Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares
Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares
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DOI：
10.1109/mass.2017.50
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发表时间：
2017-10
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通讯作者：
Alireza Zaeemzadeh;M. Joneidi;Behzad Shahrasbi;Nazanin Rahnavard
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作者：
Alireza Zaeemzadeh;M. Joneidi;Behzad Shahrasbi;Nazanin Rahnavard
本文研究了在有外围传感器存在的情况下的目标定位问题。这个问题在实践中很重要，因为在许多实际应用中，传感器可能会无意或恶意地报告不相关的数据。该问题是制定通过应用鲁棒统计技术的平方距离测量和两种不同的方法来解决这个问题。第一种方法是计算效率高，但是，只有客观的收敛性是理论上保证。另一方面，建立了第二种方法的全序列收敛性。为了享受这两种方法的好处，它们被集成到开发一个混合算法，提供计算效率和理论保证。这些算法被评估为不同的模拟和真实世界的场景。数值结果表明，所提出的方法满足Cr'amer-Rao下限（CRLB）的足够大的数量的测量。当测量的数量是小的，建议的位置估计器不实现CRLB，但它仍然优于现有的几种定位方法。
In this paper, the problem of target localization in the presence of outlying sensors is tackled. This problem is important in practice because in many real-world applications the sensors might report irrelevant data unintentionally or maliciously. The problem is formulated by applying robust statistics techniques on squared range measurements and two different approaches to solve the problem are proposed. The first approach is computationally efficient; however, only the objective convergence is guaranteed theoretically. On the other hand, the whole-sequence convergence of the second approach is established. To enjoy the benefit of both approaches, they are integrated to develop a hybrid algorithm that offers computational efficiency and theoretical guarantees.The algorithms are evaluated for different simulated and real-world scenarios. The numerical results show that the proposed methods meet the Cr'amer-Rao lower bound (CRLB) for a sufficiently large number of measurements. When the number of the measurements is small, the proposed position estimator does not achieve CRLB though it still outperforms several existing localization methods.