An IMU-Aided Carrier-Phase Differential GPS Positioning System

TL;DR

This paper presents a Bayesian network-based IMU-aided carrier-phase differential GPS system for land vehicle navigation, integrating multiple sensor measurements to improve positioning accuracy.

Contribution

It introduces a novel Bayesian framework for tightly coupling GPS, IMU, and speedometer data in land vehicle navigation systems.

Findings

Enhanced positioning accuracy through sensor integration

Effective constraint enforcement for vehicle motion

Detailed implementation of the Bayesian filtering approach

Abstract

We consider the problem of carrier-phase differential GPS positioning for an land vehicle navigation system (LVNS), tightly coupled with an inertial measurement unit (IMU) and a speedometer. The primary focus is to apply Bayesian network to an IMU-aided GPS positioning system based on carrier-phase differential GPS. We describe the implementation details of the positioning system that integrates GPS measurements (i.e., pseudo-range, carrier-phase and doppler), IMU measurements, and speedometer measurements. We derive the linearized state process equation and the measurement equation for GPS and speedometer. To account for constraints of land vehicle, we add two more pseudo measurements to ensure the perpendicular velocities close to zero.