The Hybrid NPO-GRNN Method for Real-Time Multi-Target Localization and Tracking in WSN Utilizing the Kalman Filter

Abstract

This study aims to determine the location and track of sensor nodes in indoor spaces. The challenge of significant estimation errors in target position brought on by erratic noise in received signal strength indicator (RSSI) readings is a major area of current research focus, especially in interior conditions. In place of the traditional RSSI-based approach, this study suggested a hybrid technology called Nomadic People Optimizer-Generalized Regression Neural Network (NPO-GRNN) to increase the sensor nodes' capacity to estimate location and target tracking with more accuracy. The RSSI values can be used by the GRNN method as start data to determine and trace the target node's location. The spread constant (σ) is a crucial part of the GRNN architecture. To choose the spread constant (σ), an insecure and sometimes unreliable method by trial and error is employed. The ideal GRNN spread constant is found using the NPO approach. To get around these problems and improve L & T tracking precision without the need for additional equipment, the hybrid NPO-GRNN method was employed, and these coordinates were refined using a Kalman filter to increase accuracy. Impressive results were obtained by the tracking algorithm NPO-GRNN-UKF hybrid, which performed better than the traditional LNSM approach. By comparing the suggested approach to the traditional RSSI, a significant 98.4% and 98.1% for targets 1 & 2, respectively, gain can be achieved.

Keywords

• RSSI, GRNN, NPO, Target Tracking, Indoor localization, Trilateration