An Accelerated Algorithm for Detecting Micro-Moving Objects of Radar Life Detector of Smart Campus Based on Block Data Analysis | Zendy

Buge Liang | Zendy; Yanghao Jin | Zendy; Degui Yang | Zendy; Dangjun Zhao | Zendy; Yadong Zhang | Zendy

Open Access

An Accelerated Algorithm for Detecting Micro-Moving Objects of Radar Life Detector of Smart Campus Based on Block Data Analysis

Author(s) -

Buge Liang,

Yanghao Jin,

Degui Yang,

Dangjun Zhao,

Yadong Zhang

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.2018.2872967

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

As a non-contact life detecting equipment of smart campus, the ultra-wide band (UWB) radar life detector is widely used in finding victims of post-disaster search and rescue. However, due to the complex detection environment and the weak life characteristics of victims, the detecting time consuming by current detecting methods is long meanwhile the sensitivity is low, as a result, the process of search and rescue is slow and the probability of missed detection is high. To solve this problem, three works have been done in this research: first, a mathematical model of the echo of UWB radar is established; second, a concept, the critical speed of fast and slow objects, is put forward as a reference value of signal processing; and third, a new accelerated algorithm of which utilizes block data analysis to detect micro-moving objects is proposed in this paper. It has been verified both by simulation and experiment that the new algorithm can improve the detecting sensitivity and the speed of detection.

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