Real Time Position Location & Tracking (PL&T) using Prediction Filter and Integrated Zone Finding in OFDM Channel

TL;DR

This paper proposes a real-time PL&T system using prediction filters and integrated zone finding in OFDM channels, enhancing accuracy in GPS-denied environments with dynamic node roles and multipath channel analysis.

Contribution

It introduces a novel integrated zone finding and triangulation algorithm combined with Kalman filtering for improved PL&T accuracy in mobile networks.

Findings

Enhanced PL&T accuracy in Rayleigh and Rician channels

Effective dynamic role switching of nodes improves localization

Robust performance under multipath fading conditions

Abstract

The nature of pre-determined and on-demand mobile network fabrics can be exploited for real time Position Location and Tracking (PL&T) of radios and sensors (nodes) for Global Positioning System (GPS) denied or GPS-free systems. This issue is addressed by a novel system of integrated zone finding and triangulation method for determining the PL&T of nodes when mobile network fabrics are employed based on using directional antennas for radio communications. Each mobile node is switched dynamically between being a reference and a target node in PL&T operation to improve the PL&T accuracy of a target node. This paper presents the Baseline PL&T with predictive Kalman filter and Integrated Zone based PL&T algorithm design that integrates zone finding and triangulation method. The performance of the proposed algorithm is analysed using Interleaving-KV sample coding & error correction in Rayleigh and Rician channel using Orthogonal Frequency Division Multiplexing (OFDM) system under the severe multipath fading.