TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

TL;DR

TenDSuR introduces a tensor-based sub-Nyquist radar system that efficiently estimates target parameters with fewer measurements while maintaining high resolution, leveraging tensor compressed sensing and completion techniques.

Contribution

The paper presents a novel tensor-based radar framework that enables simultaneous high-resolution target parameter estimation at sub-Nyquist sampling rates, improving measurement efficiency.

Findings

Achieves joint estimation of target parameters at native resolutions with fewer measurements.

Tensor completion enhances off-grid target recovery performance.

Demonstrates effectiveness through numerical experiments comparing tensor-OMP and tensor completion.

Abstract

We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-OMP and tensor completion algorithms for signal recovery. Our numerical experiments demonstrate joint estimation of all three target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid target recovery with respect to tensor-OMP.