Weighted adaptive filtering algorithm for carrier tracking of deep space signal

Abstract

Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system. For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio (SNR) is unknown. If the frequency-locked loop (FLL) or the phase-locked loop (PLL) with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused. Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence. Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio. The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process. In addition, the upper diagonal (UD) factorization and innovation adaptive control are used to reduce model estimation errors, suppress filter divergence and improve filtering accuracy. The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.