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10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Wen, Fangqing Wu, Lei Cai, Changxin Huang, Dongmei Yangtze Univ Natl Demonstrat Ctr Expt Electrotech & Elect Educ Jingzhou 434023 Peoples R China Naval Command Coll Dept Training Management Nanjing 210016 Peoples R China

ISBN: (纸本)9781538647523

In this paper, we investigate into joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input multiple-output (MIMO) radar in the presence of gain-phase error and mutual coupling error. An auxiliary sensors-based framework is proposed, the matched array measurement of the MIMO radar is formulated as a parallel factor (PARAFAC) model, which links the problem of joint DOD and DOA estimation to PARAFAC decomposition. Thereafter, the DODs and DOAs are obtained via least square strategy. The proposed method is computationally more efficient than the existing reduced-MUSIC method. Besides, it can achieve closed-form solutions for DODs and DOAs, which are paired automatically. Numerical experiments demonstrate the effectiveness of our method.

关键词： multiple-input multiple-output radar direction estimation mutual coupling gain-phase error position error parallel factor analysis

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Direction finding in bistatic MIMO radar with unknown spatially colored noise 23

Direction finding in bistatic MIMO radar with unknown spatia...

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23rd IEEE International Conference on Digital Signal Processing (DSP)

作者： Wen, Fangqing Zhang, Xinyu Yang, Fuzhou Zhang, Zijing Yangtze Univ Sch Elect & Informat Jingzhou 434023 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538668122;9781538668115

This paper addresses the problem of direction finding for bistutic MAIO radar in the presence of unknown spatially colored noise. Taking the stationary property of the colored noise into consideration, a covariance differencing-based method is presented. The proposed method has computational complexity very close to that of ESPRIT. Besides, it can suppress the colored noise without any virtual aperture loss. Therefore, it improves the accuracy of angle estimation compared with the existing de-noising algorithms. Numerical experiments are provided to examine the effectiveness and improvement of our algorithm.

关键词： multiple-input multiple-output radar direction estimation spatially colored noise

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Design and Calibration of a Portable 24-GHz 3-D MIMO FMCW radar with a Non-uniformly Spaced Array and RF Front-End Coexisting on the Same PCB Layer 13

Design and Calibration of a Portable 24-GHz 3-D MIMO FMCW Ra...

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13th IEEE Dallas Circuits and Systems Conference (DCAS)

作者： Peng, Zhengyu Nallabolu, Prateek Li, Changzhi Texas Tech Univ Dept Elect Engn Lubbock TX 79409 USA

ISBN: (纸本)9781538692622

This paper presents a portable 24-GHz multiple-input multiple-output (MIMO) radar with 16 transmit (Tx) channels and 16 receive (Rx) channels. The radar is intended for short-range localization and three-dimensional (3-D) imaging by detecting the ranges, azimuth angles, and zenith angles of targets in front of the radar. The radar is capable of two-dimensional (2-D) beamforming achieved using a non-uniformly spaced planar array. A prototype of this 16x16 radar has been built to demonstrate its short-range localization capability.

关键词： Frequency-modulated continuous-wave radar multiple-input multiple-output radar planar arrays time-division multiplexing

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Spatial multiplexing gain in MIMO radars with widely separated antennas

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IET SIGNAL PROCESSING 2018年第2期12卷 207-213页

作者： Majd, Mohammad Nazari Radmard, Mojtaba Chitgarha, Mohammad Mahdi Farina, Alfonso Bastani, Mohammad Hassan Nayebi, Mohammad Mahdi Sharif Univ Technol Dept Elect Engn Tehran Iran Selex ES Rome Italy

multiple-input multiple-output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors' goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS-MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS-MIMO radar in the case of multiple targets, suitable for such purpose;Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

关键词： MIMO radar radar antennas antenna arrays space division multiplexing wireless channels spatial multiplexing gain multiple-input multiple-output radar system performance enhancement widely separated antennas ambiguity function WS-MIMO radar MIMO wireless channel MIMO wireless system MIMO communication

