针对非线性系统噪声未知时粒子滤波容易发散或者精度下降的问题,提出一种粒子滤波和改进的Sage-Husa估计器相结合的混合滤波算法。首先用粒子滤波对系统状态进行初步估计,将初步估计值作为次级Sage-Husa滤波器的输入量测值,并与系统状态方程组成新的系统,进而用改进的Sage-Husa算法实时估计系统噪声的统计特性并进行滤波,得到最终的系统状态估计值;为了进一步比较算法的性能,对算法的复杂度进行了定量计算,分析表明优化的算法并未明显提高算法的计算量;最后通过目标跟踪仿真实验验证了算法的有效性。

To solve the filtering accuracy reduction and divergence problems in the nonlinear system when the noise characteristics are unknown,a hybrid filter composed of Sage-Husa filter and particle filter is proposed.Firstly,a preliminary estimate of the state variables is provided by particle filter,which is then taken as the input measurement value of the secondary filter,and forms a new system with the state equation.After that,the modified Sage-Husa filter is used for estimating the statistic property of system noise in real time,and the final system state estimated value is obtained.The calculation complexity of the algorithms is calculated out quantitatively to compare the algorithms performance further,the result shows that the calculation complexity keeps unchanged in new algorithm.Finally,target tracking simulation results demonstrated the availability of the new algorithm.