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采用滤波器组，将盲分离与声源定位结合在一起。滤波器组中分别进行子带处理，并且在每个子带中进行声源定位，最后综合所有子带估计的方向得到多个声源的方位信息，同时将声源信息作为ICA分析的约束条件，可以在色噪声及多得到更好的盲分离效果。其优势在于用滤波器组进行多次的估计声源方向，再加以合适的权值均衡后即可得到更优的估计。-The use of filters will be blind source separation and sound localization together. Filter
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结合马尔可夫过程，提出一种概率论的声源定位算法，并给出了基于DSP的机器人实现。其中声源定位部分采用三个麦克风呈三角形放置，为减小由于噪声等引起的TDOA估计误差，采用马尔可夫过程计算时延，这样计算的时延会更可靠。该方案中的声源定位也属于一维定位，即只需知道声源的方向角-Combination of Markov process, a probability theory of sound source location algorithm, and give the robot based o
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此文档为IEEE TRANSACTIONS ON SIGNAL PROCESSING信号源被动定位与跟踪领域经典文章“Alleviating Sensor Position Error in Source Localization Using Calibration Emitters at Inaccurate Locations”的PDF版原文，相关程序另外上传，进行相同领域研究的童鞋可以参考学习！-This document is for the IEEE TRANSACTIONS ON S
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IEEE TRANSACTIONS ON SIGNAL PROCESSING在定位与跟踪领域的经典文献“Source Localization Using TDOA and FDOA Measurements in the Presence of Receiver Location Errors: Analysis and Solution”的PDF文档，此文档的MATLAB程序同时上传，进行信号源被动定位与跟踪研究的同学可下载使用！-IEEE TRANSACTIONS ON SIGNAL PR
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time differance of arrival is localization technique
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We consider the source localization problem using
time-difference-of-arrival (TDOA) measurements in sensor networks.
The maximum likelihood (ML) estimation of the source
location can be cast as a nonlinear/nonconvex optimization
problem,
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In this paper, we propose a robust and low-complexity
acoustic source localization technique based on time differences of
arrival (TDOA), which addresses the scenario of distributed sensor
networks in 3D environments. Network nodes are assumed
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The paper presents a smartphone-based shooter localization
system. As muzzle blasts are difficult to detect at longer distances
and consequently present higher false detection rates,
the system relies on shockwaves only.
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In this paper, we investigate the problem of
reliable detection and localization of active sound source
using a new fusion approach of the vision and the
acoustic data that enables reducing errors of acoustic
localization caused mainly by syn
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