For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Adaptive Two-Step Bayesian Generalized Likelihood Ratio Test Algorithm for Low-Altitude Detection
Hao ZHOU Guoping HU Junpeng SHI Bin XUE
IEICE TRANSACTIONS on Communications
Publication Date: 2019/03/01
Online ISSN: 1745-1345
Type of Manuscript: PAPER
Category: Antennas and Propagation
low-altitude, multipath, clutter, target detection, generalized likelihood ratio test,
Full Text: PDF(1.6MB)
>>Buy this Article
The low-altitude target detection remains a difficult problem in MIMO radar. In this paper, we propose a novel adaptive two-step Bayesian generalized likelihood ratio test (TB-GLRT) detection algorithm for low-altitude target detection. By defining the compound channel scattering coefficient and applying the K distributed clutter model, the signal models for different radars in low-altitude environment are established. Then, aiming at the problem that the integrals are too complex to yield a closed-form Neyman-Pearson detector, we assume prior knowledge of the channel scattering coefficient and clutter to design an adaptive two-step Bayesian GLRT algorithm for low-altitude 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 low-altitude environment. With the TB-GLRT 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.