Phase Code Discovery for Pulse Compression Radar: A Genetic Algorithm Approach

TL;DR

This paper introduces GASeq, a genetic algorithm that effectively discovers phase codes with low autocorrelation for pulse compression radar, outperforming existing methods especially at longer code lengths.

Contribution

The paper presents a novel genetic algorithm approach, GASeq, for designing phase codes with superior autocorrelation properties in pulse compression radar.

Findings

GASeq outperforms state-of-the-art codes at length 59 with SCR of 50.84.

GASeq discovers longer codes with SCR of 63.23 at length 100.

The approach is scalable and surpasses deep learning-based methods.

Abstract

Discovering sequences with desired properties has long been an interesting intellectual pursuit. In pulse compression radar (PCR), discovering phase codes with low aperiodic autocorrelations is essential for a good estimation performance. The design of phase code, however, is mathematically non-trivial as the aperiodic autocorrelation properties of a sequence are intractable to characterize. In this paper, we put forth a genetic algorithm (GA) approach to discover new phase codes for PCR with the mismatched filter (MMF) receiver. The developed GA, dubbed GASeq, discovers better phase codes than the state of the art. At a code length of 59, the sequence discovered by GASeq achieves a signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to search phase codes with a longer code length, which thwarts existing deep learning-based approaches. At a code length of 100, the best phase code discovered by GASeq exhibit an SCR of 63.23.