A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model

TL;DR

This paper introduces a fast, sparse approximation-based algorithm called OMP-SD for synthesizing high-resolution range profiles in stepped-frequency radar, reducing computational complexity and improving robustness against data loss.

Contribution

It proposes a novel OMP-SD algorithm that formulates HRRP synthesis as a sparse approximation problem, incorporating sensing dictionaries to reduce complexity and mitigate model mismatch.

Findings

OMP-SD reduces computational complexity significantly.

It performs well in noisy and noiseless conditions.

The method effectively handles data loss in HRRP synthesis.

Abstract

To achieve high range resolution profile (HRRP), the geometric theory of diffraction (GTD) parametric model is widely used in stepped-frequency radar system. In the paper, a fast synthetic range profile algorithm, called orthogonal matching pursuit with sensing dictionary (OMP-SD), is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori information that targets are sparsely distributed in the range space, the synthetic range profile (SRP) can be accomplished even in presence of data lost. Besides, the computational complexity is reduced by introducing sensing dictionary (SD) and it mitigates the model mismatch at the same time. The computation complexity decreases from O(MNDK) flops for OMP to O(M(N +D)K) flops for OMP-SD. Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.