A multi-level intelligent track tracking association method, electronic device and storage medium

Abstract

The present invention discloses a multi-level intelligent track tracking association method, electronic device and storage medium. The point track condenses to obtain point track data, and the point track data is divided into 1 to N levels; the Nth level point track data is filtered, and the filtered Nth level point track data is associated; if the association is successful, the process ends; if the association fails, the point track association parameters are adjusted, and then the previous level point track data is filtered, and the filtered point track data of this level is associated; the above steps are repeated until the association is successful or the first level point track data is filtered and the steps are repeated until the filtered first level point track data is associated; the association operation ends after the first level point track data is associated. The multi-level intelligent track tracking association method of the present invention has the advantages of being able to improve the continuity of track tracking and reduce the loss rate of the target track.

Description

Technical Field

[0001] The invention relates to a radar data processing technology, in particular to a multi-level intelligent track tracking association method, electronic equipment and storage medium for target track tracking. Background Art

[0002] Radar is an electronic device that uses electromagnetic waves to detect targets. An important part of radar data processing is target track tracking, especially when radar tracking low-altitude targets is affected by ground clutter, weather clutter, etc., and there is residual clutter. The quality of target track tracking is directly related to the target tracking continuity and the operator's workload.

[0003] The data association problem is a critical link in track tracking. Solving the data association problem in target tracking is to solve the problem of matching point tracks with tracks. Improper association between point tracks and tracks may lead to the wrong target or loss of tracking, which will seriously affect the performance of the entire tracking system.

[0004] Normal track tracking processing includes point track preprocessing, point track correlation, automatic start, filter tracking and other functions. The traditional method of point navigation correlation in the filter tracking process is to give a prediction window after filtering prediction, and the point navigation correlation of the points in the window is completed. The general processing process is to filter the point track and then perform track processing (track start, track filtering, point navigation correlation, etc.), and there is a contradictory relationship between point track filtering and track tracking. That is, if the filtering criteria of the point track filtering is set too low, it is easy to cause incorrect association of point navigation, resulting in the problem of track tracking jumping points. If the point track filtering criteria are set too high, it is easy to lose track of the target.

[0005] In the track tracking process of the prior art, especially the track tracking process of the low-altitude radar, the relationship between point track filtering and track tracking is not properly handled, the continuity of track tracking is poor, and the problem of track loss is prone to occur. Summary of the invention

[0006] The present invention aims to avoid the deficiencies existing in the above-mentioned prior art and provide a multi-level intelligent track tracking association method to improve the continuity of track tracking and thereby effectively reduce the target track loss rate.

[0007] The present invention adopts the following technical solutions to solve the technical problems.

[0008] A multi-level intelligent track tracking association method of the present invention comprises the following steps:

[0009] Step 1: obtain point data by point condensation, filter the point data hierarchically, and divide the data after hierarchical filtering into 1 to N levels;

[0010] Step 2: Store the point trace results of each level in the above 1 to N level point trace data respectively;

[0011] Step 3: Associate the filtered Nth level point data;

[0012] Step 4: If the association is successful, the association operation is completed;

[0013] Step 5: If the association fails, adjust the point navigation association parameters, and then associate the point track data of the previous level after filtering;

[0014] Step 6: Repeat the above steps 3 to 5 until the association is successful or repeat steps 3 to 5 until the filtered first-level point data is associated;

[0015] Step 7: After associating the first-level point trace data, whether the association is successful or failed, the association operation is ended.

[0016] The structural characteristics of a multi-level intelligent track tracking association method of the present invention are also:

[0017] Preferably, in step 5, adjusting the point navigation associated parameters is narrowing the point navigation associated parameters.

[0018] Preferably, the narrowing method is geometric narrowing.

[0019] Preferably, the narrowing method is arithmetic narrowing.

[0020] Preferably, the point-to-point navigation associated parameters include but are not limited to track prediction tracking window parameters.

[0021] Preferably, the track prediction tracking window parameters are narrowed by reducing the prediction window.

[0022] Preferably, the track prediction tracking window parameters are narrowed by increasing the matching threshold of the characteristic parameters.

[0023] The present invention also discloses an electronic device, comprising:

[0024] at least one processor; and,

[0025] a memory communicatively connected to the at least one processor; wherein,

[0026] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-level intelligent track tracking association method.

