Designing Sequence with Minimum PSL Using Chebyshev Distance and its Application for Chaotic MIMO Radar Waveform Design

TL;DR

This paper introduces new cyclic algorithms for designing sequences with minimal peak side-lobe levels to improve autocorrelation properties in MIMO radar waveforms, demonstrating superior performance over traditional methods.

Contribution

It presents novel cyclic algorithms for PSL minimization and applies them to chaotic MIMO radar waveform design, enhancing autocorrelation performance.

Findings

Algorithms outperform traditional ISL minimization methods.

Fast-randomized SVD improves algorithm efficiency.

Numerical results confirm superior autocorrelation properties in MIMO radar applications.

Abstract

Controlling peak side-lobe level (PSL) is of great importance in high-resolution applications of multiple-input multiple-output (MIMO) radars. In this paper, designing sequences with good autocorrelation properties are studied. The PSL of the autocorrelation is regarded as the main merit and is optimized through newly introduced cyclic algorithms, namely; PSL Minimization Quadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular (PMAR), and PSL Optimization Cyclic Algorithm (POCA). It is revealed that minimizing PSL results in better sequences in terms of autocorrelation side-lobes when compared with traditional integrated side-lobe level (ISL) minimization. In order to improve the performance of these algorithms, fast-randomized Singular Value Decomposition (SVD) is utilized. To achieve waveform design for MIMO radars, this algorithm is applied to the waveform generated from a modified Bernoulli chaotic system. The numerical experiments confirm the superiority of the newly developed algorithms compared to high-performance algorithms in mono-static and MIMO radars.