摘要
本文应用自适应估计理论,提出了一种指数渐消因子自适应算法。该算法通过实测残差与理论残差的比值来确定指数方程的系数,调节自适应渐消因子,保证了滤波的稳定性,提高了滤波精度,并且冲破了经验储备系数的限制。最后对比其他三种自适应滤波算法进行了仿真比较,仿真结果表明,指数渐消因子自适应滤波算法是一种实用而有效的算法。
The standard Kalman filter requires an accurate mathematical model whose system noise and measuring noise are white and no-correlated noise. However, it is difficult to establish an accurate mathematical model as well as most of the statistical characteristics of noise are unknown.. This paper proposes an exponent fading factor adaptive algorithm based on the adaptive estimation. The algorithm confirms exponent equation＇s coefficient by calculating the ratio of the real value and the theoretical value of residual covariance, accommodates the fading factor and ensures the filter＇s stability and precision. What＇s more, it breaks the restriction of the experiential reserve coefficient. The algorithm is compared with other three algorithms. Results show that the algorithm is a filtering method which is highly adaptive and effective.
出处
《电子测量技术》
2010年第1期40-42,共3页
Electronic Measurement Technology
关键词
卡尔曼滤波
自适应卡尔曼滤波
指数渐消因子
储备系数
Kalman filtering
adaptive Kalman filtering
exponent fading factor
experiential reserve coefficient
