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multiple-input multiple-output radar for Lesion Classification in Ultrawideband Breast Imaging

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2010年第1期4卷 187-201页

作者： Chen, Yifan Craddock, Ian James Kosmas, Panagiotis Ghavami, Mohammad Rapajic, Predrag Univ Greenwich Sch Engn Chatham ME4 4TB Kent England Guangxi Univ Sch Comp Elect & Informat Guangxi 530004 Peoples R China Univ Bristol Dept Elect & Elect Engn Bristol BS8 1UB Avon England Kings Coll London Div Engn London WC2R 2LS England

This paper studies the problem of applying multiple-input multiple-output (MIMO) radar techniques for lesion classification in ultrawideband (UWB) breast imaging. Ongoing work on this topic has suggested that benign and malignant masses, which usually possess remarkable architectural differences, could be distinguished by exploiting their morphology-dependent UWB microwave backscatter. We have previously approached this problem by deriving the complex natural resonances of the late-time target response, where the damping factors vary with the border profiles of anomalies. In this paper, we investigate the potential advantage of MIMO radar to enhance the resonance scattering phenomenon in breast tissue discrimination. MIMO radar can choose freely the probing signals transmitted via its antennas to exploit the independence between signals at the array elements, thereby enhancing the performance of target classification. Based on the observed damping factors and the receiver operating characteristics at different classifiers, which correspond to various diversity paths in the MIMO radar system, two data-fusion rules are proposed for robust lesion differentiation. Finally, numerical examples are provided to demonstrate the efficacy of the proposed imaging technique.

关键词： Breast cancer detection lesion morphology microwave imaging multiple-input multiple-output radar robust lesion classification

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Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

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Chinese Physics B 2015年第11期24卷 70-76页

作者：文方青张弓贲德 College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing Research Institute of Electronics Technology

This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.

关键词： multiple-input multiple-output radar random arrays direction of arrival estimation sparseBayesian learning

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Robust and fast beamforming with magnitude response constraints for multiple-input multiple-output radar

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IET radar SONAR AND NAVIGATION 2016年第3期10卷 610-616页

作者： Yu, HongBo Feng, DaZheng Nie, WeiKe Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China NW Univ Xian Sch Informat & Technol Xian 710127 Peoples R China

In this study, a new robust and fast beamformer is developed for multiple-input multiple-output radar to improve the robustness against steering vector (SV) mismatches. A convex robust model with magnitude response constraints (MRC) is established by using the conjugate symmetry characteristic of the transmitted-received SV. To reduce the computational complexity and the number of samples required, the full-dimensional weight vector (WV) is expressed as the Kronecker product of the transmitted and received array WVs. Thus, the convex problem is converted into a bi-quadratic cost function and it is solved by combining the bi-iterative algorithm and convex quadratic program. The proposed beamformer can flexibly control the beamwidth of the robust region and achieve high output signal-to-interference-plus-noise ratio. Moreover, the proposed method has lower computational complexity and faster sample convergence rate as compared with the traditional MRC-iterative second-order cone programming and MRC-semi-definite programming beamformers. Finally, numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed beamformer.

关键词： array signal processing MIMO radar iterative methods quadratic programming convex programming robust fast beamforming magnitude response constraints multiple-input multiple-output radar steering vector mismatches MRC full-dimensional weight vector WV Kronecker product bi-quadratic cost function bi-iterative algorithm convex quadratic program numerical simulations

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Robust minimum variance multiple-input multiple-output radar beamformer

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IET SIGNAL PROCESSING 2013年第9期7卷 854-862页

作者： Zhang, Wei Wang, Ju Wu, Siliang Beijing Inst Technol Dept Elect & Informat Beijing 100081 Peoples R China

A robust minimum variance (MV) beamforming approach is proposed for improving the robustness of multiple-input multiple-output (MIMO) radar against the mismatches of the steering vector and the finite sample effects. In contrast to existing robust MV beamformers (RMVBs), the proposed RMVB utilises a specific structured model of virtual steering vector (also named transmit-receive steering vector) of MIMO radar rather than the commonly used unstructured model in phased-array radar. The basic idea of the proposed RMVB is to estimate the desired transmit and receive steering vectors under two quadratic constraints. To solve this problem, an iterative algorithm is developed. Simulations are provided to confirm the effectiveness of the proposed method.

