Radar Information Theory for Joint Communication and Parameter Estimation with Passive Targets

TL;DR

This paper establishes information theoretic bounds for joint radar and communication systems with semi-passive targets, proposing waveform optimization and analyzing data rate limits for enhanced target parameter estimation and communication.

Contribution

It introduces a novel framework combining information theory and waveform optimization for joint radar and communication with passive targets.

Findings

Derived CRBs for target parameter estimation accuracy.

Proposed waveform optimization using target ambiguity functions.

Established lower bounds on data rates for joint systems.

Abstract

In this paper, we derive the information theoretic performance bounds on communication data rates and errors in parameter estimation, for a joint radar and communication (JRC) system. We assume that targets are semi-passive, i.e. they use active components for signal reception, and passive components to communicate their own information. Specifically, we let the targets to have control over their passive reflectors in order to transmit their own information back to the radar via reflection-based beamforming or backscattering. We derive the Cramer-Rao lower bounds (CRBs) for the mean squared error in the estimation of target parameters. The concept of a target ambiguity function (TAF) arises naturally in the derivation CRBs. Using these TAFs as cost function, we propose a waveform optimization technique based on calculus of variations. Further, we derive lower bounds on the data rates for communication on forward and reverse channels, in radar-only and joint radar and communications scenarios. Through numerical examples, we demonstrate the utility of this framework for transmit waveform design, codebook construction, and establishing the corresponding data rate bounds.