Plot-level cooperative anti-interference method based on multi-source information space-time consistency judgment

Abstract

The invention provides a trace point level cooperative anti-interference method based on multi-source information space-time consistency judgment, which is applied to a multi-source information fusion system and comprises the implementation steps of receiving n radar trace point data and unifying trace point coordinates; performing common-view-area judgment on each trace point; performing time window judgment on the trace point; carrying out homologous judgment on plots of a plurality of different radars; the plots subjected to plot arrangement are sent to a composite tracking module, and a target tracking track is formed through multi-radar data association filtering; and judging whether the current trace point is a target source of the existing track, if the current trace point is the target of the target track, performing trace point arrangement, and otherwise, performing interference point elimination. Through the method, on the basis of not sacrificing the radar detection performance, the cooperative anti-interference problem in a multi-radar information source fusion system is solved, a more stable anti-interference effect is provided, and the target tracking start of the radar is more stable and reliable.

Description

Technical Field

[0001] The present invention relates to data processing technology in the field of radar technology, and in particular to collaborative anti-interference technology. Background Art

[0002] With the birth of radar technology, radar jamming and anti-jamming technologies have been developing in the game with each other. The endless jamming methods have promoted the improvement of radar anti-jamming technology, and the implementation of various anti-jamming measures has stimulated the innovation of radar jamming technology. In the face of increasingly complex electromagnetic environments, the strength of modern radar anti-jamming capabilities even directly affects the trend and final outcome of the war. Traditional single radars have relatively limited target information due to the single frequency band data. Most of the existing fusion system anti-jamming measures are based on single radar signal-level anti-jamming, which is very sensitive to information such as the waveform and spectrum characteristics of the interference. In the case of constantly changing interference patterns, the anti-jamming effect is usually unstable. At the same time, radars usually need to sacrifice more resources to achieve relatively ideal results in order to resist interference. However, modern fusion systems can obtain rich multi-source sensor space, time, and frequency information, providing a new way for fusion systems to resist active interference. By fusion analysis of target-related data under different radars, the difference between true and false targets can be found, and interference can be effectively identified or suppressed, which greatly improves the survivability of multi-source information fusion systems under active interference.

[0003] The existing anti-interference measures of radar fusion systems face the following main problems: 1) It requires sacrificing a large amount of radar resources and certain detection performance; 2) It is sensitive to interference patterns and the anti-interference effect is unstable. Summary of the invention

[0004] In view of the shortcomings of the existing anti-interference methods of radar fusion systems, the present invention provides a point-level collaborative anti-interference method based on spatiotemporal consistency judgment of multi-source information. Without sacrificing radar detection resources, it solves the collaborative anti-interference problem in the multi-radar information source fusion system and provides a more stable anti-interference effect.

[0005] In order to achieve the above technical objectives, the present invention proposes a point-level collaborative anti-interference method based on spatiotemporal consistency judgment of multi-source information, the process includes:

[0006] Step 1: Receive n radar trace data and transform the data of each radar into a unified coordinate system according to the position of each sensor;

[0007] Step 2: Make a common view zone judgment for each point trace: According to the position of the current point trace, determine whether it is in the scanning area of ​​multiple radars. If the current point trace is in the scanning range of two or more radars, proceed to the next step, otherwise the point trace is regarded as a non-interference point for point trace arrangement;

[0008] Step 3: Time window judgment of the point trace: Calculate the time-related regional window according to the timestamp of the current point trace and the sector data update time of each radar, and then judge whether there is a point trace for each radar in the time-related regional window. If there is a point trace for other radars, proceed to the next step, otherwise jump to step 6;

[0009] Step 4: Determine the homology of the traces from multiple different radars: Take the timestamp of each trace from the traces of different radars, and use the position and timestamp of these traces to calculate the motion model in the sense of minimum root mean square error. Calculate the position deviation between the current trace and the motion model, and determine whether the position deviation is greater than the threshold. If the deviation is greater than the threshold, the trace is a non-homologous trace and proceed to the next step. Otherwise, the trace is a homologous trace and the trace is regarded as a non-interference point for trace arrangement.

