Abstract
Single-snapshot direction-of-arrival (DOA) estimation plays an important role in dynamic target detection and tracking applications. Because a single-snapshot signal provides few information for statistics calculation, recently compressed sensing (CS) theory is applied to solve single-snapshot DOA estimation, instead of the traditional DOA methods based on statistics. However, when the unknown sources are closely located, the spatial signals are highly correlated, and its overcomplete dictionary is made up of dense grids, which leads to a serious decrease in the estimation accuracy of the CS-based algorithm. In order to solve this problem, this paper proposed a two-step compressed sensing-based algorithm for the single-snapshot DOA estimation of closely spaced signals. The overcomplete dictionaries with coarse and refined grids are used in the two steps, respectively. The measurement matrix is constructed by using a very sparse projection scheme based on chaotic sequences because chaotic sequences have determinism and pseudo-randomness property. Such measurement matrix is mainly proposed for compressing the overcomplete dictionary in preestimation step, while it is well designed by choosing the steering vectors of true DOA in the accurate estimation step, in which the neighborhood information around the true DOAs partly solved in the previous step will be used. Monte Carlo simulation results demonstrate that the proposed algorithm can perform better than other existing single-snapshot DOA estimation methods. Especially, it can work well to solve the issues caused by closely spaced signals and single snapshot.
Highlights
Direction-of-arrival (DOA) estimation plays an important role for target/source localization, which is widely used in many fields including radar, sonar, speech, communication, and medical diagnosis [1,2,3,4]
Traditional high-resolution DOA estimation algorithms use the statistics of observed signals to improve the performance efficiency, such as multiple signal classification (MUSIC) algorithm and estimation method of signal parameters via rotational invariance techniques (ESPRIT) [5, 6]. ese algorithms require to receive the signals observed in a period of time
A two-step algorithm based on compressed sensing (CS) theory is proposed to solve the single-snapshot dense DOA estimation problem. e features of the proposed method include the following: (1) the search spaces with coarse and refined grids are used in the two estimations, respectively, so that the size of the overcomplete matrices is reduced to a low level; (2) a new double-structure measurement matrix is firstly presented using very sparse projection scheme and chaotic sequence matrix; and (3) the performance of the proposed algorithm is quite good demonstrated by numerical examples
Summary
Direction-of-arrival (DOA) estimation plays an important role for target/source localization, which is widely used in many fields including radar, sonar, speech, communication, and medical diagnosis [1,2,3,4]. In order to improve the accuracy of single-snapshot DOA estimation, the latest research studies on this topic mainly used the compressed sensing theory [8, 9] because in CSbased algorithms, the signals can be reconstructed in spatial by an overcomplete dictionary, whose sampling interval could be smaller than the Nyquist limit [10]. Erefore, the design of overcomplete dictionary becomes the key step in the CS-based single-snapshot DOA estimation algorithms for closely spaced signals, which is the main content of this paper. In order to improve the estimation accuracy, this paper proposed a two-step method to construct the overcomplete dictionary. E proposed CS-based method used a double-structure measurement matrix for closely spaced DOA estimation.
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