CLC number: TN713
On-line Access: 2021-08-17
Received: 2020-05-01
Revision Accepted: 2020-07-02
Crosschecked: 2021-07-14
Cited: 0
Clicked: 4740
Citations: Bibtex RefMan EndNote GB/T7714
Yun Zhu, Shuang Liang, Xiaojun Wu, Honghong Yang. A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000209 @article{title="A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain", %0 Journal Article TY - JOUR
基于随机有限集的非齐次马尔可夫链联合概率数据关联滤波器1陕西师范大学现代教学技术教育部重点实验室,中国西安市,710062 2陕西师范大学计算机科学学院,中国西安市,710119 3西安电子科技大学前沿交叉研究院,中国西安市,710071 摘要:提出一种启发式方法,通过随机有限集理论优化数据关联跟踪算法的后验密度。具体而言,提出一种改进的联合概率数据关联滤波方法,即最近邻集合联合概率数据关联方法(NNSJPDA)。为提高边缘化的准确性,利用一种基于Kullback–Leibler散度的最近邻方法,对所有可能的数据关联事件中的目标标签进行转换。此外,进一步考虑目标标签向量的分布。后验密度转换后,可得到目标标签向量的转移矩阵。该转移矩阵随时间变化,使得目标标签向量分布的传播遵循非齐次马尔可夫链。证明了该链本质上是双随机的,并推导了相应定理。通过举例和仿真,验证了所提方法的有效性。本文结果可推广到相同随机有限集框架下的其他数据关联方法。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Bar-Shalom Y, Tse E, 1975. Tracking in a cluttered environment with probabilistic data association. Automatica, 11(5):451-460. [2]Beard M, Reuter S, Granström K, et al., 2015. A generalised labelled multi-Bernoulli filter for extended multi-target tracking. Proc 18th Int Conf on Information Fusion, p.991-998. [3]Bloem EA, Blom HAP, 1995. Joint probabilistic data association methods avoiding track coalescence. Proc 34th IEEE Conf on Decision and Control, p. 2752-2757. [4]Fitzgerald RJ, 1985. Track biases and coalescence with probabilistic data association. IEEE Trans Aerosp Electron Syst, AES-21(6):822-825. [5]Fitzgerald RJ, 1990. Development of practical PDA logic for multitarget tracking by microprocessor. Proc American Control Conf, p.1-23. [6]Fortmann T, Bar-Shalom Y, Scheffe M, 1983. Sonar tracking of multiple targets using joint probabilistic data association. IEEE J Ocean Eng, 8(3):173-183. [7]Garcia-Fernandez AF, 2016. Track-before-detect labeled multi-Bernoulli particle filter with label switching. IEEE Trans Aerosp Electron Syst, 52(5):2123-2138. [8]Horn RA, Johnson CR, 1985. Matrix Analysis. Cambridge University Press, New York, USA. [9]Jing PL, Xu SY, Li X, et al., 2015. Coalescence-avoiding joint probabilistic data association based on bias removal. EURASIP J Adv Signal Process, 2015(1):24. [10]Kullback S, 1968. Information Theory and Statistics. Dover, New York, USA. [11]Li TC, Su JY, Liu W, et al., 2017. Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front Inform Technol Electron Eng, 18(12):1913-1939. [12]Li TC, Wang XX, Liang Y, et al., 2020. On arithmetic average fusion and its application for distributed multi-Bernoulli multitarget tracking. IEEE Trans Signal Process, 68:2883-2896. [13]Liang S, Zhu Y, Hao L, et al., 2019. Nearest-neighbour joint probabilistic data association filter based on random finite set. Proc 8th Int Conf on Control, Automation and Information Sciences, p.1-6. [14]Mahler RPS, 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans Aerosp Electron Syst, 39(4):1152-1178. [15]Mahler RPS, 2007a. PHD filters of higher order in target number. IEEE Trans Aerosp Electron Syst, 43(4):1523-1543. [16]Mahler RPS, 2007b. Statistical Multisource-Multitarget Information Fusion. Artech House, Boston, USA. [17]Panakkal VP, Velmurugan R, 2013. Effective joint probabilistic data association using maximum a posteriori estimates of target states. Proc 16th Int Conf on Information Fusion, p.781-788. [18]Reid D, 1979. An algorithm for tracking multiple targets. IEEE Trans Autom Contr, 24(6):843-854. [19]Reuter S, Wilking B, Wiest J, et al., 2013. Real-time multi-object tracking using random finite sets. IEEE Trans Aerosp Electron Syst, 49(4):2666-2678. [20]Reuter S, Vo BT, Vo BN, et al., 2014. The labeled multi-Bernoulli filter. IEEE Trans Signal Process, 62(12):3246-3260. [21]Schuhmacher D, Vo BT, Vo BN, 2008. A consistent metric for performance evaluation of multi-object filters. IEEE Trans Signal Process, 56(8):3447-3457. [22]Sidenbladh H, 2003. Multi-target particle filtering for the probability hypothesis density. Proc 6th Int Conf on Information Fusion, p.800-806. [23]Sidenbladh H, Wirkander SL, 2003. Tracking random sets of vehicles in terrain. Proc Conf on Computer Vision and Pattern Recognition Workshop, p.98. [24]Svensson D, Svensson L, Guerriero M, et al., 2011. The multi-target set JPDA filter with target identity. SPIE, 8050:805010. [25]Svensson L, Svensson D, Guerriero M, et al., 2011. Set JPDA filter for multitarget tracking. IEEE Trans Signal Process, 59(10):4677-4691. [26]Touri B, Nedić A, 2011. Alternative characterization of ergo-dicity for doubly stochastic chains. Proc 50th IEEE Conf on Decision and Control and European Control Conf, p.5371-5376. [27]Vo BN, Ma WK, 2006. The Gaussian mixture probability hypothesis density filter. IEEE Trans Signal Process, 54(11):4091-4104. [28]Vo BN, Vo BT, 2019. A multi-scan labeled random finite set model for multi-object state estimation. IEEE Trans Signal Process, 67(19):4948-4963. [29]Vo BN, Singh S, Doucet A, 2005. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Trans Aerosp Electron Syst, 41(4):1224-1245. [30]Vo BN, Vo BT, Phung D, 2014. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Trans Signal Process, 62(24):6554-6567. [31]Vo BT, Vo BN, 2013. Labeled random finite sets and multi-object conjugate priors. IEEE Trans Signal Process, 61(13):3460-3475. [32]Vo BT, Vo BN, Cantoni A, 2009. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans Signal Process, 57(2):409-423. [33]Williams JL, 2015. An efficient, variational approximation of the best fitting multi-Bernoulli filter. IEEE Trans Signal Process, 63(1):258-273. [34]Zajic T, Mahler RPS, 2003. Particle-systems implementation of the PHD multitarget-tracking filter. SPIE, 5096:291-299. [35]Zhu Y, Wang J, Liang S, 2017. Efficient joint probabilistic data association filter based on Kullback–Leibler divergence for multi-target tracking. IET Radar Sonar Navig, 11(10):1540-1548. [36]Zhu Y, Wang J, Liang S, 2019. Covariance control joint integrated probabilistic data association filter for multi-target tracking. IET Radar Sonar Navig, 13(4):584-592. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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