3-D RSS-AOA Based Target Localization Method in Wireless Sensor Networks Using Convex Relaxation

Shengming Chang,You Zheng,Jun Li,Jianyu Bao,Peng An

doi:10.1109/access.2020.3000793

Shengming Chang, You Zheng + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3000793

Copy DOI

Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 36	License type: CC BY 4.0

Affiliation: Ningbo University of Technology

Abstract

This paper addresses a target localization problem in 3-D wireless sensor networks using a hybrid system that fuses received signal strength and angle of arrival measurements. First, we formulate the received signal strength and angle of arrival measurement models as the pseudo-linear equations. Then, the bias is derived from the 3-D angle of arrival measurements that take the measurement noise into account to improve the localization performance. Furthermore, a non-convex estimator is derived based on the Least Squares criterion. Finally, semi-definite relaxation and second-order cone relaxation are applied to transform the derived non-convex estimator into a convex one. We propose a semi-definite relaxation and second-order cone relaxation-based estimator which yields the best performance under a large measurement noise or a small measurement noise. The generalization of the proposed method for known transmit power can also be applied to the case when transmit power is not know. Theoretical analysis and computer simulations corroborate the superior performance of the proposed localization methods over the existing ones.

Highlights

Wireless sensor networks (WSNs) have been used in a wide range of applications, such as target tracking, navigation, emergency services, friends finding and intelligent transportation [1], [2]
Chang et al.: 3-D received signal strength (RSS)-angle of arrival (AOA) Based Target Localization Method in WSNs localization accuracy of this method highly depends on the selection of initial point, which means that if the initial solution is not close to the global solution, the final solution will lead to a poor localization accuracy
To avoid this disadvantage of Maximum Likelihood (ML) estimator, convex optimization method is proposed to solve the RSS-AOA based localization problem, where the semi-definite programming (SDP) method and the second order cone programming (SOCP) method give the best performance with a higher computational complexity [27], [28]

Summary

INTRODUCTION

Wireless sensor networks (WSNs) have been used in a wide range of applications, such as target tracking, navigation, emergency services, friends finding and intelligent transportation [1], [2]. Chang et al.: 3-D RSS-AOA Based Target Localization Method in WSNs localization accuracy of this method highly depends on the selection of initial point, which means that if the initial solution is not close to the global solution, the final solution will lead to a poor localization accuracy To avoid this disadvantage of ML estimator, convex optimization method is proposed to solve the RSS-AOA based localization problem, where the semi-definite programming (SDP) method and the second order cone programming (SOCP) method give the best performance with a higher computational complexity [27], [28]. We consider the localization problem in 3D wireless sensor networks based on RSS and AOA measurements. The main contributions of this paper are summarized as follows: 1) The proposed methods begin with the RSS and 3D AOA measurements and derive the pseudo-linear equations. 2) The RSS and AOA models are transformed into unified form, a novel non-convex objective function for solving the target location is derived based on LS criterion, which tightly approximates the ML one for small noise.

SYSTEM MODEL

COMPLEXITY ANALYSIS

SIMULATION RESULTS

CONCLUSION