A New Low Complexity Angle of Arrival Algorithm for 1D and 2D Direction Estimation in MIMO Smart Antenna Systems

Al-Sadoon, Mohammed A. G.; Ali, Nazar; Dama, Y. A. S.; Zuid, Abdulkareim; Jones, S. M.; Abd‐Alhameed, Raed A.; Noras, James M.

doi:10.3390/s17112631

Cited by 33 publications

(20 citation statements)

References 38 publications

(37 reference statements)

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“…It is assumed that L narrowband signals, arriving from different elevation (θ j ) and azimuth angles (φ j ) angles are incident on an arbitrary antenna array, as shown in Figure 1 [36]. The signal sources are assumed to lie in the far-field.…”

Section: Aoa Model and Problem Formulationmentioning

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: Aoa Model and Problem Formulationmentioning

confidence: 99%

“…A Propagator Direct Data Acquisition (PDDA) that can determine the DoAs directly from the measurement data without the need to form the covariance matrix has been presented in Reference [36]. This method partitions the received data matrix into two parts and then finds the cross-correlation between them.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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“…The array covariance matrix (R xx ) can be computed by applying the expectation process to the received data matrix as follows [18]:…”

Section: The Projection Matrix Constructionmentioning

confidence: 99%

“…After that, based on the used method where some AoA algorithms need to decompose the CM, which requires O (M 3 ) or computes the projection matrix that needs to O (M 2 C) arithmetic operations, etc. [18,40]. It should be noted with massive MIMO, the burden of O (M 3 ) will suffer from infeasibly high computational complexity.…”

Section: Computational Complexity Analysismentioning

confidence: 99%

“…Although these methods can provide good estimation, they are infeasible with large-dimension matrices since they need inverse or matrix decomposition operations to solve the problem. Alternatively, a Propagator Direct Data Acquisition (PDDA) method can find the AoAs directly from the observed data matrix without the need to calculate the inverse of the Covariance Matrix (CM) or apply the Eigen/Singular Value Decomposition (E/SVD) approach [18]. Instead, the PDDA method calculates the cross-correlation vector that represents the cross-correlation between the observed data from the first sensor and the other sensors.…”

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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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Sparse linear array in the estimation of AoA and AoD with high resolution and low complexity

Beulah¹,

Venkateswaran²

2019

Trans Emerging Tel Tech

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High resolution with low complexity angle of arrival and departure estimation in real-time radar and wireless communication has been a topic of intensive research. In this paper, two novel algorithms are proposed, namely, minimum redundancy and minimum redundancy-reduced correlation algorithms, for finding the angles of arrival and departure with high resolution and low complexity. The first of the two proposed algorithms is meant for the reduction in the active number of antennas (minimum redundancy) without generating virtual angles. The second involves a reduction in subspace leakage to enable improvement of the peak power of the estimated true angles. The authors have assumed the users as coherent and the channel following Saleh-Valenzuela model. This has been done for the validation of the performance of the proposed algorithms. Numerical simulations demonstrate the out performance of the existing sparse array subspace-based methods in the estimation of angles by the proposed methods.

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A New Low Complexity Angle of Arrival Algorithm for 1D and 2D Direction Estimation in MIMO Smart Antenna Systems

Cited by 33 publications

References 38 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

Sparse linear array in the estimation of AoA and AoD with high resolution and low complexity

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