Spatial Diversity in Radars—Models and Detection Performance

Fishler, E.; Haimovich, Alexander M.; Blum, Rick S.; Cimini, Leonard J.; Chizhik, Dmitry; Valenzuela, R.A.

doi:10.1109/tsp.2005.862813

Cited by 1,384 publications

(851 citation statements)

References 20 publications

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“…For the traditional phased-array radar systems which utilize an array of closely spaced sensors, their received signal model is given by [14]:…”

Section: Multipath Geometry Model For Phased-array Radarmentioning

confidence: 99%

“…In [14], the deflection coefficient, called "detectors SNR", is adopted to describe the detection performance of MIMO radar system. The expressions of deflection coefficient is…”

Section: System Configuration Of Mimo Radarmentioning

confidence: 99%

“…Fishler et al focused on the application of the target spatial diversity to improve detection performance and demonstrated that statistical MIMO radar provided great improvements over other types of array radars [14]. For low-grazing angle detection of MIMO radar, the authors in [15] utilized the time reversal technique in a multipath environment to achieve high target detectability.…”

Section: Introductionmentioning

confidence: 99%

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Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Ding¹,

Chen²,

Wang³

et al. 2013

PIER B

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Abstract-In this paper, we focus on target detection and system configuration optimization of Multiple-input Multiple-output (MIMO) radar in low-grazing angle, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient, considering the curved earth effect, is derived. Then, the general signal model for MIMO radar is introduced for low-grazing angle. Using the NeymanPearson sense, the detector of MIMO radar with multipath is analyzed. We use the deflection coefficient as a criterion of system configuration both for MIMO radar and phased-array radar. The simulation results show that the performance can be enhanced markedly when the multipath effects are considered, and the optimal configuration of phased-array radar is with the same number of transmitters as that of receivers, however, the optimal configuration of MIMO radar depends on the signal-to-noise ratio (SNR).

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“…For the traditional phased-array radar systems which utilize an array of closely spaced sensors, their received signal model is given by [14]:…”

Section: Multipath Geometry Model For Phased-array Radarmentioning

confidence: 99%

“…In [14], the deflection coefficient, called "detectors SNR", is adopted to describe the detection performance of MIMO radar system. The expressions of deflection coefficient is…”

Section: System Configuration Of Mimo Radarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Ding¹,

Chen²,

Wang³

et al. 2013

PIER B

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“…Multi-input multi-output (MIMO) radar has attracted more and more attention recently [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Unlike the traditional phased-array radar which can only transmit scaled versions of a single waveform, MIMO radar can use multiple transmitting elements to transmit arbitrary waveforms.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the traditional phased-array radar which can only transmit scaled versions of a single waveform, MIMO radar can use multiple transmitting elements to transmit arbitrary waveforms. Two categories of MIMO radar systems can be classified by the configuration of the transmitting and receiving antennas: (1) MIMO radar with widely separated antennas (see, e.g., [1,2]), and (2) MIMO radar with colocated antennas (see, e.g., [3]). For MIMO radar with widely separated antennas, the transmitting and receiving elements are widely spaced such that each views a different aspect of the target.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of MIMO radar waveform and biased estimator with prior information in the presence of clutter

Wang

Liao

Liu

et al. 2011

EURASIP J. Adv. Signal Process.

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In this article, we consider the problem of joint optimization of multi-input multi-output (MIMO) radar waveform and biased estimator with prior information on targets of interest in the presence of signal-dependent noise. A novel constrained biased Cramer-Rao bound (CRB) based method is proposed to optimize the waveform covariance matrix (WCM) and biased estimator such that the performance of parameter estimation can be improved. Under a simplifying assumption, the resultant nonlinear optimization problem is solved resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution of the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

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Adaptive Radar

Smith¹

1999

Wiley Encyclopedia of Electrical and Electronics Engineering

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The sections in this article are Adaptive Radar Fundamentals Statistical Model for Adaptive Radar Optimum Adaptive Processing Space–Time Adaptive Processing Adaptive Detection Adaptive Weight Computation Adaptive Radar Estimation Conclusions

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Spatial Diversity in Radars—Models and Detection Performance

Cited by 1,384 publications

References 20 publications

Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Joint optimization of MIMO radar waveform and biased estimator with prior information in the presence of clutter

Adaptive Radar

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