An independent sequential maximum likelihood approach to simultaneous track-to-track association and bias removal

Qiong Song; Yuehuan Wang; Xiaoyun Yan; Dang Liu

doi:10.1117/12.2205507

14 December 2015 An independent sequential maximum likelihood approach to simultaneous track-to-track association and bias removal

Qiong Song, Yuehuan Wang, Xiaoyun Yan, Dang Liu

Author Affiliations +

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981308 (2015) https://doi.org/10.1117/12.2205507
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

In this paper we propose an independent sequential maximum likelihood approach to address the joint track-to-track association and bias removal in multi-sensor information fusion systems. First, we enumerate all kinds of association situation following by estimating a bias for each association. Then we calculate the likelihood of each association after bias compensated. Finally we choose the maximum likelihood of all association situations as the association result and the corresponding bias estimation is the registration result. Considering the high false alarm and interference, we adopt the independent sequential association to calculate the likelihood. Simulation results show that our proposed method can give out the right association results and it can estimate the bias precisely simultaneously for small number of targets in multi-sensor fusion system.

Citation Download Citation

Qiong Song, Yuehuan Wang, Xiaoyun Yan, and Dang Liu "An independent sequential maximum likelihood approach to simultaneous track-to-track association and bias removal", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981308 (14 December 2015); https://doi.org/10.1117/12.2205507

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