Sequential Maximum-Likelihood Estimation of Wideband Polynomial-Phase Signals on Sensor Array

TL;DR

This paper introduces a sequential maximum-likelihood estimator for wideband polynomial-phase signals on sensor arrays, effectively estimating source directions and polynomial coefficients even in complex multi-source scenarios.

Contribution

It proposes a novel sequential estimation method using RANSAC and array processing to improve computational efficiency and accuracy in wideband polynomial-phase signal analysis.

Findings

Achieves Cramér-Rao bounds in multi-source scenarios

Supports multiple sources and higher-order polynomials

Demonstrates effectiveness in closely spaced signals

Abstract

This paper presents a novel sequential estimator for the direction-of-arrival and polynomial coefficients of wideband polynomial-phase signals impinging on a sensor array. Addressing the computational challenges of Maximum-likelihood estimation for this problem, we propose a method leveraging random sampling consensus (RANSAC) applied to the time-frequency spatial signatures of sources. Our approach supports multiple sources and higher-order polynomials by employing coherent array processing and sequential approximations of the Maximum-likelihood cost function. We also propose a low-complexity variant that estimates source directions via angular domain random sampling. Numerical evaluations demonstrate that the proposed methods achieve Cram\'er-Rao bounds in challenging multi-source scenarios, including closely spaced time-frequency spatial signatures, highlighting their suitability for advanced radar signal processing applications.