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Gridless DOA estimation of coherent signals based on Alternating Direction Multiplier Method

PHYSICAL COMMUNICATION 2025年 68卷

作者： He, Jiai Ou, Mingxia Zhang, Xiaoyu Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China

To address the problem of low accuracy and high algorithm complexity of traditional Direction of Arrival (DOA) estimation algorithm for detecting coherent sources in communication systems, this paper investigates a DOA estimation method of coherent sources under coprime arrays based on Alternating Direction Multiplier Method (ADMM). Firstly, the array elements are arranged into a coprime array, and the holes are interpolated to form a virtual uniform line array to obtain the covariance matrix of the virtual output signal;Then, the non-zero column of interpolation covariance matrix is used to construct the multi-measurement vector matrix reconstruction optimization problem. Finally, the ADMM is introduced to solve the optimization problem to reconstruct the augmented Toeplitz matrix to reduce the algorithm complexity and combine with the Multiple Signal Classification (MUSIC) algorithm to estimate the DOA accurately. Simulation results show that compared with traditional methods, the proposed method is insensitive to the coherence between signals and has higher resolution and estimation accuracy, while greatly reducing the computational complexity of the algorithm, which is more suitable for large-scale array systems.

关键词： Direction of Arrival estimation of coherent source Coprime arrays array interpolation Alternating Direction Multiplier Method

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interpolation array TECHNIQUE FOR DIRECTION FINDING VIA TAYLOR SERIES FITTING

INTERPOLATION ARRAY TECHNIQUE FOR DIRECTION FINDING VIA TAYL...

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IEEE China Summit and International Conference on Signal and Information Processing

作者： Ming-Yang Cao Lei Huang Wei-Xin Xie H. C. So Dept. of Electron. & Inf. Eng. Harbin Inst. of Technol. Harbin China

ISBN: (纸本)9781479919499

interpolation array approach based on Taylor series fitting for arbitrary geometry array is developed in this paper. As the steering vector is a function of direction-of-arrival (DOA), Taylor series fitting for the steering vector is conducted within an angle range, and the steering matrix is decomposed into a coefficient matrix multiplying by a Vandermonde structure vector bearing the DOA information. Moreover, the coefficient matrix is calculated by the least squares method. Results show that the proposed approach has a better DOA estimation performance over the traditional interpolation array technique under uncorrelated and correlated signal conditions.

关键词： Direction-of-arrival array interpolation Taylor series array processing

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An Improved DOA Estimation Approach Using Coarray interpolation and Matrix Denoising

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SENSORS 2017年第5期17卷 1140-1140页

作者： Guo, Muran Chen, Tao Wang, Ben Harbin Engn Univ Coll Informat & Commun 145 Nantong St Harbin 150001 Peoples R China Harbin Engn Univ Coll Automat 145 Nantong St Harbin 150001 Peoples R China Temple Univ Dept Elect & Comp Sci Philadelphia PA 19122 USA

Co-prime arrays can estimate the directions of arrival (DOAs) of O (MN) sources with O (M + N) sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number of degrees of freedom is limited due to the existence of holes in difference coarrays if subspace-based algorithms such as the spatial smoothing multiple signal classification (MUSIC) algorithm are utilized. To address this issue, techniques such as positive definite Toeplitz completion and array interpolation have been proposed in the literature. Another factor that compromises the accuracy of DOA estimation is the limitation of the number of snapshots. Coarray-based processing is particularly sensitive to the discrepancy between the sample covariance matrix and the ideal covariance matrix due to the finite number of snapshots. In this paper, coarray interpolation based on matrix completion (MC) followed by a denoising operation is proposed to detect more sources with a higher accuracy. The effectiveness of the proposed method is based on the capability of MC to fill in holes in the virtual sensors and that of MC denoising operation to reduce the perturbation in the sample covariance matrix. The results of numerical simulations verify the superiority of the proposed approach.

