基于星凸随机超曲面的扩展目标伽马高斯混合势概率假设密度滤波器
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摘要
针对杂波和检测不确定情况下扩展目标形状估计精度低的问题,提出了一种基于星凸随机超曲面模型(SRHM)的扩展目标伽马高斯混合势概率假设密度(CPHD)滤波器.该算法在高斯混合概率假设密度滤波的框架下,首先将目标形状建模为星凸随机超曲面,然后通过CPHD滤波估计出目标的质心位置和目标数目,最后通过将已估计的目标质心位置作为目标形状的中心点来结合量测对目标形状进行估计.其中,算法通过自适应估计尺度变换因子对形状边界进行约束优化,解决了星凸随机超曲面模型存在的边界形状不规则的问题.设计扩展目标个数未知以及含有杂波的实验场景,实验结果验证了该算法的有效性和可行性.
In view of the low accuracy of shape estimation in multiple extended target tracking under the circumstances of clutter and detection uncertainty, a Gamma Gaussian-mixture cardinalized probability hypothesis density(CPHD) filter for extended target tracking, which is based on star-convex random hypersurface model(SRHM), is proposed. Firstly, under the Gaussian-mixture CPHD filter framework, the proposed algorithm models the shape of the target as star-convex random hypersurface model. The CPHD filter is then used to estimate the centroid position and the number of targets. Finally, after taking the estimated centroid position as the center of the target's shape, the algorithm estimates the shape of the target through the use of the acquired measurements. The problem of irregular shape boundary, which exists in the star-convex random hypersurface model is solved by adaptively estimating the scaling factor and applying the constrained optimization method to optimize shape boundary in this algorithm. The simulation in clutter environment with the unknown number of extended target validates the effectiveness and feasibility of the proposed algorithm.
In view of the low accuracy of shape estimation in multiple extended target tracking under the circumstances of clutter and detection uncertainty, a Gamma Gaussian-mixture cardinalized probability hypothesis density(CPHD) filter for extended target tracking, which is based on star-convex random hypersurface model(SRHM), is proposed. Firstly, under the Gaussian-mixture CPHD filter framework, the proposed algorithm models the shape of the target as star-convex random hypersurface model. The CPHD filter is then used to estimate the centroid position and the number of targets. Finally, after taking the estimated centroid position as the center of the target's shape, the algorithm estimates the shape of the target through the use of the acquired measurements. The problem of irregular shape boundary, which exists in the star-convex random hypersurface model is solved by adaptively estimating the scaling factor and applying the constrained optimization method to optimize shape boundary in this algorithm. The simulation in clutter environment with the unknown number of extended target validates the effectiveness and feasibility of the proposed algorithm.
引文
[1] HU Q, JI H B, ZHANG Y Q. Tracking of maneuvering nonellipsoidal extended target with varying number of sub-objects. Mechanical Systems and Signal Processing, 2018, 99(1):262–284.
[2] LAN J, LI X R. Tracking of extended object or target group using random matrix:New model and approach. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6):2973–2989.
[3] BEARD M, REUTER S, GRANSTR¨OM K, et al. Multiple extended target tracking with labeled random finite sets. IEEE Transactions on Signal Processing, 2016, 64(7):1638–1653.
[4] ZEA A, FAION F, BAUM M, et al. Level-set random hypersurface models for tracking nonconvex extended objects. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6):2990–3007.
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[6] GRANSTR¨OM K, ORGUMER U. A PHD filter for tracking multiple extended targets using random matrices. IEEE Transactions on Signal Processing, 2012, 60(11):5657–5671.
[7] LI Cuiyun, LIN Jinpeng, JI Hongbing. A Gamma Gaussian mixture CPHD filter for extended target tracking based on ellipse random hypersurface models. Control and Decision, 2015, 30(9):1551–1558.(李翠芸,林锦鹏,姬红兵.一种基于椭圆RHM的扩展目标Gamma高斯混合CPHD滤波器.控制与决策, 2015, 30(9):1551–1558.)
[8] BAUM M, HANEBECK U D. Shape tracking of extended objects and group targets with star-convex RHMs. Proceedings of the 14th International Conference on Information Fusion. Chicago:IEEE, 2011:1–8.
[9] WEI S, FENG X X, KONG Y B. CBMeMBer filter for extended object tracking using box particle. Proceedings of IEEE International Conference on Computer Communication and the Internet. Wuhan:IEEE, 2016:454–458.
