Anti-interference tracking method and device based on pseudo measurement structure in complex interference scene

Abstract

The invention belongs to the technical field of radar data processing, and particularly relates to an anti-interference tracking method and device based on a pseudo measurement structure in a complex interference scene, and the method comprises the steps: S1, determining the measurement type of each piece of received measurement information according to a missing dimension; s2, associating each piece of received measurement information with an existing track according to a measurement type, and determining an associated track of each piece of measurement information; s3, for each piece of measurement information associated with each track, if there is a missing dimension, predicting and supplementing the missing dimension; and S4, for each track, selecting the measurement information with the minimum statistical distance with the track from the measurement information associated with the track to perform filtering tracking on the track. According to the invention, the dimension information measured in an interference scene is effectively utilized, the complexity of an anti-interference tracking algorithm is reduced, and the tracking stability and accuracy of the track are improved.

Description

Technical Field

[0001] The present application belongs to the field of radar data processing technology, and in particular relates to an anti-interference tracking method and device based on pseudo-measurement construction in complex interference scenarios. Background Art

[0002] With the continuous development of modern technology and the advancement of weapons and equipment, the actual scenarios faced by target tracking are becoming increasingly complex, especially the problem of target tracking in complex interference scenarios, which has attracted more and more attention.

[0003] In non-interference scenarios, the measurements detected by radar generally include four-dimensional information of radial distance, radial velocity, azimuth and pitch angle. In interference scenarios, the distance and velocity dimensions can be interfered with, such as distance deception interference, velocity deception interference, distance and velocity combination deception interference, etc. Due to the existence of various interference methods, the confidence of radial distance or radial velocity in the four-dimensional information of the measurement detected by radar is greatly reduced or even missing, which poses a great challenge to target tracking, especially the multiple interference measurements caused by the coexistence of multiple interferences. Summary of the invention

[0004] In order to solve the above problems, the present application provides an anti-interference tracking method and device based on pseudo-measurement construction in a complex interference scenario. By dimensional classification of the measurements received in the interference scenario, it can be divided into full-dimensional measurement, missing distance measurement, missing speed measurement, and missing distance and speed measurement. If the measurement associated with the track does not include full-dimensional measurement, pseudo-measurement construction is performed on multiple missing-dimensional measurements, and further full-dimensional filtering or correction is performed.

[0005] The first aspect of the present application provides an anti-interference tracking method based on pseudo-measurement construction in a complex interference scenario, which mainly includes:

[0006] Step S1: Determine the measurement type of each received measurement information according to the missing dimension;

[0007] Step S2: for each received measurement information, associate it with the existing track according to the measurement type, and determine the associated track of each measurement information;

[0008] Step S3: For each measurement information associated with each track, if there is a missing dimension, predict and supplement the missing dimension;

[0009] Step S4: For each track, select the measurement information with the smallest statistical distance to the track from among the associated measurement information to perform filtering tracking on the track.

[0010] Preferably, in step S1, the measurement types include full-dimensional measurement without any missing dimension, missing distance measurement with missing distance dimension, missing speed measurement with missing speed dimension, and missing distance and speed measurement with missing distance and speed dimensions.

[0011] Preferably, step S2 further comprises:

[0012] Step S21, comparing each associated dimension of the measurement type to which the measurement information belongs with the corresponding dimension of each track to determine the associated dimension residual;

[0013] Step S22: when the correlation dimension residual is within a preset range, the measurement type is associated with the track;

[0014] Step S23: When there are different measurement types in the multiple measurement information associated with the track, one or more measurement information belonging to the measurement type with the most associated dimensions is retained.

[0015] Preferably, step S3 further comprises:

[0016] For the measurement information of the missing distance dimension, random measurement distance noise is added on the basis of the assumed predicted distance to construct the measurement distance;

[0017] For measurement information that lacks a speed dimension, the measurement speed is constructed based on a linear function or a quadratic function of time constructed from speed information in historical measurement information.