关键词： array interpolation direction-of-arrival estimation matrix denoising MUSIC nuclear norm minimization

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Interpolating Coprime arrays With Translocated and Axis Rotated Compressed Subarrays by Iterative Power Factorization for DOA Estimation

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IEEE ACCESS 2018年 6卷 16445-16453页

作者： Al Mahmud, Tarek Hasan Shabir, Kashif Zheng, Rui Ye, Zhongfu Univ Sci & Technol China Dept Elect Engn & Informat Sci Natl Engn Lab Language & Speech Signal Proc Inst Stat Signal Proc Hefei 230027 Anhui Peoples R China

In this paper, a novel array structure exploiting coprime arrays is proposed, which comprises the translocation of one subarray and axis rotation with a compression of another subarray to produce a larger number of consecutive lags. This composition is named CATARCS, which stands for coprime array with translocated and axis rotated compressed subarrays. The scheme offers consecutive co-array lags in remarkable number 4MN - 1 by using only 2M + N - 1 number of sensors attaining higher degrees of freedom than the state of the art, where M and N are coprime with congenial interelement spacings. However, a very few holes still exist in the co-array, which can be filled up through interpolation. In this paper, the iterative power factorization (IPF) algorithm has been demonstrated as a tool for incremented-rank-IPF matrix recovery which is unostentatious. No extra tuning parameter is required for this approach and several undesired issues of other techniques can be reconciled by this. The main contribution of this paper is suitable modification of array geometry arrangements to increase consecutive lags and to interpolate via IPF, which is used first time for direction of arrival (DOA) estimation in order to exhibit better performances in terms of detecting and resolving a larger number of sources by enhancing resolution. Later, DOA estimation can be achieved by performing multiple signal classification (MUSIC) algorithm. Simulation results validate its effectiveness by achieving lower cost, faster, and accurate DOA estimation even in lower angular separation of sources compared with other techniques like spatial smoothing MUSIC, co-array the least absolute shrinkage and selection operator, spline, iterative co-array array interpolation, and Toeplitz completion.

关键词： array interpolation coprime array consecutive lags degrees of freedom iterative power factorization mutual coupling virtual array

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Joint DOD and DOA Estimation of Coherent Targets for Coprime MIMO Radar

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1408-1420页

作者： Zhang, Xue Zheng, Zhi Wang, Wen-Qin So, Hing Cheung Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Inst Elect & Informat Engn Guangzhou 523808 Guangdong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Recently, coprime arrays have attracted lots of interest due to their ability of providing enhanced degrees-of-freedom and reduced mutual coupling effect compared to conventional uniform linear arrays. Benefitting from these excellent properties, coprime multiple-input multiple-output (MIMO) radar has been recently suggested for improving parameter identifiability and target detection. In this paper, we address the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation of coherent targets in coprime MIMO radar. First of all, we establish an extended virtual uniform rectangular array (URA) by performing array interpolation on the transmit and receive arrays of coprime MIMO radar. Subsequently, we derive a low-rank block Hankel matrix that is constructed using the correlation information of the virtual URA expected outputs, and utilize the matched filter outputs to recover the block Hankel matrix by solving a low-rank structured matrix completion problem. Finally, we estimate the DODs and DOAs of coherent targets by applying the modified matrix pencil method on the recovered low-rank matrix. We also derive the Cramer-Rao bounds with closed-form expressions for DOD and DOA estimation of coherent targets using coprime MIMO radar. The proposed algorithm can identify multiple coherent targets and achieve parameter automatic pairing. Numerical results demonstrate the superiority of the proposed algorithm over several existing approaches when handling coherent targets.

关键词： MIMO radar Direction-of-arrival estimation Estimation US Department of Defense Signal processing algorithms MIMO communication Covariance matrices Coprime MIMO radar coherent target direction-of-departure (DOD) direction-of-arrival (DOA) array interpolation block Hankel matrix low-rank structured matrix completion

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One- and two-dimensional direction-of-arrival estimation: An overview of search-free techniques

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SIGNAL PROCESSING 2010年第5期90卷 1338-1349页

作者： Gershman, Alex B. Ruebsamen, Michael Pesavento, Marius Tech Univ Darmstadt Commun Syst Grp Inst Telecommun D-64283 Darmstadt Germany

One of major challenges in applying traditional subspace-based direction finding techniques to real-time practical problems is in that they normally require an exhaustive spectral search over the angular parameter(s). Therefore, methods avoiding such a computationally demanding spectral search step are of great interest. In this paper, an overview of one- and two-dimensional search-free direction-of-arrival (DOA) estimation methods is presented. Both cases of uniform and non-uniform sensor arrays are addressed. (C) 2009 Elsevier B.V. All rights reserved.