[10] LAN J, LI X R. State estimation with nonlinear inequality constraints based on unscented transformation. Proceedings of the 14th International Conference on Information Fusion. Chicago:IEEE, 2011:1–8.
[11] LI Cuiyun, WANG Rong, JI Hongbing. Multiple extended-target tracking based on variational Bayesian cardinality-balanced multitarget multi-Bernoulli. Control Theory&Applications, 2015, 32(2):187–195.(李翠芸,王荣,姬红兵.基于变分贝叶斯势均衡多目标多伯努利滤波的多扩展目标跟踪算法.控制理论与应用, 2015, 32(2):187–195.)
[12] ZHANG Guanghua, LIAN Feng, HAN Chongzhao, WANG Tingting.Convergence analysis for the Gaussian mixture implementation of the CBMeMBer filter. Control Theory&Applications, 2016, 33(10):1405–1411.(张光华,连峰,韩崇昭,王婷婷.一种势均衡多目标多伯努利滤波高斯混合实现的收敛性分析.控制理论与应用, 2016, 33(10):1405–1411.)
[13] CHEN Hui, DU Jinrui, HAN Chongzhao. An adaptive tracking algorithm for irregular shape extended target. Control Theory&Applications, 2018, 35(8):1111–1119.(陈辉,杜金瑞,韩崇昭.一种自适应不规则形状扩展目标跟踪算法.控制理论与应用, 2018, 35(8):1111–1119.)
[2] LAN J, LI X R. Tracking of extended object or target group using random matrix:New model and approach. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6):2973–2989.
[3] BEARD M, REUTER S, GRANSTR¨OM K, et al. Multiple extended target tracking with labeled random finite sets. IEEE Transactions on Signal Processing, 2016, 64(7):1638–1653.
[4] ZEA A, FAION F, BAUM M, et al. Level-set random hypersurface models for tracking nonconvex extended objects. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6):2990–3007.
[5] KOCH J W. Bayesian approach to extended object and cluster tracking using random matrices. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3):1042–1059.
[6] GRANSTR¨OM K, ORGUMER U. A PHD filter for tracking multiple extended targets using random matrices. IEEE Transactions on Signal Processing, 2012, 60(11):5657–5671.
[7] LI Cuiyun, LIN Jinpeng, JI Hongbing. A Gamma Gaussian mixture CPHD filter for extended target tracking based on ellipse random hypersurface models. Control and Decision, 2015, 30(9):1551–1558.(李翠芸,林锦鹏,姬红兵.一种基于椭圆RHM的扩展目标Gamma高斯混合CPHD滤波器.控制与决策, 2015, 30(9):1551–1558.)
[8] BAUM M, HANEBECK U D. Shape tracking of extended objects and group targets with star-convex RHMs. Proceedings of the 14th International Conference on Information Fusion. Chicago:IEEE, 2011:1–8.
[9] WEI S, FENG X X, KONG Y B. CBMeMBer filter for extended object tracking using box particle. Proceedings of IEEE International Conference on Computer Communication and the Internet. Wuhan:IEEE, 2016:454–458.
[10] LAN J, LI X R. State estimation with nonlinear inequality constraints based on unscented transformation. Proceedings of the 14th International Conference on Information Fusion. Chicago:IEEE, 2011:1–8.
[11] LI Cuiyun, WANG Rong, JI Hongbing. Multiple extended-target tracking based on variational Bayesian cardinality-balanced multitarget multi-Bernoulli. Control Theory&Applications, 2015, 32(2):187–195.(李翠芸,王荣,姬红兵.基于变分贝叶斯势均衡多目标多伯努利滤波的多扩展目标跟踪算法.控制理论与应用, 2015, 32(2):187–195.)
[12] ZHANG Guanghua, LIAN Feng, HAN Chongzhao, WANG Tingting.Convergence analysis for the Gaussian mixture implementation of the CBMeMBer filter. Control Theory&Applications, 2016, 33(10):1405–1411.(张光华,连峰,韩崇昭,王婷婷.一种势均衡多目标多伯努利滤波高斯混合实现的收敛性分析.控制理论与应用, 2016, 33(10):1405–1411.)
[13] CHEN Hui, DU Jinrui, HAN Chongzhao. An adaptive tracking algorithm for irregular shape extended target. Control Theory&Applications, 2018, 35(8):1111–1119.(陈辉,杜金瑞,韩崇昭.一种自适应不规则形状扩展目标跟踪算法.控制理论与应用, 2018, 35(8):1111–1119.)