Tracking method for variable number of maneuvering target

Abstract

The present invention relates to a tracking method for a variable number of maneuvering targets. In a particle state prediction and update step, the sampling of a predicted particle state set is carried out according to a particle being variable, the association degree of a current observed value and maneuvering target state particles is considered, an association problem between an observed set and a maneuvering target state sampling particle set is solved by utilizing a fuzzy auction algorithm and a particle swarm optimization theory, a judging criterion for appearance and disappearance of the maneuvering targets is given, and the update of particle weight is realized; a hybrid sampling particle set is re-sampled by utilizing a sequential importance sampling theory, and a particle set containing model information and state information as well as approaching state posteriori probability distribution of each maneuvering target is obtained; the influence of the particle being variable is considered, and a maneuvering target local state posteriori estimated value and a mean-square error are obtained through particle state fusion according to a target model probability; and finally, local tracking information of sensors is subjected to weighting fusion, so as to obtain a global state estimated value of each maneuvering target, and realize accurate estimation of number and state variation of the maneuvering targets.

Description

technical field [0001] The invention belongs to the field of maneuvering target tracking, and relates to a new target joint detection and tracking method, in particular to a variable number maneuvering target tracking method. Background technique [0002] Modern air defense systems need to rely on complementary observation information provided by multiple sensors for detection, and the complex observation environment also puts forward higher and higher requirements for maneuvering target tracking algorithms. Most of the traditional detection-then-tracking algorithms first perform threshold detection on the raw data of each frame of the sensor, and then use the measurement data exceeding the threshold for tracking processing. Although this method reduces the amount of calculation to a certain extent, it loses a lot Useful information. In order to improve the performance of target detection and tracking, the energy accumulation of multi-frame raw observation data without thre...

AI Technical Summary

Problems solved by technology

The improvement methods of the classic multi-mode particle filter algorithm generally involve four categories: particle importance sampling, resampling, model particle number, and filtering framework. Although they solve the problem of particle diversity and algorithm calculation to a certain extent, most of them do not consider The tracking situation when the number of maneuvering targets changes in the actual multi-sensor observation environment, and ignores the determination of the association between the state particle set and the current observation value, which reduces the target tracking performance

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