An Efficient Method for Joint Delay-Doppler Estimation of Moving Targets in Passive Radar

TL;DR

This paper introduces a novel symmetric Gauss-Seidel based ADMM method for joint delay-Doppler estimation in passive radar, improving accuracy and efficiency over traditional ADMM methods.

Contribution

It proposes a convergence-guaranteed sGS-ADMM approach for atomic-norm regularized convex optimization in passive radar target detection.

Findings

sGS-ADMM outperforms ADMM in accuracy

sGS-ADMM reduces computation time

Method effectively estimates delay-Doppler parameters

Abstract

Passive radar systems can detect and track the moving targets of interest by exploiting non-cooperative illuminators-of-opportunity to transmit orthogonal frequency division multiplexing (OFDM) signals. These targets are searched using a bank of correlators tuned to the waveform corresponding to the given Doppler frequency shift and delay. In this paper, we study the problem of joint delay-Doppler estimation of moving targets in OFDM passive radar. This task of estimation is described as an atomic-norm regularized convex optimization problem, or equivalently, a semi-definite programming problem. The alternating direction method of multipliers (ADMM) can be employed which computes each variable in a Gauss-Seidel manner, but its convergence is lack of certificate. In this paper, we use a symmetric Gauss-Seidel (sGS) to the framework of ADMM, which only needs to compute some of the subproblems twice but has the ability to ensure convergence. We do some simulated experiments which illustrate that the sGS-ADMM is superior to ADMM in terms of accuracy and computing time.