[0018] Preferably, step S4 further comprises:

[0019] The four-dimensional residual of the self-check between the track and the minimum measurement information is determined. When the four-dimensional residual exceeds the set value, the dimension corresponding to the track is directly corrected using the real dimension in the minimum measurement information.

[0020] The second aspect of the present application provides an anti-interference tracking device based on pseudo-measurement construction in a complex interference scenario, which mainly includes:

[0021] A measurement type determination module, used to determine the measurement type of each measurement information received according to the missing dimension;

[0022] The track association module is used to associate each received measurement information with the existing track according to the measurement type, and determine the associated track of each measurement information;

[0023] A missing dimension supplement module is used to predict and supplement the missing dimensions of each measurement information associated with each track, if there are any missing dimensions;

[0024] The measurement screening and track tracking module is used to select, for each track, the measurement information with the smallest statistical distance to the track from the associated measurement information to perform filtering and tracking on the track.

[0025] Preferably, the measurement types include full-dimensional measurements without any missing dimensions, missing distance measurements with missing distance dimensions, missing speed measurements with missing speed dimensions, and missing distance and speed measurements with missing distance and speed dimensions.

[0026] Preferably, the track association module includes:

[0027] A correlation dimension residual determination unit, used for comparing each correlation dimension of the measurement type to which the measurement information belongs with the corresponding dimension of each track, to determine the correlation dimension residual;

[0028] A track association unit, used to associate the measurement type with the track when the associated dimension residual is within a preset range;

[0029] The measurement information screening unit is used to retain one or more measurement information belonging to the measurement type with the most associated dimensions when there are different measurement types in the multiple measurement information associated with the track.

[0030] Preferably, the missing dimension supplementation module comprises:

[0031] A measurement distance construction unit is used to construct a measurement distance by adding random measurement distance noise to the measurement information of the missing distance dimension on the basis of the assumed predicted distance;

[0032] The measurement speed construction unit is used to construct the measurement speed for the measurement information with missing speed dimension according to the linear function or quadratic function about time constructed by the speed information in the historical measurement information.

[0033] Preferably, the measurement screening and track tracking module further comprises:

[0034] The state correction tracking unit is used to determine the four-dimensional residual of the self-check between the track and the minimum measurement information. When the four-dimensional residual exceeds the set value, the dimension corresponding to the track is directly corrected using the real dimension in the minimum measurement information.

[0035] This application effectively utilizes the dimensional information measured in the interference scene, reduces the complexity of the anti-interference tracking algorithm, and improves the tracking stability and accuracy of the track. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flowchart of a preferred embodiment of the anti-interference tracking method based on pseudo-measurement construction in complex interference scenarios of the present application.

[0037] Figure 2For this application Figure 1 A schematic diagram of an example structure for performing measurements in the embodiment shown.

[0038] Figure 3 For this application Figure 1 A schematic diagram of a speed measurement structure based on a linear function in the illustrated embodiment.

[0039] Figure 4 For this application Figure 1 A schematic diagram of a speed measurement structure based on a quadratic function of the illustrated embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the implementation of this application will be described in more detail in combination with the drawings in the implementation of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described implementation is a part of the implementation of this application, not all of the implementations. The implementation described below with reference to the drawings is exemplary and is intended to be used to explain this application, and cannot be understood as a limitation on this application. Based on the implementation in this application, all other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The implementation of this application is described in detail below in combination with the drawings.

[0041] The first aspect of the present application provides an anti-interference tracking method based on pseudo-measurement construction in a complex interference scenario, which mainly includes:

[0042] Step S1: Determine the measurement type of each received measurement information according to the missing dimension;

[0043] Step S2: for each received measurement information, associate it with the existing track according to the measurement type, and determine the associated track of each measurement information;

[0044] Step S3: For each measurement information associated with each track, if there is a missing dimension, predict and supplement the missing dimension;

[0045] Step S4: For each track, select the measurement information with the smallest statistical distance to the track from among the associated measurement information to perform filtering tracking on the track.

