Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares

Futa Watanabe

doi:10.1109/access.2021.3050309

Abstract

We propose a novel localization method using angle-of-arrival (AoA) measurements with two-step error variance-weighted least squares (TELS). The first step is to estimate a terminal location provisionally using least squares. The second step is to estimate the terminal location using weighted least squares, with the weights for each anchor and each evaluation-function term, calculated from the error variance based on the first step. The proposed method does not require previous information on the environment while achieving high performance. The simulation results indicate that a root mean square error (RMSE) of the proposed method is superior to that of the existing hybrid received signal strength (RSS)/AoA localization methods. When 11 anchors are deployed inside a cube with edge length 15 m, and the standard deviations of measurements are small, the RMSE of the proposed method reaches about 0.34 m. It is nearly equal to that of Cramer-Rao lower bound (CRLB) on AoA.

Highlights

With the development of location-based services for smartphones and other devices in recent years, localization methods that use wireless sensor networks (WSNs) have been attracting considerable attention [1]–[6]
In the case the standard deviations of measurements are small, the proposed method can achieve performance almost comparable to the Cramer-Rao lower bound (CRLB) on AoA
SIMULATION RESULTS we present the results of numerical simulation to verify the proposed method’s performance

Summary

Introduction

With the development of location-based services for smartphones and other devices in recent years, localization methods that use wireless sensor networks (WSNs) have been attracting considerable attention [1]–[6]. Among the range-based methods, received signal strength (RSS), time of flight (ToF), time of arrival (TOA), and time difference of arrival (TDoA) are the common measurements employed to estimate location [7]–[10]. Among the methods that do not require range information, proximity detection, fingerprint matching, and angle-of-arrival (AoA) measurements are common [11]–[16]. AoA-based localization methods are highly accurate, and various ways have been studied [14]–[16]. Hybrid localization methods that combine estimations from RSS and AoA measurements have been studied for high localization accuracy [1], [2], [17]–[20]. The weights based on the error covariance matrix [weighted linear least squares (WLLS)]

Methods

Results

Discussion

Conclusion