A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model
Rong Fan, Qun Wan, Xiao Zhang, Hui Chen, Yipeng Liu

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSparse and Compressive Sensing Techniques · Blind Source Separation Techniques · Image and Signal Denoising Methods
