Compressive Radar with Off-Grid Targets: A Perturbation Approach

TL;DR

This paper introduces a perturbation-based compressed sensing approach for radar imaging that effectively reduces off-grid target errors and enhances imaging accuracy using novel algorithms and multi-frequency waveforms.

Contribution

It develops a perturbation method to minimize gridding errors for off-grid targets and proposes algorithms like SCOMP and LOT to improve target detection and imaging accuracy.

Findings

Perturbation method reduces gridding error for off-grid targets.

Algorithms achieve accurate target localization under favorable conditions.

Numerical simulations demonstrate promising performance of the proposed approach.

Abstract

Compressed sensing (CS) schemes are proposed for monostatic as well as synthetic aperture radar (SAR) imaging with chirped signals and Ultra-Narrowband (UNB) continuous waveforms. In particular, a simple, perturbation method is developed to reduce the gridding error for off-grid targets. A coherence bound is obtained for the resulting measurement matrix. A greedy pursuit algorithm, Support-Constrained Orthogonal Matching Pursuit (SCOMP), is proposed to take advantage of the support constraint in the perturbation formulation and proved to have the capacity of determining the off-grid targets to the grid accuracy under favorable conditions. Alternatively, the Locally Optimized Thresholding (LOT) is proposed to enhance the performance of the CS method, Basis Pursuit (BP). For the advantages of higher signal-to-noise ratio and signal-to-interference ratio, it is proposed that Spotlight SAR imaging be implemented with CS techniques and multi-frequency UNB waveforms. Numerical simulations show promising results of the proposed approach and algorithms.