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Convolution Kernels Implementation of Cardinalized Probability Hypothesis Density Filter
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作者 Yue MA Jian-zhang ZHU +1 位作者 Qian-qing QIN Yi-jun HU 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2013年第4期739-748,共10页
The probability hypothesis density (PHD) propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD (CPHD) recursion is a generalization of P... The probability hypothesis density (PHD) propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD (CPHD) recursion is a generalization of PHD recursion, which jointly propagates the posterior intensity function and posterior cardinality distribution. A number of sequential Monte Carlo (SMC) implementations of PHD and CPHD filters (also known as SMC- PHD and SMC-CPHD filters, respectively) for general non-linear non-Gaussian models have been proposed. However, these approaches encounter the limitations when the observation variable is analytically unknown or the observation noise is null or too small. In this paper, we propose a convolution kernel approach in the SMC-CPHD filter. The simuIation results show the performance of the proposed filter on several simulated case studies when compared to the SMC-CPHD filter. 展开更多
关键词 random finite set (RFS) probability hypothesis density (PHD) filter cardinalized probability hypothesis density (CPHD) filter convolution kernel
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