On detection performance and system configuration of MIMO radar

Tang, Jun; Wu, Yong; Peng, Yingning; Wang, Xiutan

doi:10.1007/s11432-009-0046-4

Cited by 13 publications

(20 citation statements)

References 5 publications

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“…When the SNR is greater than −10 dB and the number of transmitter equal to that of the receiver, the system has the best detection performance. When the SNR is less than −10 dB, the case of M = 3 provides the best performance, and the results are different from literature [25], which result from the different considered scenes, i.e., with or without multipath environment.…”

Section: Numerical Simulationscontrasting

confidence: 79%

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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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Section: Numerical Simulationscontrasting

confidence: 79%

“…In [24], the waveform design methods for the optimization the CRB matrix was discussed, under a total power constraint. In [25], the authors used the relative entropy as the criterion of the configuration optimization both for MIMO radar systems and phased-array radar. Some interesting results are presented.…”

Section: Introductionmentioning

confidence: 99%

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

Ding¹,

Chen²,

Wang³

et al. 2013

PIER B

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“…In the other class, the transmit antennas are sparsely spaced to obtain spatial diversity. In the latter class, the antennas in the receiver array may be either widely separated to achieve receive spatial diversity for non-coherent detection [1,3,10,13,16,17], or closely spaced to implement coherent processing, e.g., direction finding [2,13]. If non-coherent sub-arrays are used in the transmit and receive arrays, both spatial diversity and coherent processing gain can be obtained [6,13,18].…”

Section: Introductionmentioning

confidence: 99%

On the maximum likelihood method for target localization using MIMO radars

Xia

Liao

2010

Sci. China Inf. Sci.

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A multiple-input multiple-output (MIMO) radar uses multiple antennas to simultaneously transmit multiple independent probing signals, and uses multiple antennas to receive the backscattered signals. The modeling of MIMO radar with transmit spatial diversity and coherent reception is addressed herein. The maximum likelihood (ML) method for parameter estimation using MIMO radars is considered, and two approximate ML algorithms are proposed. In the uniform noise scenario, one of the proposed algorithms performs similarly to the delay-and-sum beamformer which is optimal in the ML sense in single target case, while it outperforms the other proposed approximate ML algorithm at the cost of more computational load. In the non-uniform noise scenario, the proposed approximate ML algorithms both outperform the delay-and-sum beamformer. The efficiency of the proposed methods is validated by the simulation results.

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“…Guan et al [26] investigated the detection problem of the MIMO radar system with distributed apertures in Gaussian colored noise and partially correlated observation channels. Tang and Huang [27] introduced relative entropy as a measure to radar detection theory and analyzed the detection performance of MIMO radar and phased array radar. Tang et al [28] investigated detection performance of MIMO radar for Rician target.…”

Section: Introductionmentioning

confidence: 99%

Fluctuating Target Detection in Low-Grazing Angle With Mimo Radar

Ding¹,

Chen²,

Li³

et al. 2013

PIER

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Abstract-This paper focuses on the fluctuating target detection in low-grazing angle using Multiple-input Multiple-output (MIMO) radar systems with widely separated antennas, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes, which is equivalent to improve the signal-to-noise ratio (SNR) by using multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced for fluctuating target in low-grazing angle. Using the Neyman-Pearson sense, the detectors of fluctuating targets, i.e., Swerling 1-4, with multipath are analyzed. Finally, the simulation results show that the performance can be enhanced markedly when the multipath effects are considered.

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On detection performance and system configuration of MIMO radar

Cited by 13 publications

References 5 publications

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

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

On the maximum likelihood method for target localization using MIMO radars

Fluctuating Target Detection in Low-Grazing Angle With Mimo Radar

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