OTHR Spectrum Reconstruction of Maneuvering Target with Compressive Sensing

Yinghui Quan,Hongxian Wang,Mengdao Xing,Yachao Li,Lei Zhang

doi:10.1155/2014/870352

Yinghui Quan, Hongxian Wang + Show 3 more

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https://doi.org/10.1155/2014/870352

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Abstract

High-frequency (HF) over-the-horizon radar (OTHR) works in a very complicated electromagnetic environment. It usually suffers performance degradation caused by transient interference. In this paper, we study the transient interference excision and full spectrum reconstruction of maneuvering targets. The segmental subspace projection (SP) approach is applied to suppress the clutter and locate the transient interference. After interference excision, the spectrum is reconstructed from incomplete measurements via compressive sensing (CS) by using a redundant Fourier-chirp dictionary. An improved orthogonal matching pursuit (IOMP) algorithm is developed to solve the sparse decomposition optimization. Experimental results demonstrate the effectiveness of the proposed methods.

Highlights

High-frequency (HF) sky wave over-the-horizon radar (OTHR) can provide long-range detection of targets in large surveillance areas [1,2,3,4]
We focus on the transient interference mitigation and full spectrum reconstruction of maneuvering targets for OTHR
The segmental subspace projection and adaptive threshold are used for transient interference excision

Summary

Introduction

High-frequency (HF) sky wave over-the-horizon radar (OTHR) can provide long-range detection of targets in large surveillance areas [1,2,3,4]. Transient interference usually only exists in several pulse repetition periods, but its intensity is great. It will submerge the target signal in the Doppler frequency domain and make target detection fail. Filtering out these interferences is available [7, 8], while it destroys the integrity of signal in the time domain. Modern OTHR systems are multimode and expected to detect multitargets, simultaneously. To satisfy these requirements, discontinuous sampling mode is usually applied which results in parts of signal missing for a single target. The missed data is able to be recovered by the model coefficients and available observations [11,12,13]

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