Abstract
Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To re- duce sampling ratio, we introduce level-crossing (LC) sam- pling 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 statis- tics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sen- sors, a sparse angle space scenario can be found by solving an '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) al- gorithms, as well as subspace method, improves the DOA es- timation performance, while using less samples when com- pared with Nyquist-rate sampling and reducing sensor activ- ity especially for long time silence signal.
Highlights
Direction-of-arrival (DOA) estimation of propagating plane waves is an extensively studied problem in the field of array signal processing, sensor networks, remote sensing, etc
The performance of our proposed approach is evaluated using a linear array of 11 sensors uniformly placed on the x-axis, and the first sensor is selected as reference sensor (RS) which is placed to be at the origin
We demonstrate the feasibility of our proposed DOA estimation method by using sparse recovery algorithm with second-order statistics
Summary
Direction-of-arrival (DOA) estimation of propagating plane waves is an extensively studied problem in the field of array signal processing, sensor networks, remote sensing, etc. To determine signal source DOAs using multiple measurements vectors, minimum variance distortionless response (MVDR), and multiple signal classification (MUSIC) algorithms are commonly used [1]. By construction, all these traditional DOA estimation methods require Nyquistrate sampling of the received signals, which may result in high storage and bandwidth requirements in many sensing systems. The basis pursuit strategy has been used for formulating the DOA estimation problem as a dictionary selection problem where the dictionary entries are produced by discretizing the angle space and synthesizing the sensor signals for each discrete angle. Sparseness in angle space implies that only a few of the dictionary entries will be required to match the measurements
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