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Source Localization in Acoustic Sensor Networks via Constrained Least-Squares Optimization Using AOA and GROA Measurements

机译:通过使用AOA和GROA测量的约束最小二乘法优化在声传感器网络中进行源定位

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摘要

A constrained least-squares (CLS) 3D source localization method is presented for acoustic sensor networks with sensor position errors. The proposed approach uses angles of arrivals (AOAs) and gain ratios of arrival (GROAs) measured simultaneously at each node to estimate the source position jointly. Compared to AOA-only localization methods, the GROAs can be used in conjunction with AOA measurements so as to get more accurate results by exploiting the geometrical relationship between these two measurements. Compared to time difference of arrival localization methods, the proposed algorithm does not require accurate time synchronization over different nodes. The theoretical mean-square error matrices of the proposed approach are derived and they are exactly equal to the Cramér–Rao bound for Gaussian noise under the small error condition. Simulations validate the performance of the proposed estimator.
机译:针对具有传感器位置误差的声学传感器网络,提出了一种约束最小二乘(CLS)3D源定位方法。所提出的方法使用在每个节点同时测量的到达角(AOA)和到达增益比(GROAs)共同估算源位置。与仅使用AOA的定位方法相比,GROA可以与AOA测量结合使用,以便通过利用这两个测量之间的几何关系来获得更准确的结果。与到达时间定位方法的时差相比,该算法不需要在不同节点上进行准确的时间同步。推导了该方法的理论均方误差矩阵,它们与小误差条件下的高斯噪声的Cramér-Rao边界完全相等。仿真验证了所提出估计器的性能。

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