Extended Range Profiling in Stepped-Frequency Radar with Sparse Recovery

TL;DR

This paper introduces a compressed sensing-based algorithm for high-resolution range profiling in stepped-frequency radar, capable of recovering extended target profiles even with missing pulses, validated through simulations and real data.

Contribution

It proposes a novel CS-based algorithm for wide-range, high-resolution profiling in SF radar with missing pulses, extending the capabilities of existing methods.

Findings

The algorithm successfully recovers target profiles over multiple coarse-range-bins.

Simulation results confirm the effectiveness of the proposed method.

Real radar data demonstrates improved profile extension compared to traditional methods.

Abstract

The newly emerging theory of compressed sensing (CS) enables restoring a sparse signal from inadequate number of linear projections. Based on compressed sensing theory, a new algorithm of high-resolution range profiling for stepped-frequency (SF) radar suffering from missing pulses is proposed. The new algorithm recovers target range profile over multiple coarse-range-bins, providing a wide range profiling capability. MATLAB simulation results are presented to verify the proposed method. Furthermore, we use collected data from real SF radar to generate extended target high-resolution range (HRR) profile. Results are compared with `stretch' based least square method to prove its applicability.