Open AccessUWB Radar Target Detection Based on Hidden Markov Models
Author(s) -
Ganlin Zhao,
Qilian Liang,
Tariq S. Durrani
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2839690
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In this paper, we propose ultra-wideband (UWB) radar target detection approach based on Hidden Markov Models (HMMs). HMMs are used as a classifier to identify signal with the presence of target in a background clutter and the pure clutter response signal. Time-frequency features are extracted and features have less correlation to each other are selected based on the feature covariance matrix and fed into HMMs. The detection experiments are conducted in two different scenarios: sense-through-foliage target detection and sense-through-wall human detection. The sense-through-foliage data set contains poor quality UWB radar return echoes using low amplitude transmitting pulses. Data collected from different radar locations are tested and detection results are presented. Sense-through-wall data are collected using different UWB radar and the target is human standing behind different types of walls. HMMs parameters are also investigated to optimally model UWB radar signals for target detection.
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