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Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals

机译:使用到达时间差的Ad-Hoc数组自定位

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摘要

We investigate the problem of sensor and source joint localization using time-difference of arrivals (TDOAs) of an ad-hoc array. A major challenge is that the TDOAs contain unknown time offsets between asynchronous sensors. To address this problem, we propose a low-rank approximation method that does not need any prior knowledge of sensor and source locations or timing information. At first, we construct a pseudo time of arrival (TOA) matrix by introducing two sets of unknown timing parameters (source onset times and device capture times) into the current TDOA matrix. Then we propose a Gauss-Newton low-rank approximation algorithm to jointly identify the two sets of unknown timing parameters, exploiting the low-rank property embedded in the pseudo TOA matrix. We derive the boundaries of the timing parameters to reduce the initialization space and employ a multi-initialization scheme. Finally, we use the estimated timing parameters to correct the pseudo TOA matrix, which is further applied to sensor and source localization. Experimental results show that the proposed approach outperforms state-of-the-art algorithms.
机译:我们使用临时阵列的到达时间差(TDOA),调查传感器和源关节的定位问题。一个主要的挑战是TDOA包含异步传感器之间的未知时间偏移。为了解决这个问题,我们提出了一种低秩逼近方法,该方法不需要对传感器和源位置或时序信息有任何先验知识。首先,我们通过在当前的TDOA矩阵中引入两组未知的时序参数(源启动时间和设备捕获时间)来构建伪到达时间(TOA)矩阵。然后,我们提出了一种Gauss-Newton低秩逼近算法,以利用伪TOA矩阵中嵌入的低秩属性来共同识别两组未知时序参数。我们推导时序参数的边界以减少初始化空间,并采用多重初始化方案。最后,我们使用估计的时序参数来校正伪TOA矩阵,该矩阵将进一步应用于传感器和源定位。实验结果表明,该方法优于最新的算法。

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