Rao-Blackwellized粒子势均衡多目标多伯努利滤波器
Rao-Blackwellized particle cardinality balanced multi-target multi-Bernoulli filter

由于多伯努利滤波器直接近似递推了多目标状态的后验概率密度, 使得多目标跟踪问题在基于随机有限 集理论框架下的求解及目标状态的估计显得更为直观. 本文针对一个状态可分解(线性/非线性)的状态空间模型, 分 析基于Rao-Blackwell定理的滤波估计方法, 结合噪声的去相关构造线性状态的滤波方程. 文中详细推导并提 出Rao-Blackwellized粒子势均衡多目标多伯努利滤波器的一般实现形式, 包括给出多伯努利非线性状态粒子滤波 的实现形式, 并结合非线性滤波结果给出多伯努利线性状态的递推滤波公式. 本文提出的滤波器实现方法能够在 更低维的状态空间上进行采样, 滤波器的整体跟踪性能得到提高. 多目标跟踪的仿真实验结果验证了该算法的有 效性.
The multi-Bernoulli filter propagates approximately the multi-target posterior density so that solving target tracking problem and extracting target state based on random finite set are more tractable. Considering a state space model whose state can be divided into linear and nonlinear part, this paper analyzes the Rao-Blackwell theorem based filtering algorithm. Then, using the corresponding algorithm of decorrelation of state noises, we presents the filtering formula for linear state. Moreover, this paper proposes a Rao-Blackwellized particle cardinality balanced multi-target multi-Bernoulli filter. This algorithm firstly implements the particle filtering for multi-Bernoulli nonlinear state, and the filtering formula of multi-Bernoulli linear state is derived afterwards based on the nonlinear filtering result. The proposed filter can sample particle in a lower dimensional state space and improve the overall target tracking performance. The simulation results of the multi-target tracking show the effectiveness of the proposed approach.