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基于时延估计的移动机器人声源定位方法分析-控制科学与工程专业论文.docxVIP

基于时延估计的移动机器人声源定位方法分析-控制科学与工程专业论文.docx

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基于时延估计的移动机器人声源定位方法研究 基于时延估计的移动机器人声源定位方法研究 河北工业大学硕士学位论文 河北工业大学硕士学位论文 PAGE PAGE iv STUDY ON ACOUSTIC LOCALIZATION METHOD BASED ON THE TIME DELAY ESTIMATION OF MOBILE ROBOT ABSTRACT The research and development of sound source localization system is a research focus of industrial control and signal processing. One of the main contents of the study is the location method. In this paper, microphone array is used to study the sound source localization system. It is discussed that the necessity and the main difficulties of sound source localization based on the microphone array. And it introduces several common methods of sound source localization, as well sums up and compares their advantages and disadvantages. This paper mainly studied the method of time delay estimation, including the time delay estimation method of generalized cross-correlation and adaptive time delay estimation method. Analyses of performance of various methods are presented in principle. Also it shows applicable occasions, merits and demerits of various algorithms. A method of generalized cross-correlation time delay estimation based on neural network filtering is proposed on this basis.This paper discusses the basic theory of artificial neural network and its superiority on detail, this method has better anti-noise and adaptive performance than that before. Moreover, we summarized several common localization method based on time delay estimation and those occasions of application, which including spatial mathematic geometric method, ball interpolation method, linear interpolation method, and search positioning method based on the Tabu. Finally, a practical 3D acoustic source localization method is presented. We do experiment and analyze its performance.The experimental data shows that the positioning system is real-time and feasible, positioning accuracy is basicly satisfactory. . KEY WORDS: microphone array,sound source localization,time delay estimation, generalized cross-correlation, Neural Ne

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