Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

Abstract

Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from nonline-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.

Improved Nlos Error Mitigation Based on LTS Algorithm

“…The number of BSs under NLOS was randomly changed in each simulation loop to simulate the dynamic environment. A comparison of the cumulative distribution functions (CDF) of LSE [16], LMS [15], LTS [18] and proposed improved LTS methods is shown in Figure 1. If the number of NLOS BSs is much larger than the number of LOS BSs, the performance of the LMS algorithm decreases drastically because small number of BSs are used in the second step of position calculation, i.e., k in Eq.…”

“…And it has better than 25 cm localisation error all the time. The statistical parameter of the same simulation environments, such as mean, standard deviation and root mean square errors estimations are compared for LSE [16], LMS [15], LTS [18] and proposed improved LTS algorithms in Figure 2. The new improved LTS shows the lowest error statistics, such as mean excess delay, standard deviation (std) and root mean squares (rms) among all methods, and it is more stable with the lowest std.…”

“…There are also many less complex methods which are available. To overcome this limitation robust estimator, i.e., the least median of squares (LMS), was proposed by [14,15] for NLOS error mitigation.…”

“…Authors in [14,15] proposed a Least Median of Squares (LMS) algorithm in order to exclude NLOS BSs from the set of BSs adopted for the target position calculation. LMS performs very well in mixed environments and where more LOS BSs present.…”

Nonlinear programming-based ranging optimization for three-dimensional indoor time of arrival localization

“…The other approaches of NLOS mitigation are not based on NLOS identification. Such approaches include Kalman filter, 7 redundant range estimates, 8 environmental geometrical constrains, 9 and so on. The detailed overviews of NLOS identification and mitigation are shown in Khodjaev et al 10 The localization algorithm is another important factor that affects the positioning accuracy.…”