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14
Tensor algebra and multidimensional harmonic retrieval in signal processing for
 MIMO radar,” IEEE Transactions on Signal Processing,
, 2010
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MIMO radar using compressive sampling
 IEEE Journal of Selected Topics in Signal Processing
, 2010
"... Abstract — A distributed MIMO radar is considered, in which the transmit and receive antennas belong to nodes of a small scale wireless network. The transmit waveforms could be uncorrelated, or correlated in order to achieve a desirable beampattern. The concept of compressive sampling is employed at ..."
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Abstract — A distributed MIMO radar is considered, in which the transmit and receive antennas belong to nodes of a small scale wireless network. The transmit waveforms could be uncorrelated, or correlated in order to achieve a desirable beampattern. The concept of compressive sampling is employed at the receive nodes in order to perform direction of arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOAs of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center.
Adaptive Algorithms to Track the PARAFAC Decomposition of a ThirdOrder Tensor
, 2009
"... —The PARAFAC decomposition of a higherorder tensor is a powerful multilinear algebra tool that becomes more and more popular in a number of disciplines. Existing PARAFAC algorithms are computationally demanding and operate in batch mode—both serious drawbacks for online applications. When the dat ..."
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Cited by 13 (2 self)
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—The PARAFAC decomposition of a higherorder tensor is a powerful multilinear algebra tool that becomes more and more popular in a number of disciplines. Existing PARAFAC algorithms are computationally demanding and operate in batch mode—both serious drawbacks for online applications. When the data are serially acquired, or the underlying model changes with time, adaptive PARAFAC algorithms that can track the sought decomposition at low complexity would be highly desirable. This is a challenging task that has not been addressed in the literature, and the topic of this paper. Given an estimate of the PARAFAC decomposition of a tensor at instant t, we propose two adaptive algorithms to update the decomposition at instant t +1, the new tensor being obtained from the old one after appending a new slice in the ’time ’ dimension. The proposed algorithms can yield estimation performance that is very close to that obtained via repeated application of stateofart batch algorithms, at orders of magnitude lower complexity. The effectiveness of the proposed algorithms is illustrated using a MIMO radar application (tracking of directions of arrival and directions of departure) as an example.
A PARAFACbased technique for detection and localization of multiple targets in a MIMO radar system
 In Proc. of IEEE Int. Conf. Acoust., Speech, Signal Processing
, 2009
"... In this paper, we show that the problem of detection and localization of multiple targets in a bistatic MIMO radar system can be solved by Parallel Factor (PARAFAC) analysis. Our method is deterministic and fully capitalizes on the strong algebraic structure of the received data, where the Radar Cro ..."
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In this paper, we show that the problem of detection and localization of multiple targets in a bistatic MIMO radar system can be solved by Parallel Factor (PARAFAC) analysis. Our method is deterministic and fully capitalizes on the strong algebraic structure of the received data, where the Radar Cross Section (RCS) fluctuation is not regarded as a nuisance parameter but rather as a source of time diversity. Simulation results show that our technique outperforms existing beamformingbased radar imaging methods at a lower complexity. Index Terms — MIMO radars, PARAFAC, DOADOD estimation 1.
MIMO radar detection in nonGaussian and heterogeneous clutter
, 2010
"... Abstract—In this paper, the generalized likelihood ratio testlinear quadratic (GLRTLQ) has been extended to the multipleinput multipleoutput (MIMO) case where all transmit–receive subarrays are considered jointly as a system such that only one detection threshold is used. The GLRTLQ detector ..."
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Abstract—In this paper, the generalized likelihood ratio testlinear quadratic (GLRTLQ) has been extended to the multipleinput multipleoutput (MIMO) case where all transmit–receive subarrays are considered jointly as a system such that only one detection threshold is used. The GLRTLQ detector has been derived based on the Spherically Invariant Random Vector (SIRV) model and is constant false alarm rate (CFAR) with respect to the clutter power fluctuations (also known as the texture). The new MIMO detector is then shown to be textureCFAR as well. The theoretical performance of this new detector is first analytically derived and then validated using Monte Carlo simulations. Its detection performance is then compared to that of the wellknown Optimum Gaussian Detector (OGD) under Gaussian and nonGaussian clutter. Next, the adaptive version of the detector is investigated. The covariance matrix is estimated using the Fixed Point (FP) algorithm which enables the detector to remain texture and matrixCFAR. The effects of the estimation of the covariance matrix on the detection performance are also investigated. Index Terms—Detection performance, generalized likelihood ratio testlinear quadratic (GLRTLQ), multipleinput multipleoutput (MIMO) radar, nonGaussian clutter, Spherically Invariant Random Vector (SIRV). I.
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, 2007
"... The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering end maintaining the data needed, end completing and reviewing the collection of information. Send comm ..."
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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering end maintaining the data needed, end completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collecton of
Author: ZAHID OSCAR GÓMEZ URRUTIA MIMO Radar with Colocated Antennas: Theoretical Investigation, Simulations and Development of an Experimental Platform
, 2014
"... A MultipleInput MultipleOutput (MIMO) radar is a system employing multiple transmitters and receivers in which the waveforms to be transmitted can be totally independent. Compared to standard phasedarray radar systems, MIMO radars offer more degrees of freedom which leads to improved angular reso ..."
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A MultipleInput MultipleOutput (MIMO) radar is a system employing multiple transmitters and receivers in which the waveforms to be transmitted can be totally independent. Compared to standard phasedarray radar systems, MIMO radars offer more degrees of freedom which leads to improved angular resolution and parameter identifiability, and provides more flexibility for transmit beampattern design. The main issues of interest in the context of MIMO radar are the estimation of several target parameters (which include range, Doppler, and DirectionofArrival (DOA), among others). Since the information on the targets is obtained from the echoes of the transmitted signals, it is straightforward that the design of the waveforms plays an important role in the system accuracy. This document addresses the investigation of DOA estimation of nonmoving targets and waveform design techniques for MIMO radar with colocated antennas. Although narrowband MIMO radars have been deeply studied in the literature, the existing DOA estimation techniques have been usually proposed and analyzed from a theoretical point of view, often assuming ideal conditions. This thesis analyzes existing signal processing
TwoDimensional DirectionofArrival Estimation for Trilinear DecompositionBased Monostatic Cross MIMO Radar
"... A low complexity monostatic cross multiplein multipleout (MIMO) radar scheme is proposed in this paper. The minimumredundancy linear array (MRLA) is introduced in the cross radar to improve the efficiency of the array elements. The twodimensional directionofarrival (DOA) estimation problem link ..."
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A low complexity monostatic cross multiplein multipleout (MIMO) radar scheme is proposed in this paper. The minimumredundancy linear array (MRLA) is introduced in the cross radar to improve the efficiency of the array elements. The twodimensional directionofarrival (DOA) estimation problem links to the trilinear model, which automatically pairs the estimated twodimensional angles, requiring neither eigenvalue decomposition of received signal covariance matrix nor spectral peak searching. The proposed scheme performs better than the uniform linear arrays (ULA) configuration under the same conditions, and the proposed algorithm has less computational complexity than that of multiple signal classification (MUSIC) algorithm. Simulation results show the effectiveness of our scheme.
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"... 1 Schematic diagram of the receiver.Φl denotes the measurement matrix for the lth receive ..."
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1 Schematic diagram of the receiver.Φl denotes the measurement matrix for the lth receive