A multi-target detection and tracking method under the condition of low observable and high clutter

Abstract

The invention discloses a multi-target detection and tracking method under the condition of low observability and high clutter, and belongs to the technical field of radar and sonar. The idea of ​​the present invention is to consider multiple measurements arriving at the receiver through different propagation paths as possible target measurements when dealing with the correlation problem between objects and measurements, and compare these measurements with known The multi-path measurement function is correctly correlated, so as to obtain the accumulation of target information and enhance the target detection ability; then the target is tracked by means of a sliding window. The present invention utilizes the target information that arrives at the sensor through different channels, and correctly correlates these measurement information with known multipath measurement functions, thereby obtaining the accumulation of target information and enhancing the target at low observable and high clutter levels. The detection ability in the environment can effectively reduce the influence between adjacent targets.

Description

technical field [0001] The invention belongs to the technical field of radar and sonar, and mainly relates to an extension method of maximum likelihood-probability multiple hypothesis tracking (ML-PMHT). technical background [0002] Target tracking technology is widely used in various fields, especially radar (sonar) signal system. The target tracking technology is divided into two categories: track after detection (TAD) and track before detection (TBD). In comparison, the calculation amount of TAD algorithm is low, which is conducive to real-time implementation, but because the TAD algorithm relies on the front-end signal processor to Target detection, tracking performance is not ideal in the case of low signal-to-noise ratio (SNR). Because the TBD algorithm adds target detection while tracking, it has a strong tracking ability for the target under low signal-to-noise ratio, but the application of the TBD algorithm in engineering is subject to many restrictions due to the...

AI Technical Summary

Problems solved by technology

However, the relationship between these measurements and the observed function before the target state is different, and the LLR calculated with a fixed likelihood function not only cannot accumulate target information, but also forms false target state parameter estimates

In addition, the existing basic ML-PMHT algorithm uses sequence detection for multiple targets. When the targets are close to each other, one target is searched, and other targets are not easy to be found.

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