Wang Haipeng

A Parallel Centralized Multi-sensor General Association Algorithm

This paper has researched on the problem of centralized multi-sensor multi-target tracking against a background, which has a high demand of real-time and a relative demand of tracking precision, and analyzed the advantages and disadvantages of the existent classical algorithms theoretically in this environment. Based on the research and the analysis, general association algorithm has been extended into multi-senor system through the parallel structure and a new algorithm named parallel centralized multi-sensor general association algorithm has been proposed. In this algorithm, a lot of hypotheses are built with the measurements of each sensor and the track of each target, the score of every hypothesis could be obtained with the formula of the score function in multi-sensor general association algorithm, and the state estimation of the fusion center is gained finally. The simulation results show that this algorithm could track the targets effectively with the high density clutter and the general performance of this algorithm is better than that of sequential centralized multi-sensor joint probabilistic data association algorithm and parallel centralized multi-sensor probabilistic nearest neighbor standard function.

A group target track correlation algorithm based on systematic error estimation

Aiming to solve the track refined correlation problem of the group targets with systematic errors, based on the characteristics of the group tracks, an algorithm of track refined correlation with in group targets based on systematic error compensation is proposed with the error estimation technique and the track correlation technique. In this algorithm, the groups are identified with the tracks of the sensors based on the circulatory threshold model firstly. Secondly, the preparatory correlation groups with the nearest resolving state are searched or established based on group track state identification model. Thirdly, the final systematic errors are estimated with group track systematic error estimation model and error validation model. What is more, the error compensation is completed. Finally, group track refined correlation is done with the traditional track correlation algorithms. The analysis results of the simulation data show that the general performance of this algorithm, which can meet the engineering requirement of the track refined correlation of the group targets with systematic errors very well, is better than that of fuzzy track alignment-correlation algorithm based on target topological information, track alignment-correlation algorithm based on iterative closest track and modified weighted track correlation algorithm.