相关噪声下非线性系统状态与偏差的分离估计算法

将用于零均值、高斯白噪声干扰下的非线性时变随机系统的伪偏差分离估计算法推广到 了系统及测量噪声为非零均值高斯白噪声、系统噪声及测量噪声为相关噪声的情形.通过引 入"弱化因子"概念,使得状态和偏差估计更加平滑.最后通过数字仿真证实了该方法的有效 性.同扩展卡尔曼滤波器相比,其计算量小,且可以准确估计出时变规律未知的随机时变偏 差.

A Separate Blas and State Estimation Algorithm for Nonlinear System with Correlated Noise

The estimation algorithm of pseudo-separate bias and state for nonlinear timevarying stochastic system with zero mean, Gaussian white noise disturbance is extended to the case of the system with nonzero mean and correlated noise disturbance.By using "weakening factor", smoother estimation value curve of the states and the bias can be gotten. Finally, simulation result is presented to verify the effectiveness of the new approach. It shows that, compared with Extended Kalman. Filter, the computation amount of the new algorithm is much less, and the stochastic timevarying bias of the system can be estimated exactly.