Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform

TL;DR

This paper proposes a sparse coding Fourier transform method for UAV swarms that enables efficient wideband spectrum acquisition with lower power consumption and improved performance in noisy environments.

Contribution

It introduces a novel sparse coding Fourier transform technique tailored for UAV swarms, addressing collision and non-sparsity issues in spectrum estimation.

Findings

Effective acquisition of narrowband and wideband signals simultaneously

Lower power consumption and higher compression rates achieved

Robust performance in low signal-to-noise ratio conditions

Abstract

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods.