Adaptive sparse representation for source localization with gain/phase errors (original) (raw)
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Sparsity-based direction of arrival estimation in the presence of gain/phase uncertainty
2017 25th European Signal Processing Conference (EUSIPCO)
Estimating the direction of arrival (DOA) in sensor arrays is a crucial task in array signal processing systems. This task becomes more difficult when the sensors have gain/phase uncertainty. We have addressed this issue by modeling the problem as a combination of two sparse components, the DOA vector and the gain/phase uncertainty vector. Therefore, a sparse decomposition technique is suggested to jointly recover the DOAs and the sensors with gain/phase uncertainty. The simulation results confirm that the suggested method offers very good performance in different scenarios and is superior to its counterparts.
Journal of telecommunications and information technology, 2021
Regular fully filled antenna arrays have been widely used in direction of arrival (DOA) estimation. However, practical implementation of these arrays is rather complex and their resolutions are limited to the beamwidth of the array pattern. Therefore, higher resolution and simpler methods are desirable. In this paper, the compressed sensing method is first applied to an initial fully filled array to randomly select the most prominent and effective elements which are used to form the sparse array. To keep the dimension of the sparse array equal to that of the fully filled array, the first and the last elements were excluded from the sparseness process. In addition, some constraints on the sparse spectrum are applied to increase estimation accuracy. The optimization problem is then solved iteratively using the iterative reweighted l 1 l 1 l 1 norm. Finally, a simple searching algorithm is used to detect peaks in the spectrum solution that correspond to the directions of the arriving signals. Compared with the existing scanned beam methods, such as the minimum variance distortionless response (MVDR) technique, and with subspace approaches, such as multiple signal classification (MUSIC) and ESPIRT algorithms, the proposed sparse array method offers better performance even with a lower number of array elements and in severely noisy environments. Effectiveness of the proposed sparse array method is verified via computer simulations.
2021
Date of publication (dd/mm/yyyy): 03/04/2021 Abstract – In this paper, a new method for the direction of arrival (DoA) estimation using sparse representation of covariance matrix is proposed by using a non-uniform linear array. By vectoring of covariance matrix of nonuniform linear array, a new vector will be derived. This vector is similar to received vector of a virtual uniform linear array with a large number of antennas. As the covariance matrix of this vector is rank one, then the DoA of one source will be estimated. An approach to solve this problem is spatial smoothing technique. In this method, the obtained array is divided into multiple sub-arrays and the covariance matrix of each sub-array will be estimated. Using the average of sub-arrays covariance matrix, a new full rank covariance matrix will be obtained. By quantizing the continuous angle space into a discrete set, DoA estimation can be modeled as a compressed sensing problem. The DoA of sources will be estimated by m...
A Sparse-Based Approach for DOA Estimation and Array Calibration in Uniform Linear Array
IEEE Sensors Journal, 2016
This work aims at achieving a joint estimation of direction-of-arrival (DOA) and array perturbations such as gain and phase uncertainty, mutual coupling, and sensor location error, which deteriorate the performance of the DOA estimation if not carefully handled. To that end, in this work, the array perturbations represented by a perturbation matrix as multiplicative noise to the array manifold are then reformulated to facilitate the perturbation compensations. One great finding on the perturbation matrix is that it is a sparse matrix, which contains a lot of zero elements and only few nonzero elements. With this reformulation, the perturbation compensation problems turn into sparse matrix completion problems. Then, by utilizing the sparsity of both the DOAs and perturbation matrix, a joint estimation of DOAs and array perturbations is proposed under a unified optimization framework. Additionally, numerical studies are presented to demonstrate the effectiveness of the joint estimation.
