Efficient Convex Relaxation Methods for Robust Target Localization by a Sensor Network Using Time Differences of Arrivals
Efficient Convex Relaxation Methods for Robust Target Localization by a Sensor Network Using Time Differences of Arrivals
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DOI：
10.1109/tsp.2009.2016891
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发表时间：
2009-07
影响因子：
5.4
通讯作者：
Kehu Yang;G. Wang;Z. Luo
中科院分区：
文献类型：
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作者：
Kehu Yang;G. Wang;Z. Luo
我们考虑了被动传感器网络的目标定位问题。当未知目标发出声或无线电信号时，可以利用到达时间差(TDOA)信息利用多个传感器对其位置进行定位。在这篇文章中，我们考虑了这个目标定位问题的极大似然公式，并对这个非凸优化问题提供了有效的凸松弛。我们还提出了一种在存在传感器定位误差的情况下的稳健目标定位公式。在有和没有传感器节点定位误差的情况下，分别推导了两个Cramer-Rao界。仿真结果表明，在传感器节点定位误差较大的情况下，凸松弛方法比现有的基于最小二乘的方法具有更高的效率和更好的性能。
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem. We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.