Hidden Markov Model-Based Sense-Through-Foliage Target Detection Approach

被引：0

作者：

Zhao, Ganlin ^{[1
]}

Liang, Qilian ^{[1
]}

Durrani, Tariq S. ^{[2
]}

机构：

[1] Univ Texas Arlington, Dept Elect Engn, Arlington, TX 76019 USA

[2] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow, Lanark, Scotland

来源：

COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL II: SIGNAL PROCESSING | 2020年 / 516卷

关键词：

Radar target detection; Foliage; UWB; HMM;

D O I：

10.1007/978-981-13-6504-1_84

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In this paper, we propose sense-through-foliage target detection approach based on Hidden Markov Models (HMMs). Separate Hidden Markov Models are trained for signals containing target signature and no target (clutter), respectively. Less correlated features are selected as input of Hidden Markov Models for training and testing. Foliage data is collected from three different UWB radar locations, and experimental results show that position 1 data gives the best detection result. All three locations have above 0.8 AUC from the ROC curves.

引用

页码：695 / 701

页数：7

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