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Passive Source Localization Using Time Differences of Arrival and Gain Ratios of Arrival

机译:利用到达时间差和到达增益比进行无源定位

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A source signal will be subject to different amounts of time-delay as well as attenuation when it reaches a number of spatially separated sensors. Both time-delay and attenuation are dependent on the distance between the source and the receivers. This paper performs a fundamental investigation of whether the gain ratios of arrival (GROAs), defined here as the ratio of the received signal amplitudes at the referenced sensor to the other sensors, can be utilized in conjunction with the time differences of arrival (TDOAs) to improve the source localization accuracy. We begin with a Gaussian random signal model and derive the Cramer-Rao lower bound (CRLB) of a source location estimate based on both GROAs and TDOAs. Our conclusion is that the improvement from GROAs increases when the factor c/omegao increases, where c is the signal propagation speed and omegao is the signal bandwidth. The paper proceeds to develop an algebraic closed-form solution for the source location using GROAs and TDOAs. The algebraic solution is proved theoretically to reach the CRLB accuracy under the Gaussian data model. Numerical simulations are included to support and corroborate the theoretical developments.
机译:当源信号到达多个在空间上分离的传感器时,源信号将经受不同的时间延迟和衰减。延迟和衰减都取决于信号源和接收器之间的距离。本文对是否可以结合到达时差(TDOA)来利用到达增益比(GROA)(在此定义为参考传感器与其他传感器的接收信号幅度之比)进行了基础研究。提高源定位精度。我们从高斯随机信号模型开始,并基于GROA和TDOA推导源位置估计的Cramer-Rao下界(CRLB)。我们的结论是,当因子c / omegao增加时,来自GROA的改进会增加,其中c是信号传播速度,而omegao是信号带宽。本文着手使用GROA和TDOA为源位置开发代数封闭形式的解决方案。理论上证明了代数解在高斯数据模型下达到CRLB精度。包括数值模拟以支持和证实理论发展。

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