Direction Finding for Transient Acoustic Source Based on Biased TDOA Measurement
Direction Finding for Transient Acoustic Source Based on Biased TDOA Measurement
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基于偏置TDOA测量的瞬态声源测向
DOI：
10.1109/tim.2016.2583224
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发表时间：
2016-07
影响因子：
5.6
通讯作者：
Lu Songsheng
中科院分区：
文献类型：
--
作者：
Cui Xunxue;Yu Kegen;Lu Songsheng
由多个空间分布的传感器组成的声定位系统可以通过测量瞬态声信号的相对到达时间差（TDOA）来估计源方位。然而，传感器测量可能包含不可忽略的一致的系统偏差，导致显着的方向发现估计误差。本文提出了一种有效的期望最大化（EM）算法，用于精确估计来自远场源的信号的到达方向（DOA）存在偏差的TDOA测量。未知的偏差被视为隐变量，和非线性最小二乘估计器开发联合估计的无参考模式和参考模式的偏差和DOA参数。研究了TDOA误差分布，并采用高斯分布、拉普拉斯分布、高斯-拉普拉斯分布和广义正态分布（GND）四种不同的分布来拟合实验数据。结果表明，GND分布是最适合于模拟有偏时差误差的累积分布函数，而拉普拉斯分布在精度和复杂度之间提供了一个很好的折衷。仿真和现场实验结果表明，所提出的EM估计可以大大优于现有的算法。
A sound localization system consisting of a number of spatially distributed sensors can be employed to estimate the source bearing by measuring the relative time-difference-of-arrival (TDOA) of the transient acoustic signal. However, sensor measurements may contain nonnegligible consistent systematic biases, resulting in significant direction finding estimation error. In this paper, an efficient expectation-maximization (EM) algorithm is proposed for accurately estimating the direction-of-arrival (DOA) of the signal from a far field source in the presence of biased TDOA measurements. The unknown biases are treated as hidden variables, and nonlinear least square estimators are developed to jointly estimate the biases and DOA parameters for both the reference-free mode and the reference mode. TDOA error distribution is investigated and four different distributions [Gaussian distribution, Laplace distribution, Gaussian-Laplace distribution, and generalized normal distribution (GND)] are employed to fit the experimental data. It is observed that GND is the best for modeling the biased TDOA errors in terms of cumulative distribution function fitting root mean square error, while Laplace distribution offers a good tradeoff between the accuracy and complexity. Both the simulation and field experimental results demonstrate that the proposed EM-based estimators can considerably outperform the existing algorithms.
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影响因子：
1.9
作者：
Yiyin Wang;G. Leus
通讯作者：
Yiyin Wang;G. Leus
影响因子：
7.9
作者：
Ouyang, Robin Wentao;Wong, Albert Kai-Sun;Chiang, Mung
通讯作者：
Chiang, Mung
影响因子：
1.9
作者：
D. Lindgren;O. Wilsson;F. Gustafsson;H. Habberstad
通讯作者：
D. Lindgren;O. Wilsson;F. Gustafsson;H. Habberstad
DOI：
10.1109/mnet.2018.8270623
发表时间：
2018-06
期刊：
IEEE Netw.
影响因子：
--
作者：
Xiaohua Tian
通讯作者：
Xiaohua Tian
DOI：
10.1109/tim.2010.2047035
发表时间：
2011-03-01
影响因子：
5.6
作者：
Meng, Wei;Xiao, Wendong;Xie, Lihua
通讯作者：
Xie, Lihua