Cooperative localization in mobile networks using nonparametric variants of belief propagation

Abstract

Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSN), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it offline. Therefore, in this article, we propose more flexible and efficient variants of NBP for cooperative localization in mobile networks. In particular, we provide: i) an optional 1-lag smoothing done almost in real-time, ii) a novel low-cost communication protocol based on package approximation and censoring, iii) higher robustness of the standard mixture importance sampling (MIS) technique, and iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.

Cooperative Uas Localization Using Lowcost Sensors

“…These methods are called Covariance Intersection and Split Covariance Intersection methods (Carrillo-Arce et al 2013, Wanasinghe et al 2014, Li and Nashashibi 2013. A third category of algorithms exist which are inherently distributed in nature and come under the category of belief propagation methods (Savic and Zazo 2013, Chen et al 2013, Wan et al 2015. However in case of loopy networks, belief propagation methods may not result in exact inference (Savic et al 2010) or may not even converge to the solution at all.…”

“…have been used in the distributed estimation architecture. The work for distributed cooperative localization using ground based robots seems to be very extensive (Shi et al 2010, Carrillo-Arce et al 2013, Wanasinghe et al 2014, Savic and Zazo 2013, Nerukar et al 2009, Madhavan et al 2002, Pillonetto and Carpin 2007,Bailey et al 2011) but the work for airborne platforms seems to be somewhat limited (Indelman et al 2012, Qu et al 2010, Qu and Zhang 2011, Melnyk et al 2012, Chen et al 2013, Wan et al 2015. Some of the researchers (Indelman et al 2012, Melnyk et al 2012) have attempted to use vision based sensors for cooperative localization.…”

“…Majority of these researchers (Roumeliotis and Bekey 2002, Bailey et al 2011, Rashidi and Mohammadloo 2011 have solved the problem for two dimensional spaces and while neglecting or not using the observations of inertial measurement unit (IMU). A number of estimation algorithms including UKF (Shi et al 2010), Covariance Intersection (Carrillo-Arce et al 2013), Split Covariance Intersection (Wanasinghe et al 2014, Li andNashashibi 2013), Belief propagation (Savic and Zazo 2013), MAP (Nerukar et al 2009), Iterated Kalman filter (Pillonetto and Carpin 2007) etc. have been used in the distributed estimation architecture.…”

A simple iterative positioning algorithm for client node localization in WLANs

“…Alternately, if the unknown node does not cooperate in the localization process, the procedure is executed by the set of reference nodes based on opportunistic measurements collected when the unknown node transmits. We do not include in our analysis the class of Bayesian localization algorithms [11][12][13][14] as those assume the cooperation among all nodes. In our scenario, we consider that the unknown node may be non-cooperative.…”

Simultaneous localization and tracking via real-time nonparametric belief propagation

“…Since this is in general intractable, we use a proposal distribution, the sum of Gaussian mixtures with reference particles (RPs), and then reweight the particles. This approach, called mixture importance sampling with RPs (MIS-RP) [16], is extension of standard MIS [10]. We first create a collection of ( G s,−a t + 1 + δRP)Np weighted particles by taking all particles from each incoming message excluding anchors (i.e., X (j) t−1→t and X (j) (n,t)→t , ∀n ∈ G s,−a t , j = 1, 2...Np; where G s,−a t is the set of non-anchors which can detect the target at time t) and adding a small number of uniformly distributed particles (RPs) over whole deployment area.…”