Grid Reconfiguration Method for Off-Grid DOA Estimation

Ling, Yun; Gao, Huotao; Ru, Guobao; Chen, Haitao; Li, Boya; Cao, Ting

doi:10.3390/electronics8111209

Cited by 8 publications

(10 citation statements)

References 40 publications

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“…However, the DOAs are not usually exactly on the grid, which leads to the off-grid gap problem, as shown in Figure 2 [55]. In general, there are two kinds of off-grid methods to handle this problem: One is to use a fixed grid with joint estimation of the sparse signal and the grid offset [45,46]; the other one is to use a dynamic grid [18,47,48]. Compared with fixed grid methods, dynamic grid methods have the advantages of lower computational complexity and higher accuracy.…”

Section: Data Modelmentioning

confidence: 99%

“…Both GRDOA [48] and the proposed method are off-grid DOA estimation methods based on SBL. The discrete grid points are regarded as the dynamic parameters in both methods to reduce the model error, and the performance degradation in RSBL [47] caused by middle located DOAs is effectively alleviated in both methods.…”

Section: Compared With Grdoamentioning

confidence: 99%

“…However, RSBL is still quite time consuming at other steps in the iterative updating. In GRDOA [48], the grid points get reconfigurated via a grid refining and fission strategy, which makes the method more efficient and accurate than RSBL.…”

Section: Introductionmentioning

confidence: 99%

“…The system is also robust as it is not affected by the weather and light. In addition, many existing SBL based DOA estimation methods can achieve higher accuracy than conventional ones [45][46][47][48]; the SBL-based DOA method is thus an efficient scheme for vehicle localization, as the localization method is merely dependent on the accuracy of DOA estimation. Therefore, it is worthwhile to investigate the vehicle localization systems using passive bistatic radar with an advanced SBL based DOA estimation algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

Ling

Gao

Zhou

et al. 2020

Sensors

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With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

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Section: Data Modelmentioning

confidence: 99%

Section: Compared With Grdoamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

Ling

Gao

Zhou

et al. 2020

Sensors

Self Cite

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“…The quantisation error is taken as real-valued and assumed to be uniformly distributed. The off-grid AoA refinement can be performed optimally if the coarse AoA estimates are in the correct number and all within the trust region [24,29]. To satisfy these conditions and obtain off-grid AoA refinement, large numbers of initial bias hampers and earlier iterations are required, and this, in turn, increases the computational burden and execution time.This work proposes two new AoA methods to estimate the directions of the incident signals on an antenna array.…”

mentioning

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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GSM Device Localization in Indoor Environment Using Received Signal Strength Indicator (RSSI) and Convolutional Neural Networks (CNN)

Burhan

Nawawi

Yunus

2022

Lecture Notes in Electrical Engineering

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Grid Reconfiguration Method for Off-Grid DOA Estimation

Cited by 8 publications

References 40 publications

Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

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

GSM Device Localization in Indoor Environment Using Received Signal Strength Indicator (RSSI) and Convolutional Neural Networks (CNN)

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