本文選題：同步擠壓小波　切入點：雷達回波　出處：《西安交通大學學報》2017年12期

【摘要】：針對人體運動的雷達回波信號特征復雜、不同運動姿態(tài)微多普勒頻率差異小、難以區(qū)分精細特征的問題,提出了一種采用參數(shù)可調的同步擠壓小波變換(SSTAP)的人體運動姿態(tài)分析方法。首先根據(jù)實測人體運動數(shù)據(jù)構建人體運動模型及其雷達回波模型;然后利用SSTAP方法對人體運動模型雷達回波信號進行分解,獲得人體各主要部位的時頻特征;再通過調整同步擠壓小波變換的2個參數(shù)獲得人體整體回波信號的具有最佳時頻分辨率的時頻特征,進一步與各部位的人體時頻特征比較獲得了人體運動姿態(tài)的信息。實驗結果表明,相比廣義S變換(GST)、小波變換(WT)等時頻分析方法,基于參數(shù)可調的同步擠壓小波的人體微多普勒分析結果更加清晰精細,更能反映人體微運動的特征,其微多普勒頻率的分辨率比GST、WT分別提高了17%和14%。
[Abstract]:The characteristics of radar echo signal of human body motion are complex, the difference of micro-Doppler frequency of different motion attitude is small, so it is difficult to distinguish fine features.A method of human motion attitude analysis using synchronous extrusion wavelet transform (SSTAP) with adjustable parameters is proposed.Firstly, the human motion model and radar echo model are constructed according to the measured human motion data, then the radar echo signal of human motion model is decomposed by SSTAP method, and the time-frequency characteristics of the main parts of human body are obtained.Then, by adjusting the two parameters of synchronous extrusion wavelet transform, the time-frequency features of the human whole echo signal with the best time-frequency resolution are obtained, and the human motion attitude information is further obtained compared with the human body time-frequency characteristics of various parts.The experimental results show that compared with generalized S-transform (GSTT), wavelet transform (WTT) and other time-frequency analysis methods, the results of human micro-Doppler analysis based on synchronous extrusion wavelet with adjustable parameters are clearer and finer, and can reflect the characteristics of human micro-motion more clearly.The resolution of microDoppler frequency is 17% and 14% higher than that of GSTT WT, respectively.
【作者單位】： 西安理工大學自動化與信息工程學院;后勤工程學院國防建筑規(guī)劃與環(huán)境工程系;
【基金】：國家自然科學基金重大研究計劃資助項目(41390454)
【分類號】：TN957.51

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本文編號：1709461