摘要
对于带未知噪声方差的线性离散定常随机系统,引入左素分解可得到一个新的观测过程,它用两个滑动平均(MA)过程之和表示。用解相关函数矩阵方程组得到了噪声方差Q和R的估值器,进而基于新的观测过程的采样相关函数及其遍历性可得到噪声方差Q和R的强一致估计。算法简单,便于实时应用。一个目标跟踪系统的仿真例子说明了其有效性。
For the linear discrete time-invariant stochastic systems with unknown variances, by introducing a left coprime decomposition, a new measurement process is obtained, which is described by the sum of two moving average （MA） process. The estimators of the noise variances Q and R are obtained by solving the matrix equations for correlation function, and based on the sampled correlation function of the new measurement process and its ergodicity, the strong consistent estimators of the noise variances Q and R are obtained. The algorithm is simple, and they are suitable for real time applications. A simulation example for a target tracking system shows their effectiveness.
出处
《科学技术与工程》
2006年第14期2008-2011,共4页
Science Technology and Engineering
基金
国家自然科学基金(60374026)
黑龙江大学自动控制重点实验室基金资助
关键词
辨识
噪声方差估值器
收敛性
一致性
相关方法
identification noise variance estimator convergence consistent correlation method
