Abstract
This chapter focuses on approaches for the adaptive Zero‐Velocity Update‐aided pedestrian inertial navigation. It presents Inertial Measurement Unit (IMU)‐based floor type detection for pedestrian inertial navigation. In this method, one foot‐mounted IMU is required in the system as before, which greatly reduces the complexity of the overall system. The chapter describes some details of the algorithm and the trade‐offs involved in the parameter selection. A navigation result is presented as an example showing the effect of the floor type detection on the improvement of navigation accuracy. The chapter discusses a combination of the principal component analysis, artificial neural network, and multiple‐model Kalman filter as a potential adaptive implementation, showing that it is able to achieve a high classification accuracy with a reasonable amount of calculation. It also presents an adaptive threshold based on the Bayesian approach to enable the detector to work with various walking or running speeds.
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