Improvement of Direction of Arrival (DoA) Estimation using Compressed Sensing Based on Covariance Matrix (original) (raw)
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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.
Sparse direction-of-arrival estimation for two sources with constrained antenna arrays
2017 10th International Conference on Electrical and Electronics Engineering (ELECO), 2017
Compressive sensing (CS), multiple signal classification (MUSIC), and estimation of signal parameter via rotational invariance techniques (ESPRIT) are among the main used estimation techniques for direction of arrival (DOA). Though, the practical implementation of DOA techniques in handheld wireless devices is limited by the number of antennas and the spacing between them. A robust DOA estimation technique is needed to overcome the different impairments in the communication channel. This paper mainly focuses on DOA estimation of two sources in the presence of practical limitations. A comparison between important DOA estimation algorithms is presented including: Beamforming, Capon, MUSIC, and First-norm singular value decomposition (l1-SVD).
A Robust Multi Sample Compressive Sensing Technique for DOA Estimation Using Sparse Antenna Array
IEEE Access
In this paper, a multi sample compressive sensing (CS) technique is presented for the direction of arrival (DOA) estimation using sparse antenna array that has applications in several fields including radars and sonars. Two different types of sparse antenna arrays are considered. One is linear sparse array for DOA estimation in one dimension and other is L shaped sparse array for DOA estimation in two dimensions. To make the algorithm robust against impulsive and Gaussian noise, a preprocessing stage is introduced. First, in the preprocessing stage median difference correntropy is used that combines median difference and the generalized correntropy. This suppresses the amplitude of impulsive noise. Second, the strength of weighted moving average filter is exploited before applying the CS technique to make the algorithm more robust. In the CS techniques, the source energy is distributed among the adjacent grid due to grid mismatch. Therefore, a fitness function based on the difference of the source energy among the adjacent grid is introduced. This provides the best discretization value through iterative grid refinement for the grid. The effectiveness and robustness of the proposed method is verified through exhaustive simulations for different number of sources and noise scenarios using one dimensional and two-dimensional sparse array structures.
Continuous sparse recovery for direction of arrival estimation with co-prime arrays
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014
We consider the problem of direction of arrival (DOA) estimation using a newly proposed structure of co-prime arrays in this paper. A continuous sparse recovery method is implemented in this work. We show that in the noiseless case one can theoretically detect up to M N 2 sources with only 2M + N sensors via continuous sparse recovery. The noise statistics of coprime arrays are also analyzed to demonstrate the robustness of the proposed optimization scheme. By numerical examples, we show the superiority of the proposed method.
Sparse DOA Estimation Based on Multi-Level Prime Array with Compression
IEEE Access, 2019
A signal emitter can be located using diverse types of direction finding (DF) techniques. One of the most widely used techniques is the direction of arrival (DOA) estimation using antenna arrays. An array configuration that can increase the degrees of freedom (DOF) or the number of estimated sources is desired. Multi-level prime array (MLPA) uses multiple uniform linear subarrays where the number of elements in the subarrays is pairwise coprime integer. Compared with nested and coprime arrays, MLPA requires smaller aperture size which is important in mobile applications. Different MLPA configurations can be constructed for a given number of antennas and the one that maximizes the DOF is exploited. These configurations have a difference coarray with large number of consecutive lags and few holes. The number of consecutive lags can be increased by properly compressing the inter-element spacing of one subarray under a fixed number of antennas and without changing the aperture size. This paper proposes a new compressed MLPA configuration and demonstrates its performance in sparse DOA estimation. The resultant array, MLPA with compressed subarray (MLPAC), can have a hole-free difference coarray as in nested array case. MLPAC can estimate larger number of sources using both MUSIC and sparse reconstruction algorithms. Mutual coupling between sensors has also been evaluated. Simulation results confirm the achievable DOF and the advantage of the proposed configuration in DOA estimation.
IEEE Transactions on Aerospace and Electronic Systems, 2000
This work addresses the problem of direction-of-arrival (DOA) estimation of multiple sources using short and dynamic sensor arrays. We propose to utilize compressive sensing (CS) theory to reconstruct the high-resolution spatial spectrum from a small number of spatial measurements. Motivated by the physical structure of the spatial spectrum, we model it as a sparse signal in the wavenumber-frequency domain, where the array manifold is proposed to serve as a deterministic sensing matrix. The proposed spatial CS (SCS) approach allows exploitation of the array orientation diversity (achievable via array dynamics) in the CS framework to address challenging array signal processing problems such as left-right ambiguity and poor estimation performance at endfire. The SCS is conceptually different from well-known classical and subspace-based methods because it provides high azimuth resolution using a short dynamic linear array without restricting requirements on the spatial and temporal stationarity and correlation properties of the sources and the noise. The SCS approach was shown to outperform current superresolution and orientation diversity based methods in single-snapshot simulations with multiple sources.
International Journal of Antennas and Propagation, 2015
We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.
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.
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.