中文摘要：

针对非线性非高斯条件下目标跟踪容易发散和精度下降等问题，将容积粒子滤波引入到交互式多模型算法中，提出了一种基于容积粒子滤波的交互式多模型算法。该算法在粒子先验分布更新阶段，利用容积卡尔曼滤波器融入最新的观测数据并产生重要性密度函数，使其更加逼近系统真实状态的后验概率密度，改善了粒子滤波的性能。仿真表明在运算时间未显著变化的情况下，该算法与交互式多模型无迹粒子滤波算法相比有着更高的滤波精度和稳定性。

英文摘要：

To improve the low tracking accuracy and solve the divergence problem in target tracking under the nonlinear and non-Gaussian situation,an interacting multiple model algorithm is proposed based on cubature particle filter.The new algorithm utilizes cubature Kalman filter to incorporate the latest observation data and develop the importance density function,which is more close to the posterior density at the prior distribution up-dating phase,thus improving the particle filter performance.Simulation results show that,when compared with the interacting multiple model unscented particle filter,the proposed algorithm provides better filtering accuracy and stability while the average calculating time has no significant changes.