Self-recovering extended Kalman filter for frequency tracking

Abstract

This paper proposes a new nonlinear filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKF's failure or abnormal operation is automatically diagnosed. When the failure is diagnosed, an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated. Using the output of the nonlinear FIR filter, the EKF is reset and rebooted. In this way, the SREKF can self-recover from failures. The SREKF is applied to a frequency tracking problem for demonstration of its effectiveness.