一种基于最小二乘拟合的数据关联算法

doi:10.7527/S1000-6893.2015.0209

Abstract

Abstract:

Focusing on the hard problem of the balance between accuracy and real-time performance in multiple target tracking, a data association algorithm based on least square fitting method is proposed in this paper. Firstly, the tracking in sliding window is used to predict the next state by least square fitting respectively in different dimensions, which brings more history information to the attribution judgment. Then, cooperating with the prediction point of filter update, the next real position is judged by five defined probability events, which make the judgment of association more accurate. Finally, the state update equations and covariance are deduced in different events and the method to determine the parameters is given. The simulation results show that compared with the nearest neighbor algorithm and joint probabilistic data association algorithm, the proposed algorithm can be better in the balance of real-time and accuracy with low computational complexity, which is easy to implement in engineering practice.

Key words: least square, data association, target tracking, curve-fitting, information fusion

CLC Number:

V243.2
TP953

WANG Cong, WANG Haipeng, XIONG Wei, HE You. Data association algorithm based on least square fitting[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(5): 1603-1613.

References

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Data association algorithm based on least square fitting

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