A Fast Multi-sensor Potential Probability Hypothesis Density Filtering Method

Abstract

The invention belongs to the field of radar tracking, and aims to provide a fast multi-sensor set potential probability hypothesis density filtering method, which is realized by Gaussian mixture technology. By constructing simplified weight matrix and cost matrix, this method reduces the number of possible groups in the subsequent measurement grouping, avoids possible repetition problems in the grouping process, greatly improves the efficiency of the algorithm, and improves the tracking performance at the same time , to obtain more stable and accurate target state and target number estimation than traditional methods, and has a good engineering application prospect.

Description

technical field [0001] The present invention relates to the technical field of target tracking, in particular to a Fast Multisensor Cardinalized Probability Hypothesis Density (FMS-CPHD) filtering method. Background technique [0002] As target tracking scenarios become more and more complex, the requirements for target tracking technology are also increasing, and the performance of a single sensor often cannot meet the requirements. Multi-sensor collaborative tracking has become a common requirement in current surveillance scenarios. Compared with single-sensor tracking, the biggest problem of multi-sensor tracking is that it takes a long time and has poor timeliness. When the tracking environment is complex, it may not be able to meet the real-time requirements. At the same time, multi-sensor tracking also has unstable tracking performance, and the tracking performance cannot be guaranteed to be better than that of one of the sensors. [0003] Centralized multi-sensor fus...

AI Technical Summary

Problems solved by technology

[0002] As target tracking scenarios become more and more complex, the requirements for target tracking technology are also increasing, and the performance of a single sensor is often unable to meet the demand. Multi-sensor collaborative tracking has become a common requirement in current surveillance scenarios.

Compared with single-sensor tracking, the biggest problem with multi-sensor tracking is that it takes a long time and has poor timeliness. When the tracking environment is complex, it may not be able to meet the real-time requirements.

At the same time, multi-sensor tracking also has unstable tracking performance, and the tracking performance cannot be guaranteed to be better than one of the sensors.

[0003] Centralized multi-sensor fusion tracking can ensure that the tracking performance is better than that of any single sensor, and the tracking performance is guaranteed, but there are cases of high computational complexity and low computational efficiency

Multisensor Cardinalized Probability Hypothesis Density (MS-CPHD) filter based on Random Finite Set (RFS) theory is a centralized multi-sensor fusion tracking method with good tracking performance. However, the computational complexity is too high, the real-time performance is poor, and the performance is unstable, so it is difficult to be applied in engineering practice.

Images