[0027] The present invention also discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the multi-level intelligent track tracking and association method when executed by a processor.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] The present invention discloses a multi-level intelligent track tracking association method, electronic device and storage medium. The method comprises the following steps: obtaining track data by point condensation, dividing the track data into 1 to N levels; associating the Nth level track data after filtering; ending the method if the association is successful; adjusting the point navigation association parameters if the point navigation association fails, and then associating the track data of the previous level after filtering; repeating the above steps until the association is successful or repeating the steps until associating the first level track data after filtering; ending the association operation after associating the first level track data.

[0030] The multi-level intelligent track tracking association method, electronic device and storage medium of the present invention mainly work on the basis of using the point track data of the intermediate process in the point track filtering process to improve the performance of track tracking, especially point navigation, and at the same time, by pushing up the associated point tracks in reverse, filtering the intermediate process point tracks, relaxing the point track quality restrictions, and narrowing the track prediction tracking window and feature matching parameters, reducing the possibility of mistracking. Finally, the overall performance is optimized through comprehensive parameter adjustment.

[0031] The multi-level intelligent track tracking association method, electronic device and storage medium of the present invention have the advantages of improving the continuity of track tracking and reducing the loss rate of the target track. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flowchart of a multi-level intelligent track tracking association method.

[0033] The present invention will be further described below through specific implementation modes in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0034] See also Figure 1 A multi-level intelligent track tracking association method of the present invention comprises the following steps:

[0035] Step 1: obtain point data by point condensation, filter the point data hierarchically, and divide the data after hierarchical filtering into 1 to N levels;

[0036] Step 2: Store the point trace results of each level in the above 1 to N level point trace data respectively;

[0037] Step 3: Associate the filtered Nth level point data;

[0038] Step 4: If the association is successful, the association operation is completed;

[0039] Step 5: If the association fails, adjust the point navigation association parameters, and then associate the point track data of the previous level after filtering;

[0040] Step 6: Repeat the above steps 3 to 5 until the association is successful or repeat steps 3 to 5 until the filtered first-level point data is associated;

[0041] Step 7: After associating the first-level point trace data, whether the association is successful or failed, the association operation is ended.

[0042] During specific implementation, in step 5, adjusting the point navigation associated parameters is to narrow the point navigation associated parameters.

[0043] In a specific implementation, the narrowing method is geometric narrowing.

[0044] In a specific implementation, the narrowing method is arithmetic progression narrowing.

[0045] In a specific implementation, the point-to-point navigation associated parameters include but are not limited to track prediction tracking window parameters.

[0046] In a specific implementation, the track prediction tracking window parameter is narrowed in a manner of reducing the prediction window.

[0047] In a specific implementation, the track prediction tracking window parameters are narrowed by increasing the matching threshold of the feature parameters.

[0048] The present invention also discloses an electronic device, comprising:

[0049] at least one processor; and,

[0050] a memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-level intelligent track tracking association method.

[0052] The present invention also discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the multi-level intelligent track tracking and association method when executed by a processor.

[0053] The multi-level intelligent track tracking association method, electronic device and storage medium of the present invention mainly work on the basis of using the point track data of the intermediate process in the point track filtering process to improve the performance of track tracking, especially point navigation, and at the same time, by pushing up the associated point tracks in reverse, filtering the intermediate process point tracks, relaxing the point track quality restrictions, and narrowing the track prediction tracking window and feature matching parameters, reducing the possibility of mistracking. Finally, the overall performance is optimized through comprehensive parameter adjustment.

[0054] The main process of the multi-level intelligent track tracking association method of the present invention is as follows:

[0055] 1. After the dot traces are condensed, the dot trace data is filtered hierarchically according to the predetermined rules, and the data after hierarchical filtering is divided into 1 to N levels; in order to filter out some false points, N-level dot trace filtering is further performed to reduce the number of dot traces. The dot trace results of each level are stored separately.

[0056] The pre-set rules include but are not limited to the following rules.

