Super-Resolution Delay-Doppler Estimation for OFDM Passive Radar

TL;DR

This paper introduces a super-resolution joint delay-Doppler estimation method for OFDM passive radar using a convex optimization approach that combines atomic norm and $ ext{l}_1$-norm, improving accuracy and robustness.

Contribution

It proposes a novel compressed sensing algorithm that leverages atomic norm and $ ext{l}_1$-norm for super-resolution delay-Doppler estimation in passive radar, explicitly accounting for demodulation errors.

Findings

High accuracy demonstrated in simulations

Effective handling of demodulation errors

Convex SDP approach with efficient ADMM implementation

Abstract

In this paper, we consider the problem of joint delay-Doppler estimation of moving targets in a passive radar that makes use of orthogonal frequency-division multiplexing (OFDM) communication signals. A compressed sensing algorithm is proposed to achieve supper-resolution and better accuracy, using both the atomic norm and the $\ell_1$-norm. The atomic norm is used to manifest the signal sparsity in the continuous domain. Unlike previous works which assume the demodulation to be error free, we explicitly introduce the demodulation error signal whose sparsity is imposed by the $\ell_1$-norm. On this basis, the delays and Doppler frequencies are estimated by solving a semidefinite program (SDP) which is convex. We also develop an iterative method for solving this SDP via the alternating direction method of multipliers (ADMM) where each iteration involves closed-form computation. Simulation results are presented to illustrate the high performance of the proposed algorithm.