Resilient Interference Mitigation with Adaptive FLL-based Adaptive Notch Filtering

Abstract

Adaptive notch filters (ANFs) have shown good interference mitigation capabilities against chirp interferences. However, the chirp dynamics and jump discontinuities in frequency limit ideal ANF tuning for all chirp types. An ANF removes a spectrally sparse interference through a notch filter, and an adaptive algorithm determines the center frequency of the chirp as it changes over time. One adaptive algorithm approach uses a conventional frequency-locked loop (FLL) with fixed settings to follow the chirp frequency. A critical tuning parameter of the FLL is the loop-bandwidth, which is a trade-off between the agility and noise suppression of the FLL. On the one hand, large bandwidth adapts quickly and is suitable for addressing the chirp’s jump-discontinuities but results in noisy estimates. On the other hand, a small bandwidth responds slow but results in more precise tracking. This paper applies adaptive loop techniques commonly deployed in adaptive scalar tracking loops (ASTLs) to alter the loop-bandwidth of the FLL. As a result, the adaptive FLL-ANF (AFLL-ANF) responds much better against jump discontinuities by temporarily increasing the loop-bandwidth before settling back to a smaller loop-bandwidth once it is on the chirp signal. In recent publications, the loop-bandwidth control algorithm (LBCA) has shown promising results for adapting tracking global navigation satellite system (GNSS) receiver tracking loops, and in this paper, it is applied to a new application for FLL-based notch filters. Initial results indicate improved tracking errors compared to fixed-tuned FLL-ANFs and superior alpha-mean values.