A new DOA estimator in nonuniform noise

Madurasinghe, D.

doi:10.1109/lsp.2005.843774

Cited by 43 publications

(28 citation statements)

References 7 publications

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“…On the other hand, all the algorithms mentioned above for off-grid DOA estimation are based on the assumption that the noise is uniform Gaussian white noise which is unrealistic to meet in practice due to the non-uniform sensor response and non-ideal receiving channel [35], [36]. Aiming at dealing with the non-uniform noise, a large number of Maximum Likelihood (ML) based algorithms [37]- [40] have been proposed in the past few decades. Particularly, the stochastic ML method presented in [40] can effectively eliminate the influence of non-uniform noise by accurately estimating the covariance matrix of non-uniform noise based on a modified inverse iteration algorithm.…”

mentioning

confidence: 99%

Assistant Vehicle Localization Based on Three Collaborative Base Stations via SBL-Based Robust DOA Estimation

Wang

Wan

Dong

et al. 2019

IEEE Internet Things J.

185

101

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As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the Global Positioning Systems (GPS). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on Direction-of-Arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three Base Stations (BS) equipped with a Multiple Input Multiple Output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a Sparse Bayesian Learning (SBL) based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of non-uniform noise, where the covariance matrix of non-uniform noise is estimated by a Least Squares (LS) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method.

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mentioning

confidence: 99%

Assistant Vehicle Localization Based on Three Collaborative Base Stations via SBL-Based Robust DOA Estimation

Wang

Wan

Dong

et al. 2019

IEEE Internet Things J.

185

101

View full text Add to dashboard Cite

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“…Remark 1: The estimate of Q in ( 13) is an alternative representation of the power domain (PD) method [34], that is,…”

Section: Propositionmentioning

confidence: 99%

“…The deterministic ML estimator along with Cramer-Rao bound (CRB) for both deterministic and stochastic source models have been proposed in [32], while [33] have developed the stochastic ML algorithm for the case of nonuniform noise. A simple method has been devised in [34] possessing less computational cost than ML estimators as well as improving the precision of the DOA estimates. In [35], the authors have developed two iterative methods referred to as iterative ML subspace estimation (IMLSE) and iterative least squares subspace estimation (ILSSE), which estimate the signal subspace and noise covariance matrices based on the ML and least squares (LS) criteria, respectively.…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation in Nonuniform Sensor Noise

Esfandiari¹,

Vorobyov²

2021

Preprint

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The problem of direction-of-arrival (DOA) estimation in the presence of nonuniform sensor noise is considered and a novel algorithm is developed. The algorithm consists of three phases. First, the diagonal nonuniform sensor noise covariance matrix is estimated using an iterative procedure that requires only few iterations to obtain an accurate estimate. The asymptotic variance of one iteration is derived for the proposed noise covariance estimator. Second, a forward-only rooting-based DOA estimator as well as its forward-backward averaging extension are developed for DOA estimation. The DOA estimators take advantage of using second-order statistics of signal subspace perturbation in constructing a weight matrix of a properly designed generalized least squares minimization problem. Despite the fact that these DOA estimators are iterative, only a few iterations are sufficient to reach accurate results. The asymptotic performance of these DOA estimators is also investigated. Third, a newly designed DOA selection strategy with reasonable computational cost is developed to select L actual sources out of 2L candidates generated at the second phase. Numerical simulations are conducted in order to establish the considerable superiority of the proposed algorithm compared to the the existing state-of-the-art methods in challenging scenarios in both cases of uniform and nonuniform sensor noise.

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“…However, they suffer from high computational load since they need to solve highly non‐linear optimisation problems. A DOA estimator was proposed in [21], and it reduces the computational complexity by excluding the noise powers from the objective function. A subspace‐separation‐based estimator [22] was developed to isolate the contribution of the noise powers, based on successive array element elimination.…”

Section: Introductionmentioning

confidence: 99%