Design of OFDM radar pulses using genetic algorithm based techniques

TL;DR

This paper presents a genetic algorithm-based framework for optimizing OFDM radar pulses, focusing on phase code sequences to improve radar performance metrics like PMEPR and PSLR, with multi-objective solutions and practical detection enhancements.

Contribution

It introduces a novel GA-based optimization approach for OFDM radar waveform design, addressing multiple criteria and incorporating constraints for improved radar signal performance.

Findings

GA optimization outperforms existing methods in PMEPR reduction.

Multi-objective NSGA-II provides better trade-offs for PMEPR and PSLR.

Encoding solutions enable simultaneous PMEPR minimization and detection enhancement.

Abstract

The merit of evolutionary algorithms (EA) to solve convex optimization problems is widely acknowledged. In this paper, a genetic algorithm (GA) optimization based waveform design framework is used to improve the features of radar pulses relying on the orthogonal frequency division multiplexing (OFDM) structure. Our optimization techniques focus on finding optimal phase code sequences for the OFDM signal. Several optimality criteria are used since we consider two different radar processing solutions which call either for single or multiple-objective optimizations. When minimization of the so-called peak-to-mean envelope power ratio (PMEPR) single-objective is tackled, we compare our findings with existing methods and emphasize on the merit of our approach. In the scope of the two-objective optimization, we first address PMEPR and peak-to-sidelobe level ratio (PSLR) and show that our approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) provides design solutions with noticeable improvements as opposed to random sets of phase codes. We then look at another case of interest where the objective functions are two measures of the sidelobe level, namely PSLR and the integrated-sidelobe level ratio (ISLR) and propose to modify the NSGA-II to include a constrain on the PMEPR instead. In the last part, we illustrate via a case study how our encoding solution makes it possible to minimize the single objective PMEPR while enabling a target detection enhancement strategy, when the SNR metric would be chosen for the detection framework.