Bayes Information-theoretic Radar Waveform Design and Delay-Doppler Resolution for Extended Targets

TL;DR

This paper introduces an information-theoretic approach to radar waveform design for extended targets, optimizing mutual information to improve target detection and resolution, with comparisons to traditional Barker codes.

Contribution

It presents a novel iterative design method for waveforms based on maximizing mutual information and derives SNR-based waveforms for active sensing.

Findings

Information-theoretic waveforms have sharp main lobes in their ambiguity functions.

These waveforms exhibit excellent time autocorrelation properties.

Compared to Barker codes, the designed waveforms show improved resolution characteristics.

Abstract

In this paper, we consider the problem of information-theoretic waveform design for active sensing systems such as radar for extended targets. Contrary to the popular formulation of the problem in the estimation-theoretic context, we are rather interested in a Bayes decision theoretic approach where a target present in the environment belongs to two or more classes whose priors are known. Optimal information theory based transmit waveforms are designed by maximizing mutual information (MI) between the received signal and the target impulse response, resulting in a novel iterative design equation. We also derive signal to noise ratio (SNR) maximization based waveforms. In an effort to quantize the benefits of such a design approach, the delay-Doppler ambiguity function of information-theoretic waveforms are presented and is compared with Barker codes of similar time-bandwidth product. It is found that the ambiguity function of information-theoretic waveforms has very sharp main lobe in general and excellent time autocorrelation properties in particular.