RIBATS: RSSI-based adaptive tracking system with ASEKF for indoor WSN

Abstract

Wireless indoor tracking systems face challenges due to environmental conditions and signal attenuation, affecting location accuracy, crucial in wireless sensor network (WSN) applications. Many tracking techniques rely on specific path loss models proposed by previous researches, but these models are susceptible to changes in environmental conditions, impacting estimation outcomes. In order to solve these problems, this paper propose adaptive tracking system using received signal strength indicator (RSSI) measurement parameter called as RIBATS. Adaptive in this system refers to the reliability of an algorithm for obtaining the accurate location without any path loss modelling at dynamic indoor environments. The enhancement of weighted centroid localization (eWCL) scheme calculates the location estimation only using RSSI data measurement without propagation characterisic determination. However, estimation result from eWCL still have high error at certain area. Hence, by defining a multiplier factor as adaptive scaled to the covariance matrix of EKF can eliminate distortion effects from eWCL called as adaptive scaled extended Kalman filter (ASEKF) algorithm. An effective variance estimation algorithm for adaptive indoor tracking system using eWCL and ASEKF combination achieve 0.82 meters mean square error (MSE) value with 55.67% error reduction. Then, without using multiplier scale factor at EKF algorithm only reduce previous eWCL at 3.78% with 1.78 meters MSE value.