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为了提高强环境噪声下非线性系统估计性能,基于粒子流滤波对非线性系统估计能力强的特点,文中首先利用粒子流滤波粗估计状态向量;然后,利用卡尔曼滤波平滑由强环境噪声所导致的状态向量估计误差;最后,得到混合粒子流滤波算法。对转移方程为线性而测量方程为非线性的系统估计仿真实验表明:文中算法的参数估计精度高于普通粒子流滤波算法和粒子滤波算法,计算复杂度和普通粒子流滤波算法相当且低于粒子滤波算法。
In order to improve the estimation performance of nonlinear systems under strong ambient noise, based on the characteristics of particle filter for nonlinear system estimation, the particle filter is firstly used to estimate the state vector. Then, the Kalman filter is used to smooth the noise caused by strong ambient noise State vector estimation error; Finally, the hybrid particle filter algorithm is obtained. The simulation results of the system estimation that the transfer equation is linear and the measurement equation is nonlinear show that the proposed algorithm has higher estimation accuracy than the ordinary particle filter and particle filter, Filtering algorithm.