Truly SubNyquist Multicomponent Linear FM Signal Decomposition Method

TL;DR

This paper introduces a novel method for accurately extracting parameters of multicomponent linear FM signals at ultra-low sub-Nyquist sampling rates, addressing challenges of bandwidth and component interference.

Contribution

It presents the first direct, accurate extraction algorithm for multicomponent LFM signals at ultra-low sub-Nyquist rates, with optimized noise robustness and computational efficiency.

Findings

High accuracy in parameter extraction

Excellent noise immunity demonstrated

Faster computational speed compared to existing methods

Abstract

Accurate extraction of multicomponent linear frequency modulation (LFM) signal parameters, such as onset frequency, linear modulation frequency, amplitude, and initial phase, is of great importance in the fields of ISAR, cognitive radio, electronic countermeasures, and star-ground communications. However, the task of accurately extracting the characteristic parameters of a signal is challenging when it has an extraordinarily large bandwidth as well as cross or neighboring components in the time-frequency domain. In this paper, we first review the main current methods used for multicomponent LFM signal decomposition and their challenges, and then propose a novel multi-parameter feature parameter extraction algorithm. The algorithmrealizes the direct and accurate extraction of thefeature parameters of multicomponent LFM signals at ultra-low sub-Nyquist sampling rate for the first time. Moreover, the algorithm is optimized for the computational complexity and anti-noise problems in practical applications, so that it has high accuracy, high efficiency and good noise robustness. We also compare the algorithm with innovative and existing methods, and the results show that the algorithm has excellent performance in feature parameter extraction accuracy, noise immunity and computational speed.