[0046] The present application first determines the measurement type in step S1. In some optional implementations, in step S1, the measurement type includes full-dimensional measurement without any missing dimensions, hereinafter referred to as type 1, missing distance measurement of missing distance dimension, hereinafter referred to as type 2, missing speed measurement of missing speed dimension, hereinafter referred to as type 3, and missing distance and speed measurement of missing distance and speed dimensions, hereinafter referred to as type 4.

[0047] Figure 1 A flowchart of the specific steps for constructing pseudo-measurements based on the above four types of measurements for anti-interference tracking is given. Then, in step S2, track association is performed. Since there may be multiple targets detected by the radar, there are multiple tracks corresponding to them, and the four-dimensional information of each track is complete, but the measurement may be missing dimensions. Therefore, it is necessary to use different association dimensions for track association according to the measurement type given in step S1. For example, if the measurement type is missing speed measurement, the three dimensions of distance, azimuth, and pitch are used to associate with the track. This step is used to match the closest track and determine which of the multiple tracks (targets) each measurement should actually correspond to.

[0048] It should also be added that after being associated by the dimension association method, the association results of the track may appear in various situations. If both full-dimensional measurements and missing-dimensional measurements are associated at the same time, the full-dimensional measurements are associated first. Further, if multiple full-dimensional measurements are associated, the measurement with the smallest statistical distance to the track is associated. If the associated measurement is only the missing-dimensional measurement, it is necessary to further subdivide it according to the associated missing-dimensional measurement composition, that is, first determine whether there is a missing distance measurement or a missing speed measurement in the associated missing-dimensional measurement. If not, only the pure angle measurement of the missing distance and speed measurement type is left. The track predicted distance and speed are combined with the measurement angle of the missing distance and speed measurement type to construct a pseudo-measurement. If there is a missing distance measurement or a missing speed measurement in the associated missing-dimensional measurement, it will no longer be associated with the missing distance and speed measurement type. Further, the pseudo-measurement construction is performed on the missing distance measurement or the missing speed measurement type, and is divided into three situations for construction, namely, missing distance measurement, missing speed measurement, and both missing distance measurement and missing speed measurement.

[0049] For example, suppose there are two head-on targets that are both releasing interference signals, and the track information of the two targets is shown in Table 1 below.

[0050] Table 1 Track information list

[0051] Track number Four-dimensional information 1 [56932.5,-189.3,1.23,0.34] 2 [82705.1,-199.1,3.56,-0.28]

[0052] Since both targets are releasing interference, the local radar can only receive the missing distance measurement of one target and the missing speed measurement of the other target. The measurement information received by the local radar is shown in Table 2 below.

[0053] Table 2 Measurement information list

[0054] Sequencing number Measurement information 1 [0,-188.1,1.41,0.14] 2 [0,-192.3,1.38,0.25] 3 [82765.7,0,3.87,-0.01] 4 [82721.1,0,3.74,-0.14] 5 [82698.2,0,3.23,-0.32]

[0055] Each measurement information in Table 2 needs to match the track in Table 1. In some optional implementations, step S2 further includes:

[0056] Step S21, comparing each associated dimension of the measurement type to which the measurement information belongs with the corresponding dimension of each track to determine the associated dimension residual;

[0057] Step S22: when the correlation dimension residual is within a preset range, the measurement type is associated with the track;

[0058] Step S23: When there are different measurement types in the multiple measurement information associated with the track, one or more measurement information belonging to the measurement type with the most associated dimensions is retained.

[0059] The preset range of step S22 is, for example, [200m, 15m / s, 1.5°, 1.5°]. According to the relationship between the correlation dimension residual and the preset range, the data in Table 1 and Table 2 are calculated to obtain a correlation table as shown in Table 3.

