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Adaptive TwoStep Bayesian Generalized Likelihood Ratio Test Algorithm for LowAltitude Detection
Hao ZHOU Guoping HU Junpeng SHI Bin XUE
Publication
IEICE TRANSACTIONS on Communications
Vol.E102B
No.3
pp.571580 Publication Date: 2019/03/01
Online ISSN: 17451345
DOI: 10.1587/transcom.2017EBP3418
Type of Manuscript: PAPER Category: Antennas and Propagation Keyword: lowaltitude, multipath, clutter, target detection, generalized likelihood ratio test,
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Summary:
The lowaltitude target detection remains a difficult problem in MIMO radar. In this paper, we propose a novel adaptive twostep Bayesian generalized likelihood ratio test (TBGLRT) detection algorithm for lowaltitude target detection. By defining the compound channel scattering coefficient and applying the K distributed clutter model, the signal models for different radars in lowaltitude environment are established. Then, aiming at the problem that the integrals are too complex to yield a closedform NeymanPearson detector, we assume prior knowledge of the channel scattering coefficient and clutter to design an adaptive twostep Bayesian GLRT algorithm for lowaltitude target detection. Monte Carlo simulation results verify that the proposed detector has better performance than the square law detector, GLRT detector or Bayesian GLRT detector in lowaltitude environment. With the TBGLRT detector, the maximum detection probability can reach 70% when SNR=0dB and ν=1. Simulations also verify that the multipath effect shows positive influence on detection when SNR<5dB, and when SNR>10dB, the multipath effect shows negative influence on detection. When SNR>0dB, the MIMO radar, which keeps a detection probability over 70% with the proposed algorithm, has the best detection performance. Besides, the detection performance gets improved with the decrease of sea clutter fluctuation level.

