Positioning optimisation based on particle quality prediction in wireless sensor networks

Abstract

The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

“…Considering the real conditions, we accommodate the particle acceleration in the framework. The high-quality particle set is obtained according to the nonlinear replication algorithm [23], and the predicted particle set x i 0:t : i = 1, 2, ..., N is obtained by the PQP algorithm. The weighted centroid drift and the centroid of the predicted particle set are calculated when the centroid velocity solution is applied to the predicted particle set.…”

“…According to [23], applying the PF to the target node may significantly affect the positioning accuracy. Particles in the high-likelihood region have large weights, and those that do not intersect with the prior distribution region have weights that are approximately equal to zero.…”

“…The limitation is that observations at the current time in the reference distribution are not available. To address this problem, the PQP algorithm was proposed in [23], which presents a predictor to obtain a particle swarm of high quality and modify the reference distribution function by calculating nonlinear variations ranging between particles and specify flags.…”

“…We calculate the prior distribution parameters of the PF and obtain the particle aggregation region by using the drift vector of the VOLUME 4, 2016 particle center. To do this, we first use the particle quality prediction (PQP) function [23] to obtain the particles in the highlikelihood region. The high-quality particles participate in the subsequent calculation with high probability, which can increase the number of effective particles and enable avoiding the degradation of particles.…”

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

“…In the literature, PFs, or sequential Monte Carlo recursive methods have been studied over the last years, in order to increase accuracy in location and positioning applications [35]. Different conditions and implementations have been considered, such as NLOS in narrow-band systems [36], positioning optimization in wireless sensor networks [37] or enhancement of positions and orientation estimations [38] . The analysis on positioning bounds [39], the use of techniques such as map re-calibration [40] and [41] or multiple data fusion [42] provide further enhancement in PF -based location.…”