. Measurement data includes multiple targets.
[0049] The reconnaissance receiver measurement data is ,in Indicates the first Section reconnaissance receiver measurement data, This indicates the amplitude of the intercepted enemy radar signal. Indicates the target azimuth. This indicates the rate of change of the amplitude of the intercepted enemy radar signal. Measurement data includes multiple targets.
[0050] S120. Time alignment and outlier removal to generate time-aligned measurement data. The steps are as follows: Data measured by active radar and reconnaissance receivers are sorted chronologically to establish a unified timeline. The data is then time-aligned, and cubic spline interpolation is used to fill in any misaligned time points. Velocity constraints are also utilized. Acceleration constraints: Angular velocity constraints: Active radar measurement data that significantly deviates from the reasonable range is discarded. Azimuth angle data measured by the reconnaissance receiver is used as the basis for further processing. The rate of change of angle is obtained. And using the rate of change of angle as a constraint: Data on azimuth angles measured by reconnaissance receivers that significantly deviate from a reasonable range are excluded. Among them, Indicates the first Active radar measurement data, Indicates the first Section reconnaissance receiver measurement data, To set the target's maximum flight speed, To set the target maximum acceleration, To set the target maximum angular velocity, adjust the settings according to the actual situation. This is the maximum distance from the target, calculated based on measurement data.
[0051] S130. Kalman filtering is used to smooth the radar and reconnaissance receiver data after outlier removal, reducing noise interference, generating smoother trajectories, and improving the accuracy of subsequent correlation. Based on this, the inverse of the radar range is calculated and its derivative is taken to obtain the rate of change of the inverse of the range. And normalized to obtain Radar measures azimuth. Normalization yields The reconnaissance receiver measures the azimuth angle. Normalization yields Rate of change of intercepted radar signal amplitude Normalization yields .
[0052] S140. Calculation of multi-feature similarity matrix, the specific steps are as follows: The normalized i-th is calculated using the dynamic time warping algorithm. Section radar measures azimuth and the first Minimum cumulative distance for measuring azimuth angle with a segment reconnaissance receiver and the The reciprocal rate of change of the segment distance and the first Minimum cumulative range of the rate of change of amplitude of the intercepted radar signal Convert minimum cumulative distance to similarity. , ,in, , These are the weight parameters of the similarity matrix. To adjust the parameters. Based on this, according to similarity and The multi-feature similarity matrix of the measurement data is constructed as follows:
[0053] S150. A multi-feature similarity matrix measures the matching relationship between radar and reconnaissance receiver measurement data. Based on this, the Hungarian algorithm is used to solve the optimal matching problem between radar and reconnaissance receiver measurements, thereby obtaining the correlation matrix between the two sensors. The data correlation between radar and reconnaissance receiver measurements can then be completed based on the correlation matrix.
[0054] In this experiment, root mean square error (RMSE) and maximum tracking error (MCR) were selected as indicators for trajectory fusion. RMSE is the root mean square value of the trajectory deviation throughout the entire trajectory, comprehensively measuring the overall tracking accuracy. It quantifies the average error over the entire trajectory, avoiding the bias at a single time point from masking the overall performance. It is sensitive to sudden errors and suitable for evaluating comprehensive performance in dynamic environments. Its calculation expression is: ,in, The number of sampling points. It is the azimuth angle. For the first Individual measurement data.
[0055] After target matching of the measurement data, the association results are shown in Table 3. The comparison method is the global nearest neighbor association method. Simulation results show that when the azimuth angles of the two aircraft are similar and the root mean square error is small, the association method based on angle information for active radar and electronic warfare receiver data can still achieve a high association accuracy. However, as the root mean square error increases, the association accuracy based on angle information for active radar and electronic warfare receiver data drops rapidly. Nevertheless, the association method that adds the rate of change of the amplitude of the intercepted signal by the electronic warfare receiver and the inverse law of the distance to the active sensor as auxiliary information can still maintain a high accuracy.
[0056] Table 3. Probability of Correct Association Using the Method Described in This Invention
[0057] Therefore, it can be concluded that when the azimuth angles of two aircraft are similar, simple angle information is insufficient to cope with the situation of multiple targets in close proximity, and cannot accurately associate the two targets, thus affecting the trajectory tracking and the effectiveness of electronic warfare. However, the method proposed in this invention can improve the association accuracy by adding the rate of change of the intercepted signal amplitude of the reconnaissance receiver and the law of change of the inverse of the active radar range as auxiliary information.
