A novel algorithm of linear adaptive square-root Kalman Filtering based on Sage-Husa

作者：Zhou Yong^*; Zhang Yufeng; Zhang Chao; Zhang Juzhong

来源：Journal of Northwestern Polytechnical University, 2013, 31(1): 89-93.

• Adaptive filtering algorithms
• Kalman-filtering
• Noise covariance matrix
• Novel algorithm
• Square roots
• Square-root filtering
• Standard Kalman filters
• System noise covariance matrix

Aiming at the flaws of the standard Kalman Filter (KF) and Extended Kalman Filter (EKF), and based on the square-root filtering algorithm, we modify traditional Sage-Husa adaptive filter and present a novel algorithm of Linear Adaptive Square-Root Kalman Filtering (LASRKF) in this paper. With this new filter, the square root of system state covariance matrix is calculated recursively and the estimation of the square root of the system noise covariance matrix is obtained straightforwardly. Then the positive semi-definiteness of system state and noise covariance matrix are guaranteed; the stability and the adaptability of filter are also enhanced. Compared with the traditional Sage-Husa adaptive filtering algorithm, LASRKF algorithm improves the anti-divergence capability. Simulation results show preliminarily that the stability, accuracy and adaptability of the filter are improved greatly.

• 单位
  西北工业大学