COMPARISON OF GPS/IMU INTEGRATION METHODS FOR REAL-TIME VEHICLE POSITIONING

Abstract

This paper addresses the challenge of determining vehicle coordinates in real-time telemetry systems through the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data. A comprehensive analysis of existing sensor-fusion approaches enabled the formulation of the problem within the framework of embedded, resource-constrained real-time platforms and underscored the necessity of a systematic comparative evaluation of methods with fundamentally different computational and accuracy characteristics. Three sensor-fusion methods are considered: the complementary filter, which combines data through frequency-domain separation; the Extended Kalman Filter, which relies on Jacobian-based linearization of the nonlinear motion model; and the Unscented Kalman Filter, which applies a deterministic sigma-point transform to approximate nonlinear state propagation without linearization. The paper substantiates the choice of methodological foundations underlying each method, including the kinematic vehicle state model comprising geographic coordinates, velocity, heading angle, and sensor bias offsets, as well as the metrics used for their quantitative comparison — positioning RMSE, circular error probable, dead-reckoning drift during GPS outages, and update latency. The growing demand for reliable positioning under GPS-denied conditions, combined with the limited computational resources of embedded telemetry platforms and the nonlinear nature of vehicle kinematics, highlights the need to establish practical, hardware-aware criteria for filter selection. To address this, all three algorithms were implemented on a common hardware and software platform — a Raspberry Pi 5 single-board computer running Python 3.11 — and validated against real driving data collected on a 4.7 km route, with ground truth provided by an RTK GPS receiver. The paper presents experimental results demonstrating the relative effectiveness of the proposed methods in terms of positioning accuracy, robustness to GPS signal loss, and behavior under aggressive maneuvering. Additionally, it outlines practical recommendations for selecting an integration method depending on the computational constraints of the target embedded platform, ranging from microcontroller-based systems to single-board computers and DSP platforms