Adaptive Kalman Filter for Navigation Sensor Fusion
Dah‐Jing Jwo, Fong-Chi Chung, Tsu-Pin Weng · Sciyo eBooks · 2010
This chapter presents the adaptive Kalman filter for navigation sensor fusion. Several types of adaptive Kalman filters has been reviewed, including the innovation-based adaptive estimation (IAE) approach and the adaptive fading Kalman filter (AFKF) approach. Various types of designs for the fading factors are discussed. A new strategy through the hybridization of IAE and AFKF is presented with an illustrative example for integrated navigation application. In the first example, the fuzzy logic is employed for assisting the AFKF. Through the use of fuzzy logic, the designed fuzzy logic adaptive system (FLAS) has been employed as a mechanism for timely detecting the dynamical changes and implementing the on-line tuning of threshold c , and accordingly the fading factor, by monitoring the innovation information so as to maintain good tracking capability. In the second example, the conventional KF approach is coupled by the adaptive tuning system (ATS), which gives two system parameters: the fading factor and measurement noise covariance scaling factor. The ATS has been employed as a mechanism for timely detecting the dynamical and environmental changes and implementing the on-line parameter tuning by monitoring the innovation information so as to maintain good tracking capability and estimation accuracy. Unlike some of the AKF methods, the proposed method has the merits of good computational efficiency and numerical stability. The matrices in the KF loop are able to remain positive definitive. Remarks to be noted for using the method is made, such as: (1) The window sizes can be set different, to avoid the filter degradation/divergence; (2) The fading factors (λ P )ii should be always larger than one while (λ R ) jj does not have such limitation. Simulation experiments for navigation sensor fusion have been provided to illustrate the accessibility. The accuracy improvement based on the AKF method has demonstrated remarkable improvement in both navigational accuracy and tracking capability.