[0057] Rule 1) Signal-to-noise ratio threshold rule: The first-level point trace filtering signal-to-noise ratio threshold is T1, the second-level filtering signal-to-noise ratio threshold is T2, .... the m-th level filtering signal-to-noise ratio threshold is Tm. .... the N-th level filtering signal-to-noise ratio threshold is TN. When the point trace signal-to-noise ratio is lower than the signal-to-noise ratio threshold, the current point trace is filtered out, otherwise it is retained. Among them, T1, T2...TN can be set manually, or according to the principle of arithmetic or geometric progression, for example, Tm = T1 + (m-1) * d, where d is an arithmetic interval (for example, d = 1dB);

[0058] Tm = T1*q^(m-1), where q is the geometric coefficient (e.g. q = 1.1);

[0059] Rule 2) Azimuth width rule: The azimuth width threshold of the first-level point trace filtering is W1, the azimuth width threshold of the second-level filtering is W2, .... the m-th level filtering signal-to-noise ratio threshold is Wm.... the N-th level filtering azimuth width threshold is WN. When the point trace azimuth width is lower than the azimuth width threshold, the current point trace is filtered out, otherwise it is retained. Among them, T1, T2...TN can be set manually, or according to the principle of arithmetic progression or geometric ratio, for example, Wm=W1+(m-1)*dw, where dw is an equidistant interval (for example, dw=0.1 degree); Wm=W1*qw^(m-1), where qw is a geometric ratio coefficient (for example, qw=1.06);

[0060] Rule 3) Distance thickness deviation degree rule: the first level point trace filter distance thickness deviation threshold is R1, the second level filter distance thickness deviation threshold is R2, .... the mth level filter signal-to-noise ratio threshold is Rm.... the Nth level filter distance thickness deviation threshold is RN. When the point trace distance thickness deviation is higher than the distance thickness deviation threshold, the current point trace is filtered out, otherwise it is retained. The distance thickness deviation degree = |point trace distance thickness - standard distance thickness|, R1, R2... RN can be set manually, or it can be set according to the principle of arithmetic progression or geometric ratio, for example, Rm = R1-(m-1)*dr, where dr is an arithmetic interval (for example, dr = 2 meters);

[0061] Rm = R1*qr^(m-1), where qr is the geometric coefficient (e.g. qr = 0.95);

[0062] Rule 4) Doppler spectrum width deviation degree rule: The Doppler spectrum width deviation threshold of the first-level point trace filtering is D1, the Doppler spectrum width deviation threshold of the second-level filtering is D2, .... the m-th level filtering signal-to-noise ratio threshold is Dm.... the N-th level filtering Doppler spectrum width deviation threshold is DN. When the point trace Doppler spectrum width deviation is higher than the Doppler spectrum width deviation threshold, the current point trace is filtered out, otherwise it is retained. The Doppler spectrum width deviation degree = |point trace Doppler spectrum width - standard Doppler spectrum width|, D1, D2...DN can be set manually, or it can be set according to the principle of arithmetic progression or geometric ratio, for example, Dm = D1-(m-1)*dd, where dd is an equidistant interval (for example, dd = 8 Hz); Dm = D1*qd^(m-1), where qd is a geometric ratio coefficient (for example, qd = 0.98);

[0063] Rule 5) Point trace quality scoring rule: Point trace quality score = weighted sum of each point trace parameter, such as point trace quality score S = w1*normalized azimuth width + w2*normalized signal-to-noise ratio + w3*(1 / normalized Doppler spectrum width deviation) + w4*(1 / normalized distance thickness deviation) + ..., (w1+w2+w3+.....=1), w1~wn are weight coefficients, and then the point trace filtering criteria are filtered according to the point trace quality score. For example, the first-level point trace filtering point trace quality score threshold is S1, the second-level filtering point trace quality score threshold is S2, .... The m-th level filtering signal-to-noise ratio threshold is Sm.... The N-th level filtering point trace quality score threshold is SN. When the point trace quality score is lower than the point trace quality score threshold, the current point trace is filtered out, otherwise it is retained. S1, S2...SN can be set manually or according to the principle of arithmetic progression or geometric ratio, for example, Sm = S1 + (m-1) * ds, where dd is an arithmetic interval (for example, ds = 0.05 minutes);

[0064] Sm = S1*qs^(m-1), where qs is the geometric coefficient (e.g. qs = 1.01);

[0065] Rule 6) can also be other rules: such as amplitude deviation coefficient rule; entropy feature rule, etc.