[0010] Step 5: Send the arranged points to the composite tracking module, which forms a target tracking track through multi-radar data association filtering;

[0011] Step 6: Determine whether the current point track is the target source of the existing track: The real-time extrapolation module extrapolates the target track to the current moment based on the motion parameters of the target tracking track and the timestamp of the current point track, and determines whether the current point track is within the existing target track gate based on the extrapolated position. If the current point track is within the target track gate, it is regarded as a non-interference point for point track arrangement, otherwise it is regarded as an interference point and is removed.

[0012] Preferably, the threshold in step 4 is selected to be 3 times the measurement error of the sensor corresponding to the radar.

[0013] Preferably, the calculation method of the motion model in the sense of minimum root mean square error in step 4 is:

[0014] Step 4.1: Select two points from multiple points of different radars to initialize the parameters of the uniform linear motion model;

[0015] Step 4.2: Take out the timestamp of each radar point trace, bring it into the motion model, and calculate the position corresponding to the timestamp;

[0016] Step 4.3: Calculate the Euclidean distance between the position of each radar point and the position calculated in the previous step as the distance error at each timestamp;

[0017] Step 4.4: Use the distance error at each timestamp to calculate the entire RMS error;

[0018] Step 4.5: Determine whether the RMS error is less than a threshold. If so, end the step. Otherwise, increase the offset value by the motion model parameters and repeat steps 4.2 to 4.5.

[0019] Preferably, the threshold in step 4.5 is selected as the maximum value of the measurement errors of each radar sensor.

[0020] The present invention eliminates the false traces of the interfered radar by utilizing the different interference conditions of sensors from different sources without losing the detection performance of the radar, thereby providing users with a clearer radar picture with easy-to-find targets, making the radar's tracking of targets more stable and reliable, and making it easier for operators to grasp the target situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a preferred flow chart of the present invention. DETAILED DESCRIPTION

[0022] The preferred implementation process of the present invention is described as follows:

[0023] Figure 1 The figure is a flow chart of a preferred implementation process of the present invention, and the specific steps include:

[0024] Step 1: Receive n radar trace data and transform the data of each radar into a unified coordinate system according to the position of each sensor;

[0025] Step 2: Make a common view zone judgment for each point trace: According to the position of the current point trace, determine whether it is in the scanning area of ​​multiple radars. If the current point trace is in the scanning range of two or more radars, proceed to the next step, otherwise the point trace is regarded as a non-interference point for point trace arrangement;

[0026] Step 3: Time window judgment of the point trace: according to the timestamp t of the current i-th point trace i and the sector data update time t of the bth radar b The time-dependent region window is v m |t i -t b |, where v m To focus on the maximum moving speed of the target. Then determine whether each radar has a point trace in the time-related regional window, and the results are expressed as follows:

[0027]

[0028] like Then proceed to the next step, otherwise jump to step 6;

[0029] Step 4: Determine the homology of the traces from multiple different radars: Extract the timestamp of each trace from the traces of different radars, and use the position and timestamp Z(t b )f b|b=1:n, the motion model in the sense of minimum root mean square error is calculated as The specific calculation steps are as follows:

[0030] Step 4.1: Select two points from multiple points of different radars to initialize the parameters of the uniform linear motion model;

[0031] Step 4.2: Take out the timestamp of each radar point trace, bring it into the motion model, and calculate the position corresponding to the timestamp;

[0032] Step 4.3: Calculate the Euclidean distance between the position of each radar point and the position calculated in the previous step as the distance error at each timestamp;

[0033] Step 4.4: Use the distance error at each timestamp to calculate the entire RMS error;

[0034] Step 4.5: Determine whether the RMS error is less than the threshold. If it is, end the step. Otherwise, increase the offset value by the motion model parameters and repeat steps 4.2 to 4.5. The threshold is selected as σ b Represents the measurement error of the corresponding sensor of radar b.

