Iterative Regression Based Hybrid Localization for Wireless Sensor Networks

Abstract

Among various localization methods, a localization method that uses a radio frequency signal-based wireless sensor network has been widely applied due to its robustness against noise factors and few limits on installation location. In this paper, we focus on an iterative localization scheme for a mobile with a limited number of time difference of arrival (TDOA) and angle of arrival (AOA) data measured from base stations. To acquire the optimal location of a mobile, we propose a recursive solution for localization using an iteratively reweighted-recursive least squares (IR-RLS) algorithm. The proposed IR-RLS scheme can obtain the optimal solution with a fast computational speed when additional TDOA and/or AOA data is measured from base stations. Moreover, while the number of measured TDOA/AOA data was limited, the proposed IR-RLS scheme could obtain the precise location of a mobile. The performance of the proposed IR-RLS method is confirmed through some simulation results.

Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection

“…In literature, various approaches based on time difference of arrival (TDoA) exist that could be employed to determine an initial search point, e. g., [8]- [11]. However, [8]- [10] require the definition of a reference locator, which has the drawback that the reference itself could exhibit an outlier measurement. On the other hand, [11] avoids the definition of a reference locator, but can not detect outliers and does not provide the possibility of assigning weights to the measurements.…”

A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location

“…The current satellite interference source localization methods mainly use the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) measured by the signals received from the main satellite and the adjacent satellites to determine the location. The localization methods can be classified into analytical methods [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ], optimized solutions [ 15 , 16 , 17 , 18 , 19 , 20 ], and intelligent algorithms [ 21 , 22 , 23 ] in recent years. The analytical methods derive the closed-form solution of the nonlinear localization equation set based on mathematical approximation and the intermediate variable.…”