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Bayesian target location and velocity estimation for multiple-input multiple-output radar

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IET radar SONAR AND NAVIGATION 2011年第6期5卷 666-670页

作者： Jiang, M. Niu, R. Blum, R. S. Nanjing Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China Syracuse Univ Dept EECS Syracuse NY 13244 USA Lehigh Univ ECE Dept Bethlehem PA 18015 USA

For a non-coherent multiple-input multiple-output radar system, the minimum mean square error (MMSE) estimator and maximum a posteriori (MAP) estimator of the target location and velocity, considered random unknown parameters, are formulated and the corresponding posterior Cramer-Rao lower bound (PCRLB) is derived. Moreover, numerical solutions for the proposed MMSE estimator and the PCRLB are obtained by using Monte-Carlo methods because of the absence of closed-form solutions. The numerical results show that the mean square errors (MSEs) of the MMSE estimate and the MAP estimate converge to the corresponding PCRLB as the signal-to-noise ratio (SNR) increases when the number of transmit and receive antennas is sufficiently large. A linear approximation can be used to simplify the MMSE estimation. It is shown in some simulations that the linear approximation of the MMSE estimate is accurate at high SNR values and the SNR needed for accurate approximation can be reduced by increasing the number of antennas employed.

关键词： Bayesian target location posterior Cramér-Rao lower bound signal-to-noise ratio MAP estimator velocity estimation transmit antennas least mean squares methods maximum likelihood estimation receive antennas radar equipment, systems and applications MMSE estimator maximum a posteriori estimator Bayes methods Single antennas multiple-input multiple-output radar minimum mean square error estimator Interpolation and function approximation (numerical analysis) target tracking radar antennas MIMO radar Other topics in statistics

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A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

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DIGITAL SIGNAL PROCESSING 2017年 69卷 193-203页

作者： Liu, Jing Zhou, Weidong Wang, Xianpeng Huang, Defeng (David) Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Nedlands WA Australia Hainan Univ Coll Informat Sci & Technol Haikou 570228 Hainan Peoples R China

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： multiple-input multiple-output radar Direction-of-arrival estimation Gain-phase errors Fourth-order cumulants Sparse representation

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Nuclear norm minimization framework for DOA estimation in MIMO radar

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SIGNAL PROCESSING 2017年 135卷 147-152页

作者： Wang, Xianpeng Wang, Luyun Li, Xiumei Bi, Guoan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Hainan Univ State Key Lab Marine Resource Utilizat South Chin Hainan 570228 Peoples R China Hainan Univ Coll Informat Sci & Technol Hainan 570228 Peoples R China Dalian Univ Technol City Coll Sch EEA Dalian Peoples R China Hangzhou Normal Univ Sch ISE Hangzhou Zhejiang Peoples R China

In this paper, the direction of arrival (DOA) estimation for noncircular sources in multiple-input multiple output (MIMO) radar is dealt with by a novel nuclear norm minimization (NNM) framework. The proposed method exploits the noncircular property of signals to extend the data model for doubling the array aperture. Then a block sparse model of the extended data is formulated without the influence of the unknown noncircularity phase, and a novel signal reconstruction algorithm based on nuclear norm minimization is proposed to recover the block-sparse matrix. In addition, a weight matrix based on the reduced dimensional noncircular Capon (RD NC-Capon) spectrum is designed to reweight the nuclear norm minimization for enhancing the sparsity of solution. Finally, the DOA is estimated from the non-zero blocks of the reconstructed matrix. Due to exploiting the extended array aperture and block-sparse information, the proposed method provides superior DOA estimation performance and higher angular resolution. Furthermore, the proposed method has a low sensitivity to the priori information on the number of sources. Simulation results are presented to verify the effectiveness and advantages of the proposed method.