[0035] Calculate the position deviation between the current point and the motion model | Z(t b )-X(t b ) | , and make the following judgment:

[0036] like | Z(t b )-X(t b ) | >3σ b , then the point trace is a non-homologous point trace, and proceed to the next step. Otherwise, the point trace is a homologous point trace, and the point trace is regarded as a non-interference point for point trace arrangement, where σ b Represents the measurement error of the corresponding sensor of radar b.

[0037] Step 5: Send the arranged points to the composite tracking module, which forms a target tracking track through multi-radar data association filtering;

[0038] Step 6: Determine whether the current point track is the target source of the existing track: The real-time extrapolation module extrapolates the target track to the current moment based on the motion parameters of the target tracking track and the timestamp of the current point track, and determines whether the current point track is within the existing target track gate based on the extrapolated position. If the current point track is within the target track gate, it is regarded as a non-interference point for point track arrangement, otherwise it is regarded as an interference point and is removed.

[0039] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A point-level collaborative anti-interference method based on spatiotemporal consistency judgment of multi-source information, characterized by: Step 1: Receive the point track data of n radars, and transform the point track data of each radar from the coordinate system of each sensor to a unified coordinate system; Step 2: Make a common view zone judgment for each point trace: According to the position of the current point trace, determine whether it exists in the scanning area of ​​multiple radars. If the current point trace exists in the scanning area of ​​two or more radars, proceed to the next step, otherwise the point trace is regarded as a non-interference point for point trace arrangement; Step 3: Time window judgment of the point trace: Calculate the time-related regional window according to the timestamp of the current point trace and the sector data update time of each radar, and then judge whether each radar has the point trace in the time-related regional window. If other radars have point traces, proceed to the next step, otherwise jump to step 6; Step 4: Make homology judgment on the traces of multiple different radars: extract the timestamp of each trace from the traces of different radars, and use the position and timestamp of these traces to calculate the motion model in the sense of minimum root mean square error, calculate the position deviation between the current trace and the motion model, and judge whether the position deviation is greater than the threshold. If the deviation is greater than the threshold, the trace is a non-homologous trace and proceed to the next step. Otherwise, the trace is a homologous trace and the trace is regarded as a non-interference point for trace arrangement. Step 5: Send the arranged points to the composite tracking module, which forms a target tracking track through multi-radar data association filtering; Step 6: Determine whether the current point track is the target source of the existing track: The real-time extrapolation module extrapolates the target track to the current moment based on the motion parameters of the target tracking track and the timestamp of the current point track, and determines whether the current point track is within the existing target track gate based on the extrapolated position. If the current point track is within the target track gate, it is regarded as a non-interference point for point track arrangement, otherwise it is regarded as an interference point and is removed.

2. The point-level collaborative anti-interference method based on spatiotemporal consistency judgment of multi-source information according to claim 1 is characterized in that: The threshold in step 4 is selected to be three times the measurement error of the sensor corresponding to the radar.

3. The point-level collaborative anti-interference method based on spatiotemporal consistency judgment of multi-source information according to claim 1 is characterized in that: The calculation method of the motion model in the sense of minimum root mean square error in step 4 is: Step 4.1: Select two points from multiple points of different radars to initialize the parameters of the uniform linear motion model; Step 4.2: Take out the timestamp of each radar point trace, bring it into the motion model, and calculate the position corresponding to the timestamp; Step 4.3: Calculate the Euclidean distance between the position of each radar point and the position calculated in the previous step as the distance error at each timestamp; Step 4.4: Use the distance error at each timestamp to calculate the entire RMS error; Step 4.5: Determine whether the RMS error is less than a threshold. If so, end the step. Otherwise, increase the offset value by the motion model parameters and repeat steps 4.2 to 4.

5.

4. The point-level collaborative anti-interference method based on spatiotemporal consistency judgment of multi-source information according to claim 3 is characterized by: The threshold in step 4.5 is selected as the maximum value of the measurement errors of each radar sensor.

Images