[0060] Table 3 Measurement and track correlation table

[0061]

[0062] It can be seen that the track number associated with measurements 1 and 2 is 1, and track 1 will be used to reconstruct these two measurements later. The track number associated with measurements 3, 4, and 5 is 2, and track 2 will be used to reconstruct these three measurements later.

[0063] Step S3 is a reconstruction step, which is used to predict and supplement the missing dimensions of the above measurements.

[0064] In some optional implementations, step S3 further includes:

[0065] (1) For the measurement information of the missing distance dimension, random measurement distance noise is added on the basis of the assumed predicted distance to construct the measurement distance.

[0066] In this embodiment, the measurement distance R is constructed Build =R Pred +δ R ; Among them, R Pred is the track prediction distance, δ R Randomly generated measurement distance noise. Figure 2 As shown, assuming that the distance of track 1 is predicted to be 56850.3m by historical curve fitting and the randomly generated measurement distance noise is 17.8m, the constructed measurement distance calculated according to the above formula is 56868.1m.

[0067] (2) For measurement information that lacks the speed dimension, the measurement speed is constructed based on a linear function or a quadratic function of time constructed from the speed information in the historical measurement information.

[0068] In this embodiment, the track fitting speed is used to assign a value to the speed dimension corresponding to the measurement information of the missing speed dimension. The track fitting function is as follows:

[0069]

[0070] The non-maneuvering scenario uses a straight-line fitting method, and the maneuvering scenario uses a curve fitting method. T is the track history correlation measurement time, V Line and V Curve is the measurement speed corresponding to the measurement time in the above different scenarios. The coefficients of the linear function or the quadratic function in the above formula are generated by fitting according to the historical measurement information.

[0071] Figure 3 This is a schematic diagram of the measurement speed construction of a linear function. Figure 4 The diagram is a schematic diagram of the measurement speed construction of a quadratic function. For example, for the measurement data No. 3-5 above, the track fitting speed is -198.6m / s according to the historical measurement associated with the track and the above formula. Therefore, for the measurement information in Table 2, the four-dimensional measurement in Table 4 below is obtained by forgery.

[0072] Table 4 Pseudo measurement structure table

[0073] Sequencing number Measurement information 1 [56868.1m,-188.1m / s,1.41°,0.14°] 2 [56868.1m,-192.3m / s,1.38°,0.25°] 3 [82765.7m,-198.6m / s,3.87°,-0.01°] 4 [82721.1m,-198.6m / s,3.74°,-0.14°] 5 [82698.2m,-198.6m / s,3.23°,-0.32°]

[0074] Then in step S4, if the number of associated pseudo-measurements is greater than 1, the statistical distance between the associated pseudo-measurements and the track is used for further screening, and finally the pseudo-measurement with the smallest statistical distance is selected for association. For example, in the above embodiment, the number of measurements associated with track 1 is 2, and the number of measurements associated with track 2 is 3. It is further necessary to calculate the statistical distance between the pseudo-measurements corresponding to these measurements and the track. After screening, the measurement statistical distance between track 1 and measurement sequence number 2 is the smallest, and the measurement statistical distance between track 2 and measurement sequence number 5 is the smallest. The final association result is that track 1 is associated with the pseudo-measurement with measurement sequence number 2, and track 2 is associated with the pseudo-measurement with measurement sequence number 5.

[0075] In some optional implementations, step S4 further includes:

[0076] The four-dimensional residual of the self-check between the track and the minimum measurement information is determined. When the four-dimensional residual exceeds the set value, the dimension corresponding to the track is directly corrected using the real dimension in the minimum measurement information.

[0077] In this embodiment, if the residual between the track prediction state and the pseudo-measurement distance or speed dimension is large, filtering divergence will occur if filtering is performed. At this time, the dimension corresponding to the track should be directly corrected using the real dimension in the pseudo-measurement, and finally the four-dimensional information of the track is obtained through filtering or correction.

