Subspace Optimization in Centralized Noncoherent MIMO Radar

Pratt, Thomas G.; Huang, Yih-Fang; Gong, Zhangbin; Lemmon, Mike

doi:10.1109/taes.2011.5751254

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…By exploiting the uncorrelated nature of spatiotemporal received signals, the effect of variation of target RCS can be optimised. In the existing literature, authors have addressed different target detection problems related to MIMO radars and proposed solutions to enhance the target detection performance [10, 11]. The multiple transmitters and receivers of MIMO radars can either be collocated or widely spaced.…”

Section: Introductionmentioning

confidence: 99%

Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter

Bolisetti

Patwary

Ahmed

et al. 2015

The Scientific World Journal

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The problem of optimising the target detection performance of MIMO radar in the presence of clutter is considered. The increased false alarm rate which is a consequence of the presence of clutter returns is known to seriously degrade the target detection performance of the radar target detector, especially under low SNR conditions. In this paper, a mathematical model is proposed to optimise the target detection performance of a MIMO radar detector in the presence of clutter. The number of samples that are required to be processed by a radar target detector regulates the amount of processing burden while achieving a given detection reliability. While Subspace Compressive GLRT (SSC-GLRT) detector is known to give optimised radar target detection performance with reduced computational complexity, it however suffers a significant deterioration in target detection performance in the presence of clutter. In this paper we provide evidence that the proposed mathematical model for SSC-GLRT detector outperforms the existing detectors in the presence of clutter. The performance analysis of the existing detectors and the proposed SSC-GLRT detector for MIMO radar in the presence of clutter are provided in this paper.

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Section: Introductionmentioning

confidence: 99%