Real-Valued 2-D Direction of Arrival Estimation via Sparse Representation
arXiv (Cornell University), 2024
Despite many advantages of direction-of-arrivals (DOAs) in sparse representation domain, they have high computational complexity. This paper presents a new method for real-valued 2-D DOAs estimation of sources in a uniform circular array configuration. This method uses a transformation based on phase mode excitation in uniform circular arrays which called real beamspace SVD (RB-SVD). This unitary transformation converts complex manifold matrix to real one, so that the computational complexity is decreased with respect to complex-valued computations-its computation, at least, is one-fourth of the complex-valued case; moreover, some benefits from using this transformation are robustness to array imperfections, a better noise suppression because of exploiting an additional real structure, and etc. Numerical results demonstrate the better performance of the proposed approach over previous techniques such as C-SVD, RB-ESPRIT, and RB-MUSIC, especially in low signal-to-noise ratios.
IEEE Transactions on Signal Processing, 2005
We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise.
Mathematical Problems in Engineering
The paper investigates DOA estimation of coherent Signals with the limited aperture sparse array. Mutual coupling between the sensors of the array cannot be ignored in practical radar with a limited aperture of array sensors, which will result in a degradation in the performance of Direction of Arrival (DOA) estimation. This paper proposes a Mutual-coupling-optimized array (MCOA) with a limited aperture in this scenario to reduce the mutual coupling effect. Firstly, we prove the sparse uniform linear array (SULA) has the smallest mutual coupling leakage when the array aperture and the number of sensors is determined. Secondly, we modify the spacing of the array sensors in SULA to make sure that the spacing between all array sensors and the reference sensor are coprime aiming to estimate DOA without spatial aliasing. Thirdly, we give an expression for the array element spacing arrangement with reduced mutual coupling leakage. Finally, the coherent signals are well resolved by the Spa...
DOA Estimation Exploiting Interpolated Multi-Frequency Sparse Array
2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)
We consider gridless direction-of-arrival (DOA) estimation of much more targets than the number of physical sensors through the exploitation of multi-frequency sparse array design and processing which increase the degrees of freedom as more frequency components are used. A modified sensor interpolation technique is developed to accurately estimate the signal covariance matrix using very few snapshots, thereby eliminating the requirement of a large number of snapshots as in conventional different coarray-based DOA estimation. Simulation results demonstrate high-resolution gridless DOA estimation capability of more targets than the number of physical sensors.
L0-Sparse DOA Estimation of Close Sources with Modeling Errors
2020 28th European Signal Processing Conference (EUSIPCO), 2021
In the field of array processing, Direction-Of-Arrival (DOA) estimation of close sources in the presence of modeling errors is a challenging problem. Indeed, the degradation of high-resolution methods on such scenario is well known and documented in the literature. This paper proposes an operational sparse L0-regularized method as an alternative. In sparse DOA estimation methods, the determination of the regularization parameter is a critical point, and it is generally empirically tuned. We first provide, in the presence of modeling errors, a theoretical statistical study to estimate the admissible range for this parameter in the presence of two incoming sources. For close sources, we therefore show that the admissible range is shortened. For an operational system, an off-line predetermination of the regularization parameter is required. We show that its selection is closely connected to the resolution limit for a given modeling error. Numerical simulations are presented to demonstrate the efficiency of the proposed implementation and its superiority in comparison with high-resolution methods.
Source localization using time difference of arrival within a sparse representation framework
2011
The problem addressed is source localization via time-difference-of-arrival estimation in a multipath channel. Solving this localization problem typically implies cross-correlating the noisy signals received at pairs of sensors deployed within reception range of the source. Correlation-based localization is severely degraded by the presence of multipath. The proposed method exploits the sparsity of the multipath channel for estimation of the line-of-sight component. The time-delay estimation problem is formulated as an ℓ1-regularization problem, where the ℓ1-norm is used as a channel sparsity constraint. The proposed method requires knowledge of the pulse shape of the transmitted signal, but it is blind in the sense that information on the specific transmitted symbols is not required at the sensors. Simulation results show that the proposed method delivers higher accuracy and robustness to noise compared to conventional or even super-resolution MUSIC time-difference-of-arrival source localization methods.