[0066] Rule 7) can also be some irregularly changing rules, such as the first N-1 levels of filtering according to the rules described above, and only the following criteria are used when filtering the Nth level of traces: when the distance of the trace is less than the distance Tr, and the trace comes from the low beam (a beam channel signal), the trace is filtered out, otherwise it is retained. Tr can be set manually.

[0067] 2. When performing point-to-point correlation, first correlate the points after filtering the Nth level points. If the correlation is successful, the process ends.

[0068] 3. If it is not associated, further tighten the point navigation association parameters, and then associate it with the point trace results of the N-1 level point trace filtering. If the association is completed, if it is not associated, further tighten the point navigation association parameters, and then associate it with the point trace results of the N-2 level point trace filtering.

[0069] 3. Similarly, the process of associating the point navigation with the point trace results of the N-2 level point trace filtering is the same as before. If any level in the middle can be associated, it ends. Otherwise, after tightening the point navigation association parameters, the point navigation of the next level is associated until the point navigation result of the 1st level point trace filtering is associated.

[0070] 4. After the first-level point track is associated with the point route, the operation is terminated regardless of whether it is associated or not.

[0071] The multi-level intelligent track tracking association method, electronic device and storage medium of the present invention belong to the track tracking problem in the field of radar data processing. It is a reverse upward tracing intelligent track association method, which aims to improve the radar track tracking capability, especially the track tracking problem of low-altitude radar. The main function is to provide a multi-level intelligent track tracking association method, which improves the continuity of track tracking and reduces the loss rate of target track under the condition of less clutter residual point tracks (radar operation screen is cleaner, reducing the burden on operators).

[0072] In the present invention, the parameters related to point navigation are adjusted, and the parameters include but are not limited to track prediction tracking window parameters and matching characteristic parameters. The characteristic parameters include distance thickness, azimuth width, signal-to-noise ratio change rate, amplitude deviation, data source (such as beam signal selection), etc. The matching narrowing method includes but is not limited to geometric change and arithmetic change.

[0073] The criterion narrowing rule methods in the association process include: reducing the prediction window, increasing the feature matching threshold (signal-to-noise ratio, EP echo number, range thickness, azimuth width), data source (such as beam signal selection), etc.

[0074] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0075] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A multi-level intelligent track tracking association method, Its characteristics are: The following steps are involved: Step 1: obtain point trace data by point trace aggregation, filter the point trace data hierarchically, and divide the data after hierarchical filtering into 1 to N levels; filter the point trace data hierarchically according to predetermined rules, and the predetermined rules include signal-to-noise ratio threshold rule, azimuth width rule, distance thickness deviation degree rule, Doppler spectrum width deviation degree rule, point trace quality scoring rule, amplitude deviation coefficient rule and entropy feature rule; Step 2: Store the point trace results of each level in the above 1 to N level point trace data respectively; Step 3: Associate the filtered Nth level point data; Step 4: If the association is successful, the association operation is completed; Step 5: If the association fails, adjust the point navigation association parameters, and then associate the point track data of the previous level after filtering; Step 6: Repeat the above steps 3 to 5 until the association is successful or repeat steps 3 to 5 until the filtered first-level point data is associated; Step 7: After associating the first-level point trace data, whether the association is successful or failed, the association operation is ended.

2. A multi-level intelligent track tracking association method according to claim 1, Its characteristics are: In the step 5, the step of adjusting the point navigation associated parameters is to narrow the point navigation associated parameters.

3. A multi-level intelligent track tracking association method according to claim 2, Its characteristics are: The narrowing method is geometric narrowing.

4. A multi-level intelligent track tracking association method according to claim 2, Its characteristics are: The narrowing method is arithmetic narrowing.

5. A multi-level intelligent track tracking association method according to claim 2, Its characteristics are: The point-to-point navigation associated parameters include but are not limited to track prediction tracking window parameters.

6. A multi-level intelligent track tracking association method according to claim 5, Its characteristics are: The track prediction tracking window parameter is narrowed in such a way that the prediction window is reduced.

7. A multi-level intelligent track tracking association method according to claim 5, Its characteristics are: The track prediction tracking window parameters are narrowed by increasing the matching threshold of the characteristic parameters.

8. An electronic device, It is characterized in that include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-level intelligent track tracking association method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, It is characterized in that When the computer program is executed by a processor, the multi-level intelligent track tracking association method according to any one of claims 1 to 7 is implemented.

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