Open AccessSignal Denoising of Multi Element Infrared Signal Based on Wavelet Transform
Author(s) -
Bingshan Lei,
Weina Hao,
Keding Yan,
Jing Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1639/1/012102
Subject(s) - wavelet , signal (programming language) , noise reduction , waveform , infrared , artificial intelligence , false alarm , computer science , rolling element bearing , computer vision , wavelet transform , acoustics , pattern recognition (psychology) , engineering , vibration , radar , telecommunications , physics , optics , programming language
General target detection system is based on active detection method, which is usually of poor concealment and bulky. In order to improve the concealment and dexterity of airborne detection, an airborne infrared scanning imaging system for ground vehicles based on passive infrared detection is proposed. In order to solve the problem of false alarm and false recognition when the rotary airborne multi-element infrared detection is used to identify armored vehicles on the ground, based on the wavelet theory, the infrared signal output from the air to ground rotary detection system is de noised by wavelet. The denoising threshold function suitable for the rotating multi element infrared detection system is constructed. Through calculation analysis and experimental verification, the fours-layer coif wavelet basis function is the best. On the premise of not losing the infrared information of the original armored vehicle target, the output waveform is flat when there is no armored vehicle, and the output waveform is smooth when there is armored vehicle target. It provides a theoretical basis for the later realization of armored vehicle type identification.