Analysis of Visible Light Communication using Integrated Avalanche Photodiode
Volume-4 | Issue-2
A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications
Volume-3 | Issue-3
TOWARDS GHZ METALLIC ACCESS NETWORKS
Volume-1 | Issue-1
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Process Control Ladder Logic Trouble Shooting Techniques Fundamentals
Volume-1 | Issue-4
TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
COMPUTATIONAL OFFLOADING FOR PERFORMANCE IMPROVEMENT AND ENERGY SAVING IN MOBILE DEVICES
Volume-1 | Issue-4
ANALYSIS OF ROUTING PROTOCOLS IN FLYING WIRELESS NETWORKS
Volume-1 | Issue-3
Dual Edge-Fed Left Hand and Right Hand Circularly Polarized Rectangular Micro-Strip Patch Antenna for Wireless Communication Applications
Volume-2 | Issue-3
Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection
Volume-3 | Issue-2
TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application
Volume-3 | Issue-3
Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles
Volume-3 | Issue-3
Cyber-attack and Measuring its Risk
Volume-3 | Issue-4
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Analysis of Solar Power Generation Performance Improvement Techniques
Volume-4 | Issue-3
Pollination Inspired Clustering Model for Wireless Sensor Network Optimization
Volume-3 | Issue-3
Design of Low Power Cam Memory Cell for the Next Generation Network Processors
Volume-3 | Issue-4
A STUDY OF RESEARCH NOTIONS IN WIRELESS BODY SENSOR NETWORK (WBSN)
Volume-1 | Issue-2
Computation of Constant Gain and NF Circles for 60 GHz Ultra-low noise Amplifiers
Volume-3 | Issue-3
Volume - 2 | Issue - 2 | june 2020
Published
18 May, 2020
For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.
KeywordsPrediction position Improved Bayesian enhanced Least-Squares Localization and tracking Wireless sensor networks
Full Article PDF Download Article PDF