关键词： multiple-input multiple-output radar Direction of arrival estimation Sparse representation Noncircular signal Nuclear norm minimization

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A novel unitary PARAFAC method for DOD and DOA estimation in bistatic MIMO radar

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SIGNAL PROCESSING 2017年 138卷 273-279页

作者： Xu, Baoqing Zhao, Yongbo Cheng, Zengfei Li, Hui Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Peoples R China

In this paper, a novel unitary parallel factor (U-PARAFAC) algorithm of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar is proposed. A real-valued tensor signal model is constructed by applying the traditional forward-backward averaging technique. Subsequently, the fact that the real-valued tensor follows a PARAFAC model is proved, thus the subspace-based high-order singular value decomposition (HOSVD) method can be avoided in the subsequent solving process. Furthermore, directly operating the real-valued loading factors instead of the signal subspace, traditional unitary ESPRIT (U-ESPRIT) method is firstly extended to the real-valued PARAFAC model. The new algorithm, which exploits the multidimensional structure and does not require the estimation of signal subspace, having good performance especially at low signal-to-noise ratio (SNR). More attractively, compared with classical tensor methods such as the PARAFAC algorithm and the unitary tensor-ESPRIT algorithm, the U-PARAFAC algorithm still performs well without sacrificing array aperture when targets are highly correlated or closely spaced. Additional angle pair-matching is not required. Simulation results verify the effectiveness of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multiple-input multiple-output radar Angle estimation Real-valued tensor Parallel factor decomposition

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Motion compensation for TDM MIMO radar by sparse reconstruction

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ELECTRONICS LETTERS 2017年第24期53卷 1604-1606页

作者： Hu, Xueyao Lu, Man Li, Yang Wang, Yanhua Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Beijing Racobit Elect Informat Technol Co Ltd Beijing Peoples R China

A motion-compensation method that applies sparse reconstruction (SR) to reconstruct the Doppler spectrum of targets based on a random transmission scheme is proposed for time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radar. Since the random transmission can eliminate the characteristic of periodic time-delay in conventional TDM scheme between transmit cycles, the angle information of a target is not affected by its motion. Therefore, the angle and velocity are no longer coupled with each other and can be estimated separately. This method not only overcome the space-frequency coupling problem but also enhances the unambiguous Doppler interval. Another advantage is that the method is valid even when the estimated target velocity is ambiguous. The results reported here offer the possibility of utilising SR to solve conventional TDM MIMO problems. The effectiveness of the proposed method is demonstrated by experimental results.

关键词： MIMO radar motion compensation radar imaging image reconstruction time division multiplexing motion-compensation method TDM MIMO radar sparse reconstruction SR Doppler spectrum reconstruction random transmission scheme time-division multiplexing multiple-input multiple-output radar periodic time-delay transmit cycles angle information space-frequency coupling problem

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Adaptive weight matrix design and parameter estimation via sparse modeling for MIMO radar

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SIGNAL PROCESSING 2017年 139卷 1-11页

作者： Gong, Pengcheng Wang, Wen-Qin Wan, Xianrong Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Sichuan Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430072 Hubei Peoples R China

Although sparse representation and sparse recovery algorithms for colocated multiple-input multiple output (MIMO) radar have received much attention, incoherence of the sensing matrix received few discussions. In this paper, we propose adaptive weight matrix design and parameter estimation via sparse modeling to improve the recovery performance for MIMO radar. First, a sparse framework is formulated for the MIMO array with decoupled transmit weight matrix and steering matrix. Next, a two stage method is proposed to optimize the two matrices to improve the DOA estimation performance. Finally, a sparse recovery approach based on l(q) (0 < q <= 1) norm optimization is developed to estimate the unknown target parameters. Furthermore, the algorithm can be iteratively implemented with the obtained closed-form solution for the optimization problem. The angle-amplitude estimation performance is examined by analyzing the Cramer-Rao lower bound (CRLB). Numerical results demonstrate that significant performance improvement has been achieved by the proposed sensing matrix optimization and adaptive weight matrix design approach. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Direction of arrival estimation multiple-input multiple-output radar Compressed sensing Sensing matrix Sparse representation

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