UNIPOL: Unimodular sequence design via a separable iterative quartic polynomial optimization for active sensing systems

TL;DR

This paper introduces UNIPOL, an efficient algorithm for designing unimodular sequences with improved autocorrelation properties, using a novel separable quartic polynomial optimization approach that outperforms existing methods in speed and effectiveness.

Contribution

The paper presents a new polynomial optimization-based algorithm for unimodular sequence design, featuring a separable quartic majorization function and parallelizable implementation.

Findings

UNIPOL achieves faster convergence to optimal sequences.

The algorithm effectively minimizes the ISL metric.

It is computationally efficient using FFT and IFFT operations.

Abstract

Sequences having better autocorrelation properties play a crucial role in enhancing the performance of active sensing systems. Hence, sequences with good autocorrelation properties are very much in demand. In this paper, we addressed the problem of designing a unimodular sequence having better side-lobe levels. We formulated it as a constrained optimization problem comprising the Integrated Side-lobe Level (ISL) metric and then proposed an effective algorithm (named UNIPOL - UNImodular sequence design via a separable iterative POLynomial optimization) where we perform the polynomial optimization at every iteration. The novelty of the paper comes from deriving a quartic majorization function that is separable in the sequence variables and that can be minimized parallelly. To evaluate the performance of our proposed algorithm we perform the numerical experiments for different sequence lengths and confirm that our proposed algorithm is the fastest algorithm to attain an actual optimum minimizer of the ISL metric. Our proposed algorithm is also computationally efficient due to its ease of implementation using the FFT, IFFT operations in a parallel fashion.