An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

Abstract

In this chapter, the authors will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, they will look at the mathematical framework and technologies involved to design positioning systems. The authors will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. They will then look at the case of robustness of the positioning system when one or some of the sensors are faulty and will describe how the positioning system can be made more robust and adaptive in order to take into account the occurrence of faulty or degraded sensors. Finally, they will go one step further and explore possible architectures for collaborative positioning systems, whereas many vehicles are interacting and exchanging data to improve their own position estimate. The chapter is concluded with some remarks on the future evolution of the field.

\({\mathbb{T}}\)-Proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with Correlated Noises

“…Multi-sensor systems and related information fusion estimation theory have attracted much attention over the last few decades due to their wide range of applications in many fields, including target tracking, robotics, navigation, big data, and signal processing [1][2][3][4][5][6][7].…”

Multi-Sensor Fusion for Navigation of Ground Vehicles

“…However, it still suffers from error sources such as low frequency, ionosphere delay, and receiver electronics noise [4], [5]. Furthermore, GPS signals require clear view of the sky.…”