A Low-Complexity UWB-Radar Signal Processing System for Real-Time Human Respiratory Feature Extraction

• Main Authors: Chiu, Yu-Fang, 邱于芳
• Other Authors: Huang, Yuan-Hao
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/22482580742784822841

碩士 === 國立清華大學 === 電機工程學系 === 102 === This paper presents a real time ultra-wideband (UWB) impulse-radio radar signal processing platform with reduced complexity. This platform is integrated with a radar front-end chip for human respiratory feature extraction and signal compression. Conventional radar detection algorithms only extract respiration rate for medical diagnosis. However, there is more useful information in the radar-detected respiratory signals for medical diagnosis. Thus, this study proposed a four segment linear wave model and an iterative correlation search algorithm to extract more respiratory features, such as inspiration and expiration speed, respiration intensity, and respiration holding ratio between inspiration and expiration. Moreover, since the iterative correlation search algorithms involves high computation cost, this study applies an early termination scheme and down sampling to reduce the complexity with negligible performance degradation. The extracted features are also useful in a remote medical monitoring system because they can be regarded as compressed respiratory signals. One-period human respiratory cycle can be expressed by extracted features instead of lots of samples. Transmission bandwidth or storage capacity can be greatly saved by transmitting or storing the extracted features. The proposed algorithm and architecture was designed and implemented on a real time radar signal processing platform with a FPGA chip. Human respiratory signals from 0.1 to 1 Hz rate are detected and analyzed along with other information in each period.