[0078] As mentioned above, the four-dimensional residual of the pseudo-measurement on track 1 and the associated one is [-64.4m, -3m / s, 0.15°, -0.09°], and the four-dimensional residual of the pseudo-measurement on track 2 and the associated one is [-6.9m, 0.5m / s, -0.33°, -0.04°]. The four-dimensional residual meets the filtering requirements and can be directly subjected to full-dimensional filtering, and further obtained:

[0079] The filtering result of track 1 is [56908.3m,-190.7m / s,1.34°,0.29°];

[0080] The filtering result of track 2 is [82701.3m,-198.7m / s,3.33°,-0.31°].

[0081] This application performs dimension classification processing on the measurements when there are multiple measurement types in complex interference scenarios, and then selects different dimension association methods based on the dimension classification results, and further processes the dimension association results. If the associated measurements contain missing-dimensional measurements, pseudo-measurement construction is required, and then statistical distance judgment and filter estimation for pseudo-measurements are used. This method improves the tracking stability of airborne multi-function radars for targets in complex interference scenarios, and provides an important method for the research of key technologies for anti-interference tracking.

[0082] This application is suitable for airborne multi-function radars to track targets in complex interference scenarios. It can not only be used in data processing for target tracking by radar platforms such as fire control, search and surveillance, but can also be applied to other filtering algorithms.

[0083] The second aspect of the present application provides an anti-interference tracking device based on pseudo-measurement construction in a complex interference scenario corresponding to the above method, mainly comprising:

[0084] A measurement type determination module, used to determine the measurement type of each measurement information received according to the missing dimension;

[0085] The track association module is used to associate each received measurement information with the existing track according to the measurement type, and determine the associated track of each measurement information;

[0086] A missing dimension supplement module is used to predict and supplement the missing dimensions of each measurement information associated with each track, if there are any missing dimensions;

[0087] The measurement screening and track tracking module is used to select, for each track, the measurement information with the smallest statistical distance to the track from the associated measurement information to perform filtering and tracking on the track.

[0088] In some optional implementations, the measurement types include full-dimensional measurements without any missing dimensions, missing distance measurements with missing distance dimensions, missing speed measurements with missing speed dimensions, and missing distance and speed measurements with missing distance and speed dimensions.

[0089] In some optional implementations, the track association module includes:

[0090] A correlation dimension residual determination unit, used for comparing each correlation dimension of the measurement type to which the measurement information belongs with the corresponding dimension of each track, to determine the correlation dimension residual;

[0091] A track association unit, used to associate the measurement type with the track when the associated dimension residual is within a preset range;

[0092] The measurement information screening unit is used to retain one or more measurement information belonging to the measurement type with the most associated dimensions when there are different measurement types in the multiple measurement information associated with the track.

[0093] In some optional implementations, the missing dimension supplementation module includes:

[0094] A measurement distance construction unit is used to construct a measurement distance by adding random measurement distance noise to the measurement information of the missing distance dimension on the basis of the assumed predicted distance;

[0095] The measurement speed construction unit is used to construct the measurement speed for the measurement information with missing speed dimension according to the linear function or quadratic function about time constructed by the speed information in the historical measurement information.

[0096] In some optional embodiments, the measurement screening and track tracking module further includes:

[0097] The state correction tracking unit is used to determine the four-dimensional residual of the self-check between the track and the minimum measurement information. When the four-dimensional residual exceeds the set value, the dimension corresponding to the track is directly corrected using the real dimension in the minimum measurement information.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. An anti-interference tracking method based on pseudo-measurement construction in complex interference scenarios, characterized in that: include: Step S1: Determine the measurement type of each received measurement information according to the missing dimension; Step S2: for each received measurement information, associate it with the existing track according to the measurement type, and determine the associated track of each measurement information; Step S3: For each measurement information associated with each track, if there is a missing dimension, predict and supplement the missing dimension; Step S4: For each track, select the measurement information with the smallest statistical distance to the track from among the associated measurement information to perform filtering tracking on the track.

