INS/Odometer Land Navigation by Accurate Measurement Modeling and Multiple-Model Adaptive Estimation

Land vehicle navigation based on the inertial navigation system (INS) and odometers (ODs) is a classical autonomous navigation application and has been extensively studied over the past several decades. In this article, we seriously analyze the error characteristics of the OD pulses and investigate three types of OD measurement models in the INS/OD integrated system. Specifically, in the pulse velocity model, a preliminary Kalman filter is designed to obtain an accurate vehicle velocity from the accumulated pulses; the pulse increment model is accordingly obtained by integrating the pulse velocity; a new pulse accumulation model is proposed by augmenting the traveled distance into the system state. The three types of measurements, along with the nonholonomic constraint, are implemented in the standard extended Kalman filter. In view of the motion-related pulse error characteristics, the multiple model adaptive estimation (MMAE) approach is exploited to further enhance the performance. Simulations and long-distance experiments are conducted to verify the feasibility and effectiveness of the proposed methods. It is shown that the standard pulse velocity measurement achieves superior performance, whereas the accumulated pulse measurement is most favorable with the MMAE enhancement.