Multi-target detection and tracking method under conditions of low observability and high clutter

Abstract

The invention discloses a multi-target detection and tracking method under conditions of low observability and high clutter, and belongs to the technical field of radar and sonar. The idea of the method is that a plurality of measurements from different propagation paths to a receiver are considered as the possible target measurements during the processing of the target-measurement correlation, and are enabled to be correctly correlated with each known multi-path measurement function, thereby obtaining the accumulation of target information, and improving the target detection capability; and then the target tracking is carried out in a mode of a sliding window. The method employs the target information which is transmitted to a sensor through different paths, enables the measurement information to be correctly correlated with each known multi-path measurement function, thereby obtaining the accumulation of target information, and improving the detection capability of a target under the conditions of the low observability and high clutter. The method can effectively reduce the impact between the adjacent objects.

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). [0002] technical background [0003] 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 du...

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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