2. The anti-interference tracking method based on pseudo-measurement construction in complex interference scenarios according to claim 1, characterized in that: In step S1, the measurement types include full-dimensional measurement without any missing dimension, missing distance measurement with missing distance dimension, missing speed measurement with missing speed dimension, and missing distance and speed measurement with missing distance and speed dimensions.

3. The anti-interference tracking method based on pseudo-measurement construction in complex interference scenarios according to claim 1, characterized in that: Step S2 further comprises: Step S21, comparing each associated dimension of the measurement type to which the measurement information belongs with the corresponding dimension of each track to determine the associated dimension residual; Step S22: when the correlation dimension residual is within a preset range, the measurement type is associated with the track; Step S23: When there are different measurement types in the multiple measurement information associated with the track, one or more measurement information belonging to the measurement type with the most associated dimensions is retained.

4. The anti-interference tracking method based on pseudo-measurement construction in complex interference scenarios according to claim 1, characterized in that: Step S3 further comprises: For the measurement information of the missing distance dimension, random measurement distance noise is added on the basis of the assumed predicted distance to construct the measurement distance; For measurement information that lacks a speed dimension, the measurement speed is constructed based on a linear function or a quadratic function of time constructed from speed information in historical measurement information.

5. The anti-interference tracking method based on pseudo-measurement construction in complex interference scenarios according to claim 1, characterized in that: Step S4 further comprises: The four-dimensional residual of the self-check between the track and the minimum measurement information is determined. When the four-dimensional residual exceeds the set value, the dimension corresponding to the track is directly corrected using the real dimension in the minimum measurement information.

6. An anti-interference tracking device based on pseudo-measurement construction in complex interference scenarios, characterized in that: include: A measurement type determination module, used to determine the measurement type of each measurement information received according to the missing dimension; The track association module is used to associate each received measurement information with the existing track according to the measurement type, and determine the associated track of each measurement information; A missing dimension supplement module is used to predict and supplement the missing dimensions of each measurement information associated with each track, if there are any missing dimensions; The measurement screening and track tracking module is used to select, for each track, the measurement information with the smallest statistical distance to the track from the associated measurement information to perform filtering and tracking on the track.

7. The anti-interference tracking device based on pseudo-measurement construction in complex interference scenarios according to claim 6, characterized in that: The measurement types include full-dimensional measurements without any missing dimensions, missing distance measurements with missing distance dimensions, missing speed measurements with missing speed dimensions, and missing distance and speed measurements with missing distance and speed dimensions.

8. The anti-interference tracking device based on pseudo-measurement construction in complex interference scenarios according to claim 6, characterized in that: The track association module includes: A correlation dimension residual determination unit, used for comparing each correlation dimension of the measurement type to which the measurement information belongs with the corresponding dimension of each track, to determine the correlation dimension residual; A track association unit, used to associate the measurement type with the track when the associated dimension residual is within a preset range; The measurement information screening unit is used to retain one or more measurement information belonging to the measurement type with the most associated dimensions when there are different measurement types in the multiple measurement information associated with the track.

9. The anti-interference tracking device based on pseudo-measurement construction in complex interference scenarios according to claim 6, characterized in that: The missing dimension supplementation module comprises: A measurement distance construction unit is used to construct a measurement distance by adding random measurement distance noise to the measurement information of the missing distance dimension on the basis of the assumed predicted distance; The measurement speed construction unit is used to construct the measurement speed for the measurement information with missing speed dimension according to the linear function or quadratic function about time constructed by the speed information in the historical measurement information.

10. The anti-interference tracking device based on pseudo-measurement construction in complex interference scenarios according to claim 6, characterized in that: The measurement screening and track tracking module further includes: The state correction tracking unit is used to determine the four-dimensional residual of the self-check between the track and the minimum measurement information. When the four-dimensional residual exceeds the set value, the dimension corresponding to the track is directly corrected using the real dimension in the minimum measurement information.

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