Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized sampling grid. To deal with such an off-grid DOA estimation problem, this paper studies an off-grid model that takes into account effects of the off-grid DOAs and has a smaller modeling error. An iterative algorithm is developed based on the off-grid model from a Bayesian perspective while joint sparsity among different snapshots is exploited by assuming a Laplace prior for signals at all snapshots. The new approach applies to both single snapshot and multi-snapshot cases. Numerical simulations show that the proposed algorithm has improved accuracy in terms of mean squared estimation error. The algorithm can maintain high estimation accuracy even under a very coarse sampling grid.Comment: To appear in the IEEE Trans. Signal Processing. This is a revised, shortened version of version

Low complexity DOA estimation for wideband off-grid sources based on re-focused compressive sensing with dynamic dictionary

Under the compressive sensing (CS) framework, a novel focusing based direction of arrival (DOA) estimation method is first proposed for wideband off-grid sources, and by avoiding the application of group sparsity (GS) across frequencies of interest, significant complexity reduction is achieved with its computational complexity close to that of solving a single frequency based direction finding problem. To further improve the performance by alleviating both the off-grid approximation errors and the focusing errors which are even worse for the off-grid case, a dynamic dictionary based re-focused off-grid DOA estimation method is developed with the number of extremely sparse grids involved in estimation refined to the number of detected sources, and thus the complexity is still very low due to the limited increased complexity introduced by iterations, while improved performance can be achieved compared with those fixed dictionary based off-grid methods

Joint Beamforming Design and 3D DoA Estimation for RIS-aided Communication System

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight (LOS) links and reflect the uplink pilot signal to sensors. To overcome the mutually coupled problem between the beamforming design at the RIS and DoA estimation, we explore the separable sparse representation structure and propose an alternating optimization algorithm. The grid-based DoA estimation is modeled as a joint-sparse recovery problem considering the grid bias, and the Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The corresponding Cram\'er-Rao lower bound (CRLB) is derived to evaluate the estimation. Then, the beampattern at the RIS is optimized to maximize the signal-to-noise (SNR) at sensors according to the estimated angles. Numerical results show that the proposed alternating optimization algorithm can achieve lower estimation error compared to benchmarks of random beamforming design.Comment: 6 pages, 6 figure

Efficient direction of arrival estimation based on sparse covariance fitting criterion with modeling mismatch

This paper studies direction of arrival (DoA) estimation with an antenna array using sparse signal reconstruction (SSR). Among the existing SSR methods, the sparse covariance fitting based algorithms, which can estimate source power and noise variance naturally, are most promising. Nevertheless, they are either on-grid model based methods whose performance are sensitive to off-grid DoAs or gridless methods which are computationally demanding. In this paper, we propose an off-grid DoA estimation algorithm based on the sparse covariance fitting criterion. We first consider a scenario in which the number of snapshots is larger than the array size. An algorithm is proposed by applying an off-grid model, which takes into account the deviations between the discretized sampling grid and the true DoAs, to the sparse covariance fitting criterion. It estimates the on-grid parameters and the deviations of off-grid DoAs separately and thus is computationally efficient to implement. Then in the case where the number of snapshots is smaller than the array size, we propose to execute the DoA estimation algorithm iteratively under the stochastic maximum likelihood (SML) criterion. The estimation accuracy and computational efficiency of the proposed algorithms are demonstrated by computer simulations

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