关键词： array processing Direction-of-arrival estimation Root-MUSIC ESPRIT array interpolation Non-uniform sensor arrays Rank reduction

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Enhanced DOA Estimation Exploiting Multi-Frequency Sparse array

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5935-5946页

作者： Zhang, Shuimei Ahmed, Ammar Zhang, Yimin D. Sun, Shunqiao Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

In this paper, we develop a general framework of multi-frequency sparse array to estimate the direction-of-arrival (DOA) of a significantly higher number of targets than the number of physical sensors. The multi-frequency sparse arrays are designed to offer zero lag redundancy in the rendered difference coarray so that the available degrees of freedom are fully utilized to enable high-resolution DOA estimation. A modified sensor interpolation technique is developed to accurately estimate the signal correlation matrix so that the effect of holes in the difference coarray is mitigated. The proposed technique accounts for both self-lags between signals corresponding to the same frequencies and the cross-lags between signals corresponding to different frequencies. As such, it enhances the DOA estimation performance compared to existing methods that either perform array interpolation utilizing only the self-lags or carry out group sparse reconstruction without exploiting array interpolation. Simulation results verify the offerings of the multi-frequency sparse arrays.

关键词： Sensor arrays array signal processing Estimation Direction-of-arrival estimation Correlation Sparse matrices Redundancy array interpolation direction-of-arrival estimation multi-frequency sparse array zero lag redundancy group sparsity

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Eigenstructure-based algorithms for direction finding with time-varying arrays

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1996年第2期32卷 689-701页

作者： Friedlander, B Zeira, A UNIV CALIF DAVIS DAVISCA 95616

This paper considers the problem of finding the directions of narrowband signals using a time-varying array whose elements move during the observation interval in an arbitrary but known way. We derive two eigenstructure-based algorithms for this problem, which are modifications of techniques developed originally for time-invariant arrays. The first uses array interpolation, and the second uses focusing matrices. Like other eigenstructure-based methods, these algorithms require a modest amount of computations in comparison with the maximum likelihood (ML) estimator. The performance of the algorithms is evaluated by Monte-Carlo simulations, and is compared with the Cramer Rao Bound (CRB). Although both techniques were successful for wideband array processing with time-invariant arrays, we found that only the interpolated array algorithm is useful for direction finding (DF) with time-varying arrays.

关键词： Monte Carlo methods direction-of-arrival estimation eigenstructure assignment interpolation matrix algebra performance evaluation time-varying systems Cramer Rao Bound Monte-Carlo simulations array interpolation direction estimation algorithms direction Cramer-Rao bounds time-varying systems Performance Evaluation arrays Monte Carlo technique eigenstructure assignment matrix algebra interpolation algorithms Direction of arrival estimation direction finding

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Gaussian Processes for Direction-of-Arrival Estimation With Random arrays

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2019年第11期18卷 2297-2300页

作者： Gupta, Arjun Christodoulou, Christos G. Luis Rojo-Alvarez, Jose Martinez-Ramon, Manel Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Univ Rey Juan Carlos Dept Signal Theory & Commun Fuenlabrada 28943 Spain

Gaussian process regression for direction-of-arrival (DOA) estimation in random linear arrays is formally introduced in this letter, a novel methodology to estimate or interpolate information in the complex domain is implemented to address the correlation between the real and complex variables of the signal model. The impinging waveform over the array aperture is sampled nonuniformly in the spatial domain using antenna elements scattered in an arbitrary manner. The output from the random array is subsequently mapped to the output of a virtual uniform array composed of an equal number of elements as the original array and a separation amounting to half a wavelength of the carrier frequency. The interpolated signal vector is finally the input to a standard root-MuSiC algorithm, which computes the DOA of incoming signals using a subspace methodology. The efficacy and robustness of the algorithm is substantiated through Monte Carlo simulations with respect to various scenarios, closely replicating real-time variations in the communication channel.

关键词： array interpolation complex Gaussian processes (GPs) direction of arrival (DOA) GP regression nonuniform array processing root-MuSiC signal interpolation unsupervised learning

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Two-dimensional wideband interpolated Root-MUSIC applied to measured seismic data

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1998年第8期46卷 2263-2267页

作者： Sidorovich, DV Gershman, AB Ruhr Univ Bochum Dept Elect Engn Signal Theory Grp Bochum Germany

We apply two-dimensional (2-D) elementspace and beamspace extensions of Friedlander's wideband interpolated root-MUSIC technique to azimuth-velocity source location using measured seismic data from the GERESS array. Real data processing shows that 2-D interpolated root-MUSIC is-able to estimate the parameters of a typical seismic source with good accuracy. The use of interpolated root-MUSIC and dimension reduction via beamspace is motivated by dramatic reduction of processing time, allowing efficient on-line implementation.

关键词： array interpolation Root-MUSIC wideband direction finding

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