Design and implementation of sound tracking multi-robot system in wireless sensor networks

Z. Meng
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引用次数: 2

Abstract

A sound target-tracking multiple-robot system is described, including 4-channel microphone array for sound collection, magnetoresistive sensor for declination measurement, wireless senor network (WSN) for exchanging information. It has embedded sound signal enhancement, recognition and location method, and sound tracking strategy based on the digital signal processor (DSP). As the wireless network nodes, three robots comprise the WSN with personal compute (PC) in order to track the three different sound targets in task-oriented collaboration. Improved spectral subtraction method is used for noise reduction. As the feature of audio signal, Mel-frequency cepstral coefficient (MFCC) is extracted. Based on K-nearest neighbor classification method, we match the trained feature template to recognize sound signal type. Improved generalized cross correlation method is utilized to estimate time delay of arrival (TDOA), and then we employ spherical-interpolation for sound location according to the TDOA and the geometrical position of microphone array. A New mapping has been proposed to direct the motor to track sound target flexibly. As the sink node, PC receives and displays the processed result in WSN, and it also has the ultimate power to make decision on the received result in order to improve their accuracy.
无线传感器网络中声音跟踪多机器人系统的设计与实现
描述了一种声音目标跟踪多机器人系统,包括用于声音采集的4通道麦克风阵列、用于赤纬测量的磁阻传感器、用于信息交换的无线传感器网络。它嵌入了声音信号增强、识别和定位方法,以及基于数字信号处理器(DSP)的声音跟踪策略。三个机器人作为无线网络节点,将无线传感器网络与个人计算机(PC)组成,以便在面向任务的协作中跟踪三个不同的声音目标。采用改进的谱减法进行降噪。作为音频信号的特征,提取了Mel-frequency倒频谱系数(MFCC)。基于k近邻分类方法,对训练好的特征模板进行匹配,识别声音信号类型。首先利用改进的广义相互关联法估计到达时延(TDOA),然后根据到达时延和传声器阵列的几何位置,采用球面插值法进行定位。提出了一种引导电机灵活跟踪声目标的新型映射方法。PC作为汇聚节点,在WSN中接收并显示处理后的结果,并对接收到的结果进行最终决策,以提高其准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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