Newtonalized Orthogonal Matching Pursuit for Linear Frequency Modulated Pulse Frequency Agile Radar

TL;DR

This paper introduces NOMP-FAR, a novel algorithm for processing linear frequency modulated radar signals, enabling accurate target detection and velocity estimation while suppressing false alarms, thus enhancing radar resolution and robustness.

Contribution

The paper develops NOMP-FAR, adapting Newtonalized orthogonal matching pursuit for frequency agile radar, improving target detection and ghost target suppression in high-resolution radar imaging.

Findings

NOMP-FAR accurately estimates target velocities and ranges.

The algorithm effectively suppresses ghost targets.

Numerical simulations confirm improved performance over existing methods.

Abstract

The linear frequency modulated (LFM) frequency agile radar (FAR) can synthesize a wide signal bandwidth through coherent processing while keeping the bandwidth of each pulse narrow. In this way, high range resolution profiles (HRRP) can be obtained without increasing the hardware system cost. Furthermore, the agility provides improved both robustness to jamming and spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP-FAR is designed to process each coarse range bin to extract the HRRP and velocities of multiple targets, including the guide for determining the oversampling factor and the stopping criterion. In addition, it is shown that the target will cause false alarm in the nearby coarse range bins, a postprocessing algorithm is then proposed to suppress the ghost targets. Numerical simulations are conducted to demonstrate the effectiveness of NOMP-FAR.