【摘要】：回聲定位是人類的一項(xiàng)基本的認(rèn)知技能。對(duì)回聲定位的研究具有重要的研究意義和應(yīng)用價(jià)值。通過對(duì)人類回聲定位機(jī)理的研究,可以提煉出一種新的面向環(huán)境感知和自主導(dǎo)航的信號(hào)處理方法。一方面,可為制造盲用的便攜式回聲定位裝置提供一定的理論依據(jù)和技術(shù)支撐;另一方面,將這種認(rèn)知方法引入雷達(dá)、聲吶等傳感設(shè)備中,可有望為其提供一種全新的思路和信號(hào)處理方法。本文在對(duì)面向二維形狀目標(biāo)識(shí)別的人類回聲定位行為實(shí)驗(yàn)進(jìn)行深入研究和分析的基礎(chǔ)上,搭建了一套人類回聲定位實(shí)驗(yàn)系統(tǒng),利用該系統(tǒng)模擬了人類回聲定位行為實(shí)驗(yàn)中的兩個(gè)場(chǎng)景:“保持靜止”場(chǎng)景和“自由運(yùn)動(dòng)”場(chǎng)景,分別在兩個(gè)場(chǎng)景下進(jìn)行了信號(hào)采集工作,研究了相應(yīng)的聲音信號(hào)特征提取方法,并分別進(jìn)行了兩個(gè)場(chǎng)景下的二維形狀目標(biāo)識(shí)別方法研究。本文的主要工作可總結(jié)如下:1)對(duì)面向二維形狀目標(biāo)識(shí)別的人類回聲定位行為實(shí)驗(yàn)的結(jié)果進(jìn)行了研究和分析,搭建了與之對(duì)應(yīng)的面向人類回聲定位的實(shí)驗(yàn)系統(tǒng),并進(jìn)行了相應(yīng)的測(cè)試;2)研究了聲音信號(hào)特征提取方法,提取出了總能量(TS)、平均頻率(AF)、低頻能量(LFS)、中頻能量(IFS)、高頻能量(HFS)、亮度(TB)、三色值(T1、T2、T3)、不規(guī)則度(IRG)等10個(gè)特征;3)模擬了“保持靜止”場(chǎng)景下的人類回聲定位實(shí)驗(yàn),分別從采集的原始聲音信號(hào)和特征提取兩個(gè)方面進(jìn)行了分析,結(jié)果表明:很難通過單一位置的聲音信號(hào)區(qū)分不同形狀目標(biāo),該結(jié)果與行為實(shí)驗(yàn)的結(jié)果相符;4)模擬了“自由運(yùn)動(dòng)”場(chǎng)景下的人類回聲定位實(shí)驗(yàn),分別對(duì)40cm復(fù)合信號(hào)、80cm復(fù)合信號(hào)以及80cm回聲信號(hào)進(jìn)行了特征提取和可視化分析,結(jié)果表明:根據(jù)這三種聲音信號(hào)的總能量特征或者平均頻率特征均可有效區(qū)分不同形狀的二維目標(biāo),該結(jié)果與行為實(shí)驗(yàn)的結(jié)果相一致。
[Abstract]:Echolocation is a basic cognitive skill. The study of echolocation has important significance and application value. By studying the mechanism of human echolocation, a new signal processing method for environment perception and autonomous navigation can be extracted. On the one hand, it can provide some theoretical basis and technical support for the manufacture of portable echolocation devices for blind use; on the other hand, it can be introduced into radar, sonar and other sensing devices. It is expected to provide a new way of thinking and signal processing. Based on the deep research and analysis of human echolocation behavior experiment for two-dimensional shape target recognition, a set of human echolocation experiment system is built in this paper. The system is used to simulate two scenarios in the human echolocation behavior experiment: "keep still" scene and "free motion" scene. The corresponding feature extraction method of sound signal is studied, and the two dimensional shape target recognition method under two scenes is studied respectively. The main work of this paper can be summarized as follows: (1) the experimental results of human echolocation behavior for two-dimensional shape target recognition are studied and analyzed, and a corresponding experimental system for human echolocation is set up. A corresponding test is carried out to study the method of feature extraction of sound signal. Total energy (TS), average frequency (AF), low frequency energy (LFS), intermediate frequency energy (IFS), high frequency energy (HFS), brightness (TB), tricolor (T _ 1 T _ 2 T _ 3), irregular degree (IRG) and so on 10 characteristics are extracted to simulate human echolocation experiments under "keeping still" scene. The results show that it is difficult to distinguish different shape objects by single position sound signal. The results are in agreement with the results of behavioral experiments. (4) the experiments of human echolocation in the "free motion" scene are simulated. The feature extraction and visual analysis of the 40cm composite signals and the 80cm echo signals are carried out, respectively. The results show that the two dimensional targets with different shapes can be effectively distinguished according to the total energy characteristics or the average frequency characteristics of the three kinds of sound signals. The results are in agreement with the experimental results.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN912.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 姚旭;王曉丹;張玉璽;權(quán)文;;特征選擇方法綜述[J];控制與決策;2012年02期

2 毛勇;周曉波;夏錚;尹征;孫優(yōu)賢;;特征選擇算法研究綜述[J];模式識(shí)別與人工智能;2007年02期

相關(guān)博士學(xué)位論文 前1條

1 劉燕;高分辨率及新模式SAR成像算法研究[D];西安電子科技大學(xué);2012年

相關(guān)碩士學(xué)位論文 前4條

1 舒占軍;基于高分辨距離像的雷達(dá)目標(biāo)識(shí)別算法研究[D];電子科技大學(xué);2016年

2 王影;基于最近鄰子空間搜索的文本分類算法[D];北京工業(yè)大學(xué);2014年

3 仲媛;最近鄰分類的若干改進(jìn)算法研究[D];南京理工大學(xué);2012年

4 姜百寧;機(jī)器學(xué)習(xí)中的特征選擇算法研究[D];中國(guó)海洋大學(xué);2009年

，

本文編號(hào)：2159458