Low Complexity Antenna Array DOA System for Localization Applications

Al-Sadoon, Mohammed A. G.; Asif, Rameez; Bin-Melha, Mohammed S.; Obeidat, H. A.; Zweid, A.; Noras, James M.; Abd‐Alhameed, Raed A.

doi:10.1109/wincom.2018.8629656

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…To this end, a covariance matrix that contains data for seven signals is generated randomly with size (30 × 30). Then, Q P has been sampled based on different values of P namely: P = ( 7,10,15,20,24,27), where the Singular Value Decomposition (SVD) applied to each sampled matrix as follows:…”

Section: The Methodology Of Matrix Samplingmentioning

confidence: 99%

“…Two sets of angles are supposed incident on the antenna array with close directions: the first set consists of two angles and the second of three angles. Six different values of P = (7,10,15,20,24,27) columns have been used to construct the projection matrix.…”

Section: Intercomparison: Numerical Examplementioning

confidence: 99%

“…This scenario investigates the variations in the performance estimation of PM methods with a variation of SNR. Five different values of P = (7,10,15,20,25) are considered in this scenario, and for each P, the SNR is assumed to vary from -10 dB to 20 dB in 2.5 dB increments. In order to investigate the effect of P values intensively, two thousand independent trials of Monte Carlo simulation (K = 2000) are carried out at each step.…”

Section: The Performance Comparison Based On the Various Signal-to-nomentioning

confidence: 99%

“…The Fifth Generation (5G) utilizes Massive Multiple Input Multiple Output (Massive MIMO) and beamforming technologies to compensate for penetration loss, to produce multiple beams simultaneously and to provide huge bandwidth and data rate [6,7]. Direction-finding systems encounter many challenges caused by mutual coupling [8,9] and propagation environments such as multipath, limited data (few snapshots), and the time-varying nature of the signals [10]. Arriving signals typically suffer from fading channels, which may cause a low Signal-to-Noise Ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

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The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy

2019

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Several works show that the linear Angle of Arrival (AoA) methods such as Projection Matrix (PM) have low computational complexity compared to the subspace methods. Although the PM method is classified as a subspace method, it does not need decomposition of the measured matrix. This work investigates the effect of the sampled columns within the covariance matrix on the projection matrix construction. To the authors' knowledge, this investigation has not been addressed in the literature. Unlike the subspace methods such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Minimum Norm, Propagator, etc., which have to use a specific number of columns, we demonstrate this aspect is not applicable in the PM method. To this end, the projection matrix is formed based on a various number of sampled columns to estimate the arrival angles. A theoretical analysis is accomplished to illustrate the relationship between the number of the sampled columns and the degrees of freedom (DOFs). The analysis shows that with the same aperture size, the DOFs can be increased by increasing only the number of sampled columns in the projection matrix calculation step. An intensive Monte Carlo simulation for different scenarios is presented to validate the theoretical claims. The estimation accuracy of the PM method, based on the proposed selected sampling methodology outperforms all the other techniques with less complexity compared to the Capon and MUSIC methods. The estimation accuracy is evaluated in terms of Root Mean Square Error (RMSE) and the Probability of Successful Detection (PSD). The results are presented and discussed.

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Section: The Methodology Of Matrix Samplingmentioning

confidence: 99%

Section: Intercomparison: Numerical Examplementioning

confidence: 99%

Section: The Performance Comparison Based On the Various Signal-to-nomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy

2019

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“…Additionally, in wireless mobile communication systems, when AoA of the desired user and interference signals are correctly estimated, an adaptive beamforming technique can be applied to emphasise the gain towards the desired signal suppressing the noise and interference signals [11,12]. Direction-finding systems face different challenges, caused by the propagation environment, for instance, time-varying nature of the propagation environment, low number of snapshots and the multipath propagation environment [13,14]. Moreover, the receiving signals usually suffer from the fading nature of the channels, which causes a poor Signal-to-Noise Ratio (SNR).Capon's Method [15], Multiple Signal Classification (MUSIC) [16] and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) [17] are common methods that deal with the AoA estimation problem.…”

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confidence: 99%

Uniform Sampling Methodology to Construct Projection Matrices for Angle-of-Arrival Estimation Applications

et al. 2019

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This manuscript firstly proposes a reduced size, low-complexity Angle of Arrival (AoA) approach, called Reduced Uniform Projection Matrix (RUPM). The RUPM method applies a Uniform Sampling Matrix (USM) criterion to sample certain columns from the obtained covariance matrix in order to efficiently find the directions of the incident signals on an antenna array. The USM methodology is applied to reduce the dependency between the adjacent sampled columns within a covariance matrix; then, the sampled matrix is used to construct the projection matrix. The size of the obtained projection matrix is reduced to minimise the computational complexity in the searching grid stage. A theoretical analysis is presented to demonstrate that the USM methodology can increase the Degrees of Freedom (DOFs) with the same aperture size and number of sampled columns compared to the classical sampling criterion. Then, a polynomial root is constructed as an alternative efficient computational solution of the UPM method in a one-dimensional (1D) array spectrum peak searching problem. It is found that this distribution increases the number of produced nulls and enhances noise immunity. The advantage of the RUPM method is that it is appropriate to apply for any array configuration while the Root-UPM offers better estimation accuracy with less execution time under a uniform linear array condition. A computer simulation based on various scenarios is performed to demonstrate the theoretical claims. The proposed direction-finding methods are compared with several AoA methods in terms of the required execution time, Signal-to-Noise Ratio (SNR) and different numbers of data measurements. The results verify that the new methods can achieve significantly better performance with reduced computational demands. necessary to process the obtained data in an efficient sampling method in order to extract valuable information about the parameters of the received signals from the increasingly large covariance matrices, which are typically too large and complex for effective manipulation with classical sampling methods [4][5][6].Estimating direction or angle of arrival (DoA/AoA) parameters has been given the most attention, particularly in far-field signal applications, in which the wavefront of the signal may be treated as being planar [7]. Radar systems [8], public security [9] and emergency call location [10] are examples of these applications. Additionally, in wireless mobile communication systems, when AoA of the desired user and interference signals are correctly estimated, an adaptive beamforming technique can be applied to emphasise the gain towards the desired signal suppressing the noise and interference signals [11,12]. Direction-finding systems face different challenges, caused by the propagation environment, for instance, time-varying nature of the propagation environment, low number of snapshots and the multipath propagation environment [13,14]. Moreover, the receiving signals usually suffer from the fading nature of the channels, which causes a ...

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The Selected Samples Effect on the Projection Matrix to Estimate the Direction of Arrival

Al-Sadoon

Al-Nedawe

Bin-Melha³

et al. 2019

2019 UK/ China Emerging Technologies (UCET)

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Low Complexity Antenna Array DOA System for Localization Applications

Cited by 6 publications

References 16 publications

The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy

The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy

Uniform Sampling Methodology to Construct Projection Matrices for Angle-of-Arrival Estimation Applications

The Selected Samples Effect on the Projection Matrix to Estimate the Direction of Arrival

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