Kalman Filter‐based Approaches for Positioning: Integrating Global Positioning with Inertial Sensors

The Kalman filter (KF) theory is a fundamental milestone in signal processing and automatic control. This chapter discusses the main techniques related to Kalman filtering for satellite navigation. In particular, It explains the structure of the KF exploited in a global navigation satellite system (GNSS) receiver to compute the position of the user and to integrate the standard GNSS receiver with an inertial navigation system (INS). The chapter shows how KF is used in a standalone GNSS receiver to compute the classic position‐velocity‐time (PVT) solution. It provides some fundamentals of inertial navigation and discusses the characterization of the inertial sensors in terms of deterministic and stocastic noises, calibration, and alignment as fundamental prerequisites for any fruitful integration process. The three classic GNSS‐INS integration principles are presented, namely the so‐called loosely integrated, tightly integrated, and ultra‐tightly integrated architectures. Finally, the results of a live vehicular test campaign to compare two such implementations are shown and discussed.