A New Maneuvering Frequency and the Variance Adaptive Filtering Algorithm

The standard adaptive Kalman filter algorithm based on the “current” statistical model (AF) has the problem of selecting Maneuvering frequency and maximum acceleration based on experience, and the problem of low accuracy in tracking non-maneuvering or weak maneuvering target. By analyzing the physical meaning of maneuvering frequency and relationship with acceleration, the maneuvering frequency adaptive algorithm are obtained. By analyzing the relationship between Kalman filtering innovation and acceleration variance, the acceleration variance adaptive algorithm are obtained. The contrasting simulation results of AF (Adaptive Filtering Algorithm) and MAF (Mending Adaptive Filtering algorithm) have showed MAF’s validity. MAF algorithm also obtains a better tracking accuracy, especially for non-maneuvering or weak maneuvering target, and makes “current” statistical model more conveniently for engineering application.