基于中心差分卡尔曼-概率假设密度滤波的多目标跟踪方法

针对非线性系统模型,提出一种基于中心差分卡尔曼-概率假设密度滤波的多目标跟踪方法.该方法采用Stirling 内插公式对非线性函数作多项式逼近,利用中心差分卡尔曼滤波和高斯混合概率假设密度滤波对后验多目标状态一阶统计量进行估计,并通过递推更新得到目标状态,以实现对多个目标的跟踪.该方法无需求解系统函数的雅可比矩阵,且具有二阶泰勒展开式精度.仿真结果表明,所提出方法能够增强算法的鲁棒性,提高目标状态和数目的估计精度.

Central difference Kalman-probability hypothesis density filter for multitargettracking

Aiming at the nonlinear system model, a central difference Kalman-probability hypothesis density filter is
proposed to track multiple targets. Multi-target tracking is fulfilled by deriving polynomial approximations with Stirling
interpolation formulas, estimating first-order statistical moment of posterior multi-target states with central difference Kalman
filter and Gaussian mixture probability hypothesis density filter, and extracting targets’ states from the recursion of probability
hypothesis density. The advantage of proposed filter is mainly that Jacobian matrix solving is unnecessary and second-order
Taylor expansion accuracy can be ensured. Simulation results show that the robustness of the algorithm is enhanced, and the
estimating accuracy of the number and states of the targets are improved.