Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics
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Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal.
KeywordsDirection-of-arrival estimation, level crossing, compressive sensing, dantzing selector, convex optimization
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2015 vol. 24, č. 1, s. 208-213. ISSN 1210-2512
- 2015/1