The Experts below are selected from a list of 6030 Experts worldwide ranked by ideXlab platform
Jisheng Dai - One of the best experts on this subject based on the ideXlab platform.
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real valued doa estimation for Uniform Linear Array with unknown mutual coupling
Signal Processing, 2012Co-Authors: Jisheng Dai, Dean ZhaoAbstract:In this paper, we propose a real-valued direction-of-arrival (DOA) estimation method for Uniform Linear Arrays (ULAs) in the presence of unknown mutual coupling. By taking advantage of the special structure of the mutual coupling matrix for ULAs, the effect of mutual coupling is eliminated by the inherent mechanism of the proposed method. Moreover, the computational complexity is reduced by a factor of at least four after further performing a unitary transformation capable of converting a complex covariance matrix into a real one. We also investigate the performance loss due to the imperfect structure of the mutual coupling matrix under the NEC-2 code. Experimental results with respect to the NEC-2 code illustrate that our new method even outperforms a state-of-the-art method in the literature.
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spatial smoothing for direction of arrival estimation of coherent signals in the presence of unknown mutual coupling
Iet Signal Processing, 2011Co-Authors: Jisheng DaiAbstract:There is rarely a method that can deal with the direction of arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling. Based on the special structure of mutual coupling matrix for Uniform Linear Array (ULA), an improved spatial smoothing algorithm for DOA estimation of coherent signals is proposed. The DOAs can be accurately estimated without any calibration sources since the effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm. Simulation results demonstrate the effectiveness of the algorithm.
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doa estimation for Uniform Linear Array with mutual coupling
IEEE Transactions on Aerospace and Electronic Systems, 2009Co-Authors: Jisheng DaiAbstract:An algorithm is presented for direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling based on the generalized eigenvalues utilizing signal subspace eigenvectors (GEESE) algorithm for Uniform Linear Array (ULA). It is not an iterative algorithm, and a spectral peak search is not required. The DOA can be accurately estimated without any calibration sources since the effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm. An algorithm for estimating the mutual coupling coefficients is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.
Zhiguo Shi - One of the best experts on this subject based on the ideXlab platform.
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coprime Array adaptive beamforming based on compressive sensing virtual Array signal
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Chengwei Zhou, Nathan A Goodman, Wenzhan Song, Zhiguo ShiAbstract:In this paper, we propose a novel adaptive beamforming algorithm for coprime Array by compressive sensing the virtual Uniform Linear Array signal. Based on the idea of coprime sampling, a much longer virtual Uniform Linear Array can be generated from a coprime Array. With a compressive sensing matrix, a connection can be built between the coprime Array with fewer physical sensors and the virtual Uniform Linear Array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual Array. The performance increment provided by the virtual Array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual Array is expected to obtain much better performance than those using the coprime Array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.
Tomas Mckelvey - One of the best experts on this subject based on the ideXlab platform.
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auto calibration of co located Uniform Linear Array antennas
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019Co-Authors: Tomas MckelveyAbstract:An algorithm for auto-calibration of a group of co-located Uniform Linear Array antennas is presented. If the number of signal sources are known and, for at least one Array, the ratio of the gains between two consecutive antenna elements is known, the individual unknown antenna gains can be estimated. The method is based on determining the antenna calibration parameters such that a matrix built from the Array snapshots has a given rank. A numerical example illustrates the performance of the method. The numerical results suggest that the method is consistent in SNR.
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auto calibration of Uniform Linear Array antennas
European Signal Processing Conference, 2019Co-Authors: Tomas MckelveyAbstract:Calibration is instrumental to realize the full performance of a measurement system. In this contribution we consider the calibration of a Uniformly Linear Array antenna where we assume each antenna element has an unknown complex gain. We present an algorithm which can be used to calibrate the Array without full knowledge of the environment. Particularly, if the number of signal sources are known we show that we can determine the individual unknown antenna gains up to an ambiguity parametrized by a single complex scalar. If the ratio of the complex gains between two consecutive elements is also known, this ambiguity is resolved. The method is based on determining the antenna calibration parameters such that the Hankel matrix of the Array snapshots has a given rank. A numerical example illustrates the performance of the method. The numerical results suggest that the method is consistent in SNR.
Dean Zhao - One of the best experts on this subject based on the ideXlab platform.
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real valued doa estimation for Uniform Linear Array with unknown mutual coupling
Signal Processing, 2012Co-Authors: Jisheng Dai, Dean ZhaoAbstract:In this paper, we propose a real-valued direction-of-arrival (DOA) estimation method for Uniform Linear Arrays (ULAs) in the presence of unknown mutual coupling. By taking advantage of the special structure of the mutual coupling matrix for ULAs, the effect of mutual coupling is eliminated by the inherent mechanism of the proposed method. Moreover, the computational complexity is reduced by a factor of at least four after further performing a unitary transformation capable of converting a complex covariance matrix into a real one. We also investigate the performance loss due to the imperfect structure of the mutual coupling matrix under the NEC-2 code. Experimental results with respect to the NEC-2 code illustrate that our new method even outperforms a state-of-the-art method in the literature.
Braham Himed - One of the best experts on this subject based on the ideXlab platform.
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doa estimation exploiting a Uniform Linear Array with multiple co prime frequencies
Signal Processing, 2017Co-Authors: Si Qin, Yimin Zhang, Moeness G Amin, Braham HimedAbstract:The co-prime Array, which utilizes a co-prime pair of Uniform Linear sub-Arrays, provides a systematical means for sparse Array construction. By choosing two co-prime integers M and N, O ( MN ) co-Array elements can be formed from only O ( M + N ) physical sensors. As such, a higher number of degrees-of-freedom (DOFs) is achieved, enabling direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In this paper, we propose an alternative structure to implement co-prime Arrays. A single sparse Uniform Linear Array is used to exploit two or more continuous-wave signals whose frequencies satisfy a co-prime relationship. This extends the co-prime Array and filtering to a joint spatio-spectral domain, thereby achieving high flexibility in Array structure design to meet system complexity constraints. The DOA estimation is obtained using group sparsity-based compressive sensing techniques. In particular, we use the recently developed complex multitask Bayesian compressive sensing for group sparse signal reconstruction. The achievable number of DOFs is derived for the two-frequency case, and an upper bound of the available DOFs is provided for multi-frequency scenarios. Simulation results demonstrate the effectiveness of the proposed technique and verify the analysis results. HighlightsWe have proposed a novel co-prime Array structure that exploits a single Uniform Linear Array and a co-prime set of frequencies.We have derived the analytical expressions of the Array aperture and the number of DOFs with respect to two and multiple co-prime frequencies.DOA estimation is formulated as a group sparse compressive sensing problem, and is effectively solved by the complex multi-task Bayesian compressive sensing technique.
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doa estimation using a sparse Uniform Linear Array with two cw signals of co prime frequencies
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013Co-Authors: Yimin Zhang, Moeness G Amin, Fauzia Ahmad, Braham HimedAbstract:In this paper, we propose the use of a sparse Uniform Linear Array to estimate the direction-of-arrival (DOA) of more sources than the number of sensors by exploiting two continuous wave signals whose frequencies satisfy a certain co-prime relationship. This extends the co-prime Array and filter concept, which was developed in either the spectral or spatial domain, to a joint spatio-spectral domain, thereby achieving high flexibility in Array structure design to meet the degrees-of-freedom and system complexity constraints. The DOA estimation is implemented in the difference co-Array context to avoid spatial undersampling, and group sparsity based compressive sensing techniques are used to determine the direction of signal arrivals.