关键词： array signal processing iterative methods radar signal processing MIMO radar robust minimum variance MIMO radar beamformer robust MV beamforming approach multiple-input multiple-output radar finite sample effects virtual steering vector transmit-receive steering vector estimation phased-array radar quadratic constraint iterative algorithm

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Robust design of multiple-input multiple-output radar waveform covariance matrix in the presence of clutter

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JOURNAL OF APPLIED REMOTE SENSING 2016年第1期10卷 035005-1-035005-15页

作者： Guo, Rongyan Wang, Hongyan Zhoukou Normal Univ Sch Phys & Elect Engn Zhoukou 466001 Henan Province Peoples R China Dalian Univ Coll Informat Engn Dalian 116622 Liaoning Peoples R China

In this work, the issue of robust waveform optimization is addressed in the presence of clutter to improve the worst-case estimation accuracy for collocated multiple-input multiple-output (MIMO) radar. Robust design is necessary due to the fact that waveform design may be sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, the robust waveform covariance matrix design is formulated here on the basis of Cramer-Rao Bound to ease this sensitivity systematically for improving the worst-case accuracy. To tackle the resultant complicated and nonlinear problem, a new diagonal loading (DL)-based iterative approach is developed, in which the inner optimization problem can first be decomposed to some independent subproblems by using the Hadamard's inequality, and then these subproblems can be reformulated into convex issues by using DL method, as well as the outer optimization problem can also be relaxed to a convex issue by translating the nonlinear function into a linear one, and, hence, both of them can be solved very effectively. An optimal solution to the original problem can be obtained via the least-squares fitting of the solution acquired by the iterative approach. Numerical simulations show the efficiency of the proposed method. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： robust waveform covariance matrix design multiple-input multiple-output radar diagonal loading parameter estimation

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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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Design of spectrally compatible waveform with constant modulus for colocated multiple-input multiple-output radar

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IET radar SONAR AND NAVIGATION 2019年第8期13卷 1373-1388页

作者： Wang, Xinhai Zhang, Gong Zhang, Yu Wang, Qixia Leung, Henry Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn 29 Jiangjun Ave Nanjing 211100 Jiangsu Peoples R China Minist Educ Key Lab Radar Imaging & Microwave Photon 29 Jiangjun Ave Nanjing 211100 Jiangsu Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

This study investigates the problem of designing unimodular waveform with spectral compatibility for multiple-input multiple-output radar in the presence of multiple targets and the signal-dependent interferences. A new approach is proposed to optimise the spectrally compatible radar waveform subject to not only the constant modulus constraint and the similarity constraint but also the signal-to-interference-plus-noise ratio (SINR) requirements for targets, aiming at suppressing the radar energy in some space-frequency areas occupied by other cooperative communication systems. However, the formulated optimisation problem is NP-hard because of the existence of the non-convex constraints. Based on the dual ascent framework, the authors develop an iterative algorithm to solve this challenging problem. The receive filter is obtained at the beginning of each iteration with the off-the-shelf technique and an innovative method termed phase-only dual ascent method is proposed to get the desired waveform. Additionally, they analyse the performance of the waveforms designed under the global SINR constraint and the local SINR constraint, respectively. Moreover, the convergence of the proposed algorithm is discussed concretely. Finally, the numerical simulations are provided to show the validity of the proposed approach.

