On detection performance of MIMO radar for Rician target

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

doi:10.1007/s11432-009-0134-5

Cited by 10 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

On the maximum likelihood method for target localization using MIMO radars

Xia

Liao

2010

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The divergence factor can be derived solely from geometrical considerations. A widely accepted approximation for the divergence factor D is given (13) where ( is the complex amplitude due to the target character istics and Ie is the carrier frequency.…”

Section: Multipath Ge Ometry Modelmentioning

confidence: 99%

“…Tang et al [12] introduced relative entropy as a measure to radar detection theory and analyzed the detection performance of MIMO radar and phased array radar. The authors in [13] investigated detection performance of MIMO radar for Rician target. In [14], the optimal detector in the Neyman-Pearson sense was derived for the statistical MIMO 978-1-4673-2197-6/12/$31.00 ©2012 IEEE radar using orthogonal waveforms.…”

Section: Introductionmentioning

confidence: 99%

Fluctuating target detection in low-grazing angle with Multi-input Multiple-output radar

Ding

Chen

You³

et al. 2012

2012 IEEE 11th International Conference on Signal Processing

View full text Add to dashboard Cite

Ahstract-This paper focuses on the fluctuating target detec tion 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. 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, detector of fluctuating target with multipath is analyzed. The simulation results show that the performance can be enhanced markedly when the multipath effects are considered.

show abstract

“…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. In [29], the optimal detector in the Neyman-Pearson sense was derived for the statistical MIMO radar using orthogonal waveforms.…”

Section: Introductionmentioning

confidence: 99%

Fluctuating Target Detection in Low-Grazing Angle With Mimo Radar

Ding¹,

Chen²,

Li³

et al. 2013

PIER

View full text Add to dashboard Cite

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.

show abstract

On detection performance of MIMO radar for Rician target

Cited by 10 publications

References 10 publications

On the maximum likelihood method for target localization using MIMO radars

On the maximum likelihood method for target localization using MIMO radars

Fluctuating target detection in low-grazing angle with Multi-input Multiple-output radar

Fluctuating Target Detection in Low-Grazing Angle With Mimo Radar

Contact Info

Product

Resources

About