237 research outputs found
Assistant Vehicle Localization Based on Three Collaborative Base Stations via SBL-Based Robust DOA Estimation
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 (GPSs). 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 (BSs) 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 nonuniform noise, where the covariance matrix of nonuniform noise is estimated by a least squares (LSs) 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
Localization of DOA trajectories -- Beyond the grid
The direction of arrival (DOA) estimation algorithms are crucial in
localizing acoustic sources. Traditional localization methods rely on
block-level processing to extract the directional information from multiple
measurements processed together. However, these methods assume that DOA remains
constant throughout the block, which may not be true in practical scenarios.
Also, the performance of localization methods is limited when the true
parameters do not lie on the parameter search grid. In this paper we propose
two trajectory models, namely the polynomial and bandlimited trajectory models,
to capture the DOA dynamics. To estimate trajectory parameters, we adopt two
gridless algorithms: i) Sliding Frank-Wolfe (SFW), which solves the Beurling
LASSO problem and ii) Newtonized Orthogonal Matching Pursuit (NOMP), which
improves over OMP using cyclic refinement. Furthermore, we extend our analysis
to include wideband processing. The simulation results indicate that the
proposed trajectory localization algorithms exhibit improved performance
compared to grid-based methods in terms of resolution, robustness to noise, and
computational efficiency
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