罗志才, 林旭, 周波阳. 自协方差最小二乘噪声估计的改进算法[J]. 武汉大学学报 ( 信息科学版), 2012, 37(10): 1164-1167.
引用本文: 罗志才, 林旭, 周波阳. 自协方差最小二乘噪声估计的改进算法[J]. 武汉大学学报 ( 信息科学版), 2012, 37(10): 1164-1167.
LUO Zhicai, LIN Xu, ZHOU Boyang. Improved Algorithm of Autocovariance Least-Squares Noise Estimation[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10): 1164-1167.
Citation: LUO Zhicai, LIN Xu, ZHOU Boyang. Improved Algorithm of Autocovariance Least-Squares Noise Estimation[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10): 1164-1167.

自协方差最小二乘噪声估计的改进算法

Improved Algorithm of Autocovariance Least-Squares Noise Estimation

  • 摘要: 针对现有自协方差最小二乘噪声估计结果非正定的问题,提出了一种能够有效克服数据长度不够以及先验信息不准的改进算法,保证噪声估计结果的正定性,从而提高自协方差最小二乘噪声估计的精度。数值仿真实验验证了该方法的正确性和有效性。

     

    Abstract: Noise estimation is the foundation for the application of Kalman filtering theory.The noise estimation results from the conventional autocovariance least-squares(ALS) are usually non-positive definite.For this purpose,an improved algorithm of ALS(IALS) is proposed to overcome effectively the problems of insufficient data and inaccurate priori information,and consequently to get positive definite noise estimates with better accuracy.Numerical simulation results validate the correctness of IALS.

     

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