Multi-dimensional dual-blind deconvolution approach toward joint radar-communications

TL;DR

This paper introduces a novel joint radar-communications system approach that estimates unknown signals and channels simultaneously using dual-blind deconvolution and atomic norm minimization, enabling precise parameter recovery in complex scenarios.

Contribution

It presents the first dual-blind deconvolution framework for joint radar-communications, handling unknown signals and channels without prior estimates.

Findings

Accurate estimation of target parameters and messages demonstrated

Method effective in highly dynamic and passive scenarios

Numerical validation confirms robustness and precision

Abstract

We consider a joint multiple-antenna radar-communications system in a co-existence scenario. Contrary to conventional applications, wherein at least the radar waveform and communications channel are known or estimated \textit{a priori}, we investigate the case when the channels and transmit signals of both systems are unknown. In radar applications, this problem arises in multistatic or passive systems, where transmit signal is not known. Similarly, highly dynamic vehicular or mobile communications may render prior estimates of wireless channel unhelpful. In particular, the radar signal reflected-off multiple targets is overlaid with the multi-carrier communications signal. In order to extract the unknown continuous-valued target parameters (range, Doppler velocity, and direction-of-arrival) and communications messages, we formulate the problem as a sparse dual-blind deconvolution and solve it using atomic norm minimization. Numerical experiments validate our proposed approach and show that precise estimation of continuous-valued channel parameters, radar waveform, and communications messages is possible up to scaling ambiguities.