Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

TL;DR

This paper introduces robust algorithms for target localization that effectively handle outlier sensors, combining efficiency and theoretical guarantees, and outperform existing methods especially with limited measurements.

Contribution

It proposes a hybrid robust localization algorithm that integrates two approaches, providing both computational efficiency and convergence guarantees, and demonstrates superior performance in simulations and real data.

Findings

Algorithms meet the Cramér-Rao lower bound with many measurements.

Outperforms existing methods with fewer measurements.

Hybrid approach balances efficiency and theoretical convergence.

Abstract

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.