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An Active and Passive Track Motion Feature Association Method Based on Sequential Processing

A motion feature, active and passive technology, applied in the field of track correlation, which can solve the problems of high processing delay, mismatch correlation error, etc.

Active Publication Date: 2021-05-14
THE 724TH RES INST OF CHINA SHIPBUILDING IND
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AI Technical Summary

Problems solved by technology

The present invention realizes multi-sensor track data correlation judgment by adopting active and passive multi-sensor correlation architecture design and high-reliability motion feature correlation judgment algorithm design, thereby solving the mismatch correlation error and processing caused by the inconsistency of time reference existing in traditional active and passive sensor correlation. High latency, greatly improving the accuracy of multi-sensor association

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  • An Active and Passive Track Motion Feature Association Method Based on Sequential Processing
  • An Active and Passive Track Motion Feature Association Method Based on Sequential Processing
  • An Active and Passive Track Motion Feature Association Method Based on Sequential Processing

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Embodiment Construction

[0006] The implementation process of the present invention is as figure 2 As shown, the specific description is the following process.

[0007] Step 1: Track traversal pairing by sectors.

[0008] The observation space is divided into 12 sectors; one sector is selected from the sector, and a target track output by an active sensor and a track output by a passive sensor are selected from the sector to form a track pair to be associated and judged ;

[0009] Step 2: Nearest Neighbor Temporal Extrapolation Registration.

[0010] 1) Select the above-mentioned group of track pairs to be associated and judged, select the observed track point obtained by the latest observation of the target track observed by the passive sensor in the track pair to be associated and judged, record it as track point P1, and set The observation time of the observed track point is used as the reference time, and the observed track point closest to the reference time in the track observed by the activ...

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Abstract

The invention relates to a method for associating active and passive track motion features based on sequential processing. Due to the lack of effective and accurate estimation of the statistical information of heterogeneous sensors and the lack of reliable cleaning methods for the associated track in the existing correlation methods, the existing active and passive correlation based on the motion characteristics of the target has a high correlation error rate and a large consumption of processing resources. etc., the present invention proposes an active and passive track motion feature association method based on sequential processing to complete the efficient association of massive observation tracks. The specific steps include: sectoral track traversal pairing, nearest neighbor time extrapolation registration , Statistical characteristic estimation of motion characteristics of active sensor targets, estimation of statistical characteristics of target motion characteristics of passive sensors, and association judgment based on the correlation of motion characteristics, so as to meet the needs of high-reliability association applications of massive observation data in complex detection environments.

Description

technical field [0001] The invention relates to a track association method, in particular to an active and passive track motion feature association method based on sequential processing. Background technique [0002] Accurate association of multi-sensor data is the key core technology to realize multi-source information fusion and exert integrated perception performance. Due to the lack of effective and accurate estimation of the statistical information of heterogeneous sensors and the lack of effective data governance means of input and output tracks in the traditional correlation method, the existing fusion technology has weak joint perception of the environment and targets and consumes a lot of processing resources. , High correlation error rate and other problems, it is difficult to meet the high reliability correlation application requirements of massive observation data in a complex detection environment. Contents of the invention [0003] In order to give full play...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/00
CPCG01C21/005
Inventor 陆翔周恒亮郑庆琳田田张宁
Owner THE 724TH RES INST OF CHINA SHIPBUILDING IND
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