DOA estimation in MIMO systems with Compressive Sensing for future handsets

Alawsh, Saleh A.; Muqaibel, Ali H.; Sharawi, Mohammad S.

doi:10.1109/aeect.2015.7360532

Cited by 3 publications

(4 citation statements)

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“…We should note that CS is essentially a framework, meaning that, if a problem can be described by a sparse model, then CS can be used to solve the problem, e.g., References [15,16,17]. Recently, due to the superiorities of the MIMO radar system, the applications of CS in the MIMO radar system have been studied in several literatures [18,19,20,21,22,23,24]. In Reference [18], the compressive reconstruction algorithm was extended to the MIMO radar system and a waveform design method was also introduced.…”

Section: Introductionmentioning

confidence: 99%

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A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements

Chen

Yang

Guo

2019

Sensors

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In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations.

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Section: Introductionmentioning

confidence: 99%

“…The CS was exploited in the spatial domain to reduce the number of antennas of the MIMO radar system in Reference [23], and the recovery guarantees were also derived. In Reference [24], the performance of several DOA estimation algorithms for the MIMO system is discussed under the practical background of future handsets.…”

Section: Introductionmentioning

confidence: 99%

A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements

Chen

Yang

Guo

2019

Sensors

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“…CS, in turn, allows the full use of virtual elements in the array resulting in M × N DOF. In [9], a comparison is performed between CS and other DOA estimation algorithms including MUSIC, Capon, and Beamforming. CS was found superior in terms of beamwidth and resolution.…”

Section: Introductionmentioning

confidence: 99%

“…In most of the works studying DOA estimation, antennas were assumed to be isotropic [1,[6][7][8][9][10][11][12][13][14]. Isotropic antennas are merely mathematical models that have real spherical radiation patterns with zero phase.…”

Section: Introductionmentioning

confidence: 99%

Patch and monopole antennas in linear coprime arrays for direction of arrival estimation using compressed sensing

Oweis

Alawsh

Muqaibel

et al. 2019

IET Microwaves, Antennas & Propagation

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Original work on direction of arrival (DOA) estimation relied on uniform linear arrays (ULAs) of antennas. Most of the work focused on improving the algorithms and the configuration of the antenna array and overlooked the effects of practical antennas on the algorithm performance. Very limited work studied DOA estimation within the physical limitations of handheld devices. In this work, we introduce three nonuniform linear coprime arrays based on patch and monopole antenna elements operating in 2.1 and 5.8 GHz bands and assess their behaviour in DOA estimation. The complex radiation patterns of the arrays were incorporated in the DOA estimation algorithm using compressed sensing (CS). Estimation accuracy is quantified by the root mean square error (RMSE) and the results are compared with those obtained by using isotropic antennas, showing that physical antennas can introduce up to 8° of error. Simulations were also carried out using the multiple signal classification (MUSIC) algorithm to demonstrate the advantage of CS in coprime arrays. The MUSIC algorithm failed to detect all sources even at maximum SNR. The impact of reducing the fundamental inter‐element spacing in coprime arrays below 0.5λ to achieve smaller array sizes is investigated as well.

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