CAJ | 학술논문

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.
침대비선성계통모형,제출일충기우중심차분잡이만-개솔가설밀도려파적다목표근종방법.해방법채용Stirling 내삽공식대비선성함수작다항식핍근,이용중심차분잡이만려파화고사혼합개솔가설밀도려파대후험다목표상태일계통계량진행고계,병통과체추경신득도목표상태,이실현대다개목표적근종.해방법무수구해계통함수적아가비구진,차구유이계태륵전개식정도.방진결과표명,소제출방법능구증강산법적로봉성,제고목표상태화수목적고계정도.
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.