Open AccessModified generalised likelihood ratio test for detecting a regular respiratory signal in through‐wall life detection
Author(s) -
Li Xin,
Li Ye
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0085
Subject(s) - likelihood ratio test , detection theory , computer science , radar , signal to noise ratio (imaging) , additive white gaussian noise , signal (programming language) , detector , speech recognition , pattern recognition (psychology) , gaussian noise , white noise , artificial intelligence , statistical power , statistics , mathematics , telecommunications , programming language
In disaster rescue, trapped survivors with regular respiration can be located, by detecting regular respiratory signals (RRSs) acquired with life‐detection radar systems. RRSs are often weak in these scenarios, due to the attenuation of the electromagnetic waves that propagate through debris. Thus, detecting RRSs under low signal‐to‐noise ratio is a key challenge in this application. In this study, RRS detection in additive white Gaussian noise was investigated from a statistical signal processing viewpoint, and a modified generalised‐likelihood ratio test (GLRT) was derived. With proper parameter settings, the modified GLRT (MG) could achieve a notable detection gain over the periodogram test and the harmogram test, two classical periodic signal detectors. Thus, the proposed MG could be used to improve the detection performance of the life‐detection radar systems used in disaster rescue applications.