Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects
碩士 === 國立中正大學 === 資訊工程研究所 === 105 === The recognition of moving targets is very helpful for social security or smart living products. Most research achieve the goal of objects tracking and identification by using a camera to collect image data for feature extraction and analysis. In this work, we us...
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ndltd-TW-105CCU003920702019-05-15T23:32:17Z http://ndltd.ncl.edu.tw/handle/ev75sz Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects 即時辨識移動物體之連續波雷達信號分析系統 WAN, PO-FU 萬勃甫 碩士 國立中正大學 資訊工程研究所 105 The recognition of moving targets is very helpful for social security or smart living products. Most research achieve the goal of objects tracking and identification by using a camera to collect image data for feature extraction and analysis. In this work, we use a continuous wave radar, and calculate the direction and speed of an moving object from its reflected radar wave according to the Doppler effect. Since different objects have their own motion behavioral patterns, we can perform classification or recognition tasks by analyzing its periodicity with time frequency analysis and extracting meaningful features. Our goal is to differentiate human and non-human objects like dogs, cars, and blank background. After collecting the human and non-human gait data through a series of experiments, we proposed four combinations of features, and used K-nearest neighbor and Support vector machine classifiers to check if Fisher linear discriminant analysis is required as a preprocessing. The recognition rate under different feature combinations and classifiers were compared. We also establish a simple user interface to facilitate the real-time moving target recognition for future applications. LIU,WEI-MIN 劉偉名 2017 學位論文 ; thesis 48 zh-TW |
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碩士 === 國立中正大學 === 資訊工程研究所 === 105 === The recognition of moving targets is very helpful for social security or smart living products. Most research achieve the goal of objects tracking and identification by using a camera to collect image data for feature extraction and analysis.
In this work, we use a continuous wave radar, and calculate the direction and speed of an moving object from its reflected radar wave according to the Doppler effect. Since different objects have their own motion behavioral patterns, we can perform classification or recognition tasks by analyzing its periodicity with time frequency analysis and extracting meaningful features. Our goal is to differentiate human and non-human objects like dogs, cars, and blank background.
After collecting the human and non-human gait data through a series of experiments, we proposed four combinations of features, and used K-nearest neighbor and Support vector machine classifiers to check if Fisher linear discriminant analysis is required as a preprocessing. The recognition rate under different feature combinations and classifiers were compared. We also establish a simple user interface to facilitate the real-time moving target recognition for future applications.
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LIU,WEI-MIN |
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LIU,WEI-MIN WAN, PO-FU 萬勃甫 |
author |
WAN, PO-FU 萬勃甫 |
spellingShingle |
WAN, PO-FU 萬勃甫 Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects |
author_sort |
WAN, PO-FU |
title |
Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects |
title_short |
Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects |
title_full |
Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects |
title_fullStr |
Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects |
title_full_unstemmed |
Continuous Wave Radar Based Signal Analysis System For Real-time Recognition Of Moving Objects |
title_sort |
continuous wave radar based signal analysis system for real-time recognition of moving objects |
publishDate |
2017 |
url |
http://ndltd.ncl.edu.tw/handle/ev75sz |
work_keys_str_mv |
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1719148558914945024 |