Identification of INS Sensor Errors from Navigation Data Based on Improved Pigeon-Inspired Optimization

Abstract

The error level of inertial sensor parameters determines the navigation accuracy of an inertial navigation system. For many applications, such as drones, errors in horizontal gyroscopes and accelerometers, can significantly affect the navigation results. Different from most methods of filter estimation, we innovatively propose using evolutionary algorithms, such as the improved pigeon-inspired optimization (PIO) method, to identify sensor errors through navigation data. In this method, the navigation data are firstly collected; then, the improved carrier pigeon optimization method is used to find the optimal error parameter values of the horizontal gyroscope and accelerometer, so as to minimize the navigation result error calculated by the navigation data. At the same time, we propose a new improved method for pigeon-inspired optimization with dimension vectors adaptive mutation (DVPIO for short) that can avoid local optima in the later stages of the iteration. In the