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Among various sparse array techniques, co-prime array is found to be more attractive because its higher DoF with a smaller number of sensing elements. However, because of its specific physical non-uniform linear structure, it would be inconvenient and costly to implement in the presence of varying detection scenarios. In this paper, we propose to exploit the current ULA to dynamically formulate a...
The recently developed super-resolution framework by Candes enables direction-of-arrival (DOA) estimation from a sparse spatial power spectrum in the continuous domain with infinite precision in the noise-free case. By means of atomic norm minimization (ANM), the discretization of the spatial domain is no longer required, which overcomes the basis mismatch problem in conventional sparse signal recovery...
While most literature in compressive sensing mostly concentrates on recovering a sparse signal from a reduced number of measurements, parameter estimation problems have recently been studied under this acquisition framework. In this paper, we focus on the problem of direction-of-arrival (DOA) estimation from compressive measurements taken at each antenna in a receiver array. In contrast with the common...
This paper considers the problem of DOA estimation of correlated sources using sparse arrays, where the number of sources can exceed the number of sensors. Depending on the magnitude of the cross correlation terms, our algorithm either treats them as additive noise (small correlation) or estimates them jointly with the DOAs (large correlation). In the latter case, the problem is cast as an equivalent...
Recently, there has been increased interest in using spatially-aliased antenna arrays for direction-finding, whereby the ambiguities associated with the identified angles are resolved using either additional hardware (e.g. sub-arrays) and/or additional numerical processing. In this paper, we describe a direction-of-arrival estimation technique for large-aperture uniform linear arrays whose inter-element...
This paper proposes a super-resolution direction-of-arrival (DOA) estimator using coprime sensor arrays (CSAs) with the min processor. The min processor resolves the CSA subarrays' spatial aliasing while achieving lower sidelobes than the product processor and maintaining a positive semi-definite spatial power spectral density (PSD) estimation. The spatial correlation function implied by the CSAmin...
Massive MIMO and beamforming is commonly considered as one of the key technologies for future cellular communications. It is targeted to solve the capacity requirements in enhanced mobile broadband and enable new services in other communication fields, e.g. machine type communications. In this work, we study M-MIMO technology in a novel approach, combining system simulations and modeling with hardware...
This paper considers detection performance of linear sparse arrays in the presence of multiple source Direction-Of-Arrivals (DOA). A number of source DOAs greater than the number of physical array elements are randomly generated, without restriction on the angular separation. Two augmentation algorithms are considered before application of traditional spatial spectrum estimation algorithms: MVDR and...
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