Signal frequency and bandwidth estimation using a learned filter pair response

Abstract

Systems and methods for estimating frequency and bandwidth of unknown signals using learned features of a filter pair for the purpose of detecting, separating and tracking these signals in an electronic receiver. The techniques could be part of a signal cueing system that initiates signal detection, separation and tracking or a signal separation and tracking system which is initialized by the cueing system and adaptively updates frequency and bandwidth estimates so as to continuously separate and track signals after initial detection. The methodology is to train the filter responses using machine learning by creating a grid of training data based on signal examples that cover a span of frequencies and bandwidths. The system estimates frequency and bandwidth in real time, inputs those estimates into interpolating lookup tables to retrieve filter coefficients, and provides those filter coefficients to a tunable tracking filter.