Two Efficient and Easy-to-Use NLOS Mitigation Solutions to Indoor 3-D AOA-Based Localization

TL;DR

This paper introduces two practical methods for improving 3-D indoor localization accuracy in mixed LOS/NLOS environments, using error mitigation techniques based on least squares and robust optimization, validated through simulations and experiments.

Contribution

It presents two novel error mitigation solutions for 3-D AOA localization in indoor environments with LOS and NLOS conditions, enhancing accuracy and robustness.

Findings

Both methods achieve high localization accuracy in simulations.

Experimental results confirm the effectiveness of the proposed solutions.

The second method provides robust performance under NLOS errors.

Abstract

This paper proposes two efficient and easy-to-use error mitigation solutions to the problem of three-dimensional (3-D) angle-of-arrival (AOA) source localization in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) indoor environments. A weighted linear least squares estimator is derived first for the LOS AOA components in terms of the direction vectors of arrival, albeit in a sub-optimal manner. Next, data selection exploiting the sum of squared residuals is carried out to discard the error-prone NLOS connections. In so doing, the first approach is constituted and more accurate closed-form location estimates can be obtained. The second method applies a simulated annealing stochastic framework to realize the robust $\ell_1$-minimization criterion, which therefore falls into the methodology of statistical robustification. Computer simulations and ultrasonic onsite experiments are conducted to evaluate the performance of the two proposed methods, demonstrating their outstanding positioning results in the respective scenarios.