Filtering algorithm based on self-adaptive new target strength

Abstract

The invention discloses a PHD (probability hypothesis density) filtering algorithm based on self-adaptive new target strength. The PHD filtering algorithm is implemented through the following steps: carrying out track initiation and track confirmation; constructing a strength function of a new target random set according to confirmed track initiation position information; merging a new target strength function constructed by current frames with a known new target strength function at previous moment so as to produce an adaptive new target strength function of the current frames; starting a PHD filter by using the adaptive new target strength function so as to complete the prediction and updating of the PHD filter; and carrying out multiple target state extraction. According to the Bayesian multiple target tracking method based on the filtering algorithm, a new target strength function is constructed only according to measurement information completely without using priori information. The algorithm can be used for solving the problem that an original PHD filter lacks a new target tracking function. The algorithm has the advantages that a new target of which the position is unknown can be tracked well, so that the application range is enlarged, the robustness of the PHD filter is improved, and the like.

Description

technical field [0001] The invention relates to an algorithm in the field of target tracking, in particular to a probability hypothesis density filtering algorithm based on adaptive new target strength. technical background [0002] The multi-target tracking problem has a very wide range of applications in both military and civilian applications, such as air early warning and air attack (multi-target attack) in the military, and multi-target tracking in civilian applications including air traffic control. The application in the military has been widely valued by various countries. The purpose of multi-target tracking (MTT) is to estimate the number and state of multiple targets from a measurement set containing clutter. The traditional multi-target tracking algorithm is based on the data association (Data Association) technology, which aims to decouple the multi-target tracking problem into multiple single-target tracking problems. However, there is combinatorial explosion...

AI Technical Summary

Problems solved by technology

Based on the literature, Ouyang Cheng et al. proposed a normalization factor correction method to improve the track normalization imbalance problem of the original algorithm, but this method still needs to know some prior information, and the method still remains after filtering. Contains a lot of clutter, weakening the main advantage of the PHD filter to remove clutter

Images