[0058] In summary, the method presented in this example can achieve spatiotemporal synchronization of measurement data from two types of heterogeneous sensors by constructing a joint correlation model of radar range characteristics and electronic reconnaissance receiver pulse amplitude characteristics, based on the relationship between reconnaissance receiver amplitude measurement and active radar range. This solves the correlation ambiguity problem of heterogeneous sensors in target azimuth ambiguity areas, improves the accuracy of data correlation when multiple targets have similar azimuth angles, reduces target data correlation errors, and effectively avoids problems such as track splitting and data fusion failure caused by correlation errors.
Claims
1. A method for correlating active radar and passive reconnaissance data based on amplitude information, characterized in that, The method includes: Step 1: Acquire active radar measurement data and reconnaissance receiver measurement data; Step 2: Sort the data measured by the active radar and reconnaissance receiver by time, establish a unified time axis, align the data in time, and use cubic spline interpolation to fill in the misaligned time points. Step 3: Remove outliers from the active radar measurement data and the reconnaissance receiver measurement data; Step 4: Use Kalman filtering to smooth the data from the active radar and reconnaissance receiver; Step 5: Calculate the similarity between the data measured by the active radar and the reconnaissance receiver using Dynamic Time Warping (DTW), and construct a multi-feature similarity matrix of the measurement data. Step 6: Obtain the correlation matrix of the data measured by the active radar and the reconnaissance receiver based on the multi-feature similarity matrix.
2. The method for correlating active radar and passive reconnaissance data based on amplitude information assistance according to claim 1, characterized in that, Step 1 specifically involves: Active radar measurement data... ,in Indicates the first Active radar measurement data, Indicates the distance between the active radar and the target. Indicates the target azimuth. Indicates the target angular velocity; The reconnaissance receiver measurement data is ,in Indicates the first Section reconnaissance receiver measurement data, This indicates the amplitude of the intercepted enemy radar signal. Indicates the target azimuth. This indicates the rate of change of the amplitude of the intercepted enemy radar signal; and Each contains measurement data for multiple targets.
3. The method for correlating active radar and passive reconnaissance data based on amplitude information assistance according to claim 2, characterized in that, In step 3, Using speed constraints: Acceleration constraints: Angular velocity constraints: Active radar measurement data that significantly deviates from the reasonable range will be removed. To set the target's maximum flight speed, To set the target maximum acceleration, To set the target maximum angular velocity.
4. The method for correlating active radar and passive reconnaissance data based on amplitude information assistance according to claim 2, characterized in that, In step 3, Based on the azimuth data measured by the reconnaissance receiver Calculate the rate of change of angle And using the rate of change of angle as a constraint: Remove azimuth angle data from reconnaissance receivers that are significantly outside the reasonable range.
5. The method for correlating active radar and passive reconnaissance data based on amplitude information assistance according to claim 2, characterized in that, In step 5, the similarity between the data measured by the active radar and the reconnaissance receiver is calculated using Dynamic Time Warping (DTW), specifically as follows: Calculate the reciprocal of the radar range and take its derivative to obtain the rate of change of the reciprocal of the range. and to Normalization yields Radar measures azimuth angle Normalization yields ; The azimuth angle measured by the reconnaissance receiver in the reconnaissance receiver measurement data Normalization yields Rate of change of intercepted radar signal amplitude Normalization yields ; Calculate the normalized first... The radar measures the azimuth angle and the normalized first azimuth angle. Minimum cumulative distance for measuring azimuth angle with a segment reconnaissance receiver And the normalized first The reciprocal rate of change of the segment distance and the normalized first digit Minimum cumulative range of the rate of change of amplitude of the intercepted radar signal Convert minimum cumulative distance to similarity. , ,in, , These are the weight parameters of the similarity matrix. To adjust the parameters.
6. The method for correlating active radar and passive reconnaissance data based on amplitude information assistance according to claim 5, characterized in that, In step 5, a multi-feature similarity matrix of the measurement data is constructed, specifically as follows: Based on similarity and The multi-feature similarity matrix of the measurement data is constructed as follows: 。 7. The method for correlating active radar and passive reconnaissance data based on amplitude information assistance according to claim 6, characterized in that, Step 6 specifically involves: A multi-feature similarity matrix measures the matching relationship between radar and reconnaissance receiver measurement data; the Hungarian algorithm is used to solve the optimal matching problem between radar and reconnaissance receiver measurements, thereby obtaining the correlation matrix between radar and reconnaissance receiver measurement data; and the data association between radar and reconnaissance receiver measurements is completed based on the correlation matrix.
8. A system for correlating active radar and passive reconnaissance data based on amplitude information, characterized in that, The system is used to perform the method as described in any one of claims 1-7.