Cube-Based Multitarget 3D Localization Using Bayesian Learning-Based Turbo Decoding in Wireless Sensor Networks

Abstract

Wireless techniques have progressed beyond two-dimensional (2D) applications; in particular, three-dimensional (3D) localization in wireless sensor networks has attracted increasing attention. While many research works in 3D localization focused on the accuracy enhancement of schemes based on the sensors’ measurements, limited research works addressed the design of 3D localization schemes considering the narrowband signal restriction of sensors. Against this background, we propose a cube-based multitarget three-dimensional (3D) localization solution by exploiting sensors’ time-difference-of-arrival (TDOA) measurements. In particular, unlike the traditional TDOA based scheme, our scheme works in an asynchronous network. Our contributions are twofold. First, the distributed TDOA–based sensor arrays placed with a predefined method create a cube-based location system in 3D space. Second, we propose a turbo expectation propagation (EP)-based decoding algorithm (TED). EP computation is an efficient tool in Bayesian machine learning. With the assistance of the iterative sensor reliability correction (ISRC), we propose an improved algorithm referred to as ISRC-TED. Specifically, the ISRC-TED algorithm outperforms the TED algorithm by utilizing the decisions of the sensor array at every iteration to improve the decoding accuracy further. A reduced-complexity tree search–based decoding strategy for TED and ISRC-TED is also proposed. In simulations, the proposed TED and ISRC-TED algorithms were highly effective, even when the partial sensor network was in power-saving mode. For example, the proposed ISRC-TED algorithm had almost no localization performance loss when 10% of the sensors were in sleep mode.