Convex Relaxation Approaches to Robust RSS-TOA Based Source Localization in NLOS Environments

摘要

For handling unreliable datasets contaminated by the nonline-of-sight (NLOS) bias errors, this correspondence statistically robustifies the traditional least squares type hybrid received signal strength and time-of-arrival location estimator using the l(1) and Huber loss functions. The two robust formulations are then tackled via different convex relaxation approaches. Numerical results are presented to make fair comparisons with the commonly adopted balancing parameter based scheme, demonstrating the positioning accuracy superiority of the proposed methods in various NLOS environments.

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