Abstract
Under poor observation condition, a single-sensor self-tuning incremental Kalman filter is proposed for the incremental system with unknown model parameters, and its convergence is proved by the dynamic error system analysis (DESA) method. As an application in signal processing, three algorithms for estimating the parameters of autoregressive (AR) signal model based on incremental model are proposed for AR signals with white noise. Based on this, a self-tuning incremental Kalman filter for AR signals is proposed. A simulation example proves its effectiveness and feasibility.
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