Experimental Study of Smoothing Modifications of the MUSIC Algorithm for Direction of Arrival Estimation in Indoor Environments

Molodtsov, Vladislav; Kureev, Aleksey; Khorov, Evgeny

doi:10.1109/access.2021.3127861

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…MoLA collects CSI from commercial devices and uses a phase calibration algorithm to eliminate phase errors. MoLA then applies the spatial smoothing algorithm [ 40 ] and the I-MUSIC algorithm [ 41 ] to find possible AoAs. The Sequential Quadratic Programming (SQP) algorithm [ 42 ] is used to solve this optimization problem.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the complex indoor environment, the signal is coherent and creates multipath effects. To decorrelate the signals as much as possible, MoLA deploys the I-MUSIC algorithm for the smoothed correlation matrix of the signal [ 40 ]. The modified correlation matrix

is defined by:

where

is a K -order opposed unit matrix and “*” denotes the complex conjugate of the correlation matrix

, as defined in Equation ( 5 ).…”

Section: Mola: Design and Optimizationmentioning

confidence: 99%

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Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization

Welsen

Brusic

2022

Sensors

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WiFi-based indoor positioning has attracted intensive research activities. While localization accuracy is steadily improving due to the application of advanced algorithms, the factors that affect indoor localization accuracy have not been sufficiently understood. Most localization algorithms used in changing indoor spaces are Angle-of-Arrival (AoA) based, and they deploy the conventional MUSIC algorithm. The localization accuracy can be achieved by algorithm improvements or joint localization that deploys multiple Access Points (APs). We performed an experiment that assessed the Test Point (TP) accuracy and distribution of results in a complex environment. The testing space was a 290 m2 three-room environment with three APs with 38 TPs. The joint localization using three APs was performed in the same test space. We developed and implemented a new algorithm for improved accuracy of joint localization. We analyzed the statistical characteristics of the results based on each TP and show that the local space-dependent factors are the key factors for localization accuracy. The most important factors that cause errors are distance, obstacles, corner locations, the location of APs, and the angular orientation of the antenna array. Compared with the well-known SpotFi algorithm, we achieved a mean accuracy (across all TPs) improvement of 46%. The unbiased joint localization median accuracy improved by 20% as compared to the best individual localization.

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Section: Related Workmentioning

confidence: 99%

is defined by:

where

is a K -order opposed unit matrix and “*” denotes the complex conjugate of the correlation matrix

, as defined in Equation ( 5 ).…”

Section: Mola: Design and Optimizationmentioning

confidence: 99%

Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization

Welsen

Brusic

2022

Sensors

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“…As for the DOA estimation in 5G and 6G wireless communication networks, it might be advisable to look at other types of antenna arrays, such as rectangular arrays, which are currently integrated into 5G base stations and handsets [ 38 ]. Additionally, it could be interesting to look at other, modified types of MUSIC, such as or spatial/backward/time smoothing MUSIC, which are better equipped to deal with highly correlated signals, as encountered due to multipath propagation in indoor environments [ 39 ]. The technique could also be extended to hybrid techniques, such as SpotFi, that rely on a combination of time-of-flight and angle-of-arrival with MUSIC to perform accurate localization with common WiFi infrastructures [ 40 ].…”

Section: Conclusion and Future Workmentioning

confidence: 99%

A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC

Brandt

Verhaevert

Hecke

et al. 2022

Sensors

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The effects of random array deformations on Direction-of-Arrival (DOA) estimation with root-Multiple Signal Classification for uniform circular arrays (UCA root-MUSIC) are characterized by a conformally mapped generalized Polynomial Chaos (gPC) algorithm. The studied random deformations of the array are elliptical and are described by different Beta distributions. To successfully capture the erratic deviations in DOA estimates that occur at larger deformations, specifically at the edges of the distributions, a novel conformal map is introduced, based on the hyperbolic tangent function. The application of this new map is compared to regular gPC and Monte Carlo sampling as a reference. A significant increase in convergence rate is observed. The numerical experiments show that the UCA root-MUSIC algorithm is robust to the considered array deformations, since the resulting errors on the DOA estimates are limited to only 2 to 3 degrees in most cases.

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“…To find this structure, the authors of the paper [12] explicitly built the estimated channel matrix by finding Directions of Arrival (DoA) for each user using some DoA estimation algorithm [13], while the authors of the paper [14] proposed a compressive sensing-based low-rank approximation of the channel matrix to translate the CE problem to a quadratic semidefinite programming problem that can be solved more efficiently. Another approach utilizes the sparsity of the channel covariance matrix to reduce the number of effective channel dimensions [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Beamspace Selection in Multi-User Massive MIMO

et al. 2023

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In this paper, we propose new Discrete Fourier Transform (DFT)-based beamspace selection algorithms for Massive Multiple Input, Multiple Output (MIMO) receiver operating in realistic multi-user (MU) scenarios. In practical uplink scenarios, there is a power ratio between signals received from different users that complicates performance analysis in MU cases. Our algorithms are inspired by a proportional fair approach to allocating spatial resources for target users. Thus, we analyze performance in terms of coverage (for low-power users) and capacity (for high-power users) and consider the implementation complexity to highlight feasible algorithms. Simulations with realistic non-line-of-sight scenarios generated by the QuaDRiGa 2.0 demonstrate our methods outperform other DFT-based alternatives, such as the beamspace selection based on the projection power maximization.INDEX TERMS 5G, multi-user massive MIMO, beamspace selection, angular domain processing.

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Experimental Study of Smoothing Modifications of the MUSIC Algorithm for Direction of Arrival Estimation in Indoor Environments

Cited by 6 publications

References 29 publications

Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization

Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization

A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC

Beamspace Selection in Multi-User Massive MIMO

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