关键词： radar signal processing MIMO radar iterative methods concave programming radar interference multiple-input multiple-output radar unimodular waveform spectral compatibility signal-dependent interferences constant modulus constraint signal-to-interference-plus-noise ratio requirements radar energy formulated optimisation problem desired waveform global SINR constraint local SINR constraint spectrally compatible radar waveform cooperative communication systems colocated MIMO radar

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Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture

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IET radar SONAR AND NAVIGATION 2016年第4期10卷 689-698页

作者： Kong, Lingjiang Li, Na Cui, Guolong Yang, Haining Liu, Qing Huo Univ Elect Sci & Technol China Dept Signal & Informat Proc Chengdu 611731 Peoples R China Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

In this study, the authors consider the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure, but different power levels (textures). A Bayesian framework is exploited where both the textures and the structure are assumed to be random. Precisely, the textures follow Gamma distribution or inverse Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, two generalised likelihood ratio tests are derived. Finally, they evaluate the capabilities of the proposed detectors against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.

关键词： MIMO radar radar detection Bayes methods radar clutter gamma distribution multiple-input multiple-output radar compound-Gaussian clutter random texture adaptive Bayesian detection Gamma distribution inverse Gamma distribution inverse complex Wishart distribution generalised likelihood ratio tests

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Resolving Range Ambiguity via multiple-input multiple-output radar With Element-Pulse Coding

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 2770-2783页

作者： Xu, Jingwei Zhang, Yuhong Liao, Guisheng So, Hing Cheung Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

In this paper, we propose a novel radar framework, referred to as multiple-input multiple-output (MIMO) radar with element-pulse coding (EPC). The EPC employs Fourier basis in joint transmit elements and pulses, which has the advantages of simplicity and convenience for practical realization. It is shown that the EPC-MIMO radar is advantageous in discriminating different pulses and resolving range ambiguity. The discrimination of different pulses is achieved in the transmit spatial frequency domain, and the identifiability of range ambiguity increases with the transmit element number of the radar. Moreover, coherent processing in Doppler domain can be performed after decoding. The coding and decoding procedures are closely related though they are performed in transmitter and receiver, respectively. As the EPC-MIMO radar is capable of resolving range ambiguity, an enhanced parameter estimation approach is developed to obtain the angle and range region number estimates of target. The parameter estimation performance is assessed by the Cramer-Rao bound, which reveals useful and interesting findings. Simulation examples are provided to verify the signal properties in EPC-MIMO radar and to demonstrate its superiority in resolving range ambiguity over existing systems.

关键词： multiple-input multiple-output radar element-pulse coding range ambiguity resolution parameter estimation Cramer-Rao bound

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Target localisation techniques and tools for multiple-input multiple-output radar

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IET radar SONAR AND NAVIGATION 2009年第4期3卷 314-327页

作者： Godrich, H. Haimovich, A. M. Blum, R. S. New Jersey Inst Technol Newark NJ 07102 USA Lehigh Univ Bethlehem PA 18015 USA

This study presents a comparative study of coherent and non-coherent target localisation techniques for multiple-input multiple-output (MIMO) radar systems with widely distributed elements. Performance is evaluated based on closed-form solutions developed for the best linear unbiased estimator (BLUE) for each of the localisation methods. These estimators afford insights into the relation between radar locations, target location and localisation accuracy. In particular, the means squared error of the BLUE is factored into a term dependent on signal and processing characteristics and a term dependent on sensor locations. The latter is referred to as geometric dilution of precision (GDOP). The best achievable accuracy for the coherent case is obtained, and a comparative study with the non-coherent case is presented. MIMO radar systems with coherent processing are shown to benefit from a gain because of coherent processing among sensors. This gain is referred to as coherent localisation gain, and it is proportional to the ratio of the signal carrier frequency to the effective bandwidth (a large ratio for typical signals). The footprint of multiple transmit/receive sensors results in a gain, referred to as MIMO gain, for both processing techniques. The MIMO gain is proportional to the product of the number of transmitting and receiving sensors. Analysis of the MIMO gain through the use of GDOP contour maps demonstrate the achievable accuracy at various target locations for a given layout of sensors.

关键词： target localisation techniques sensor locations MIMO radar systems MIMO communication radar and radionavigation closed-form solutions Radio links and equipment radar multiple-input multiple-output radar Sensing devices and transducers geometric dilution of precision best linear unbiased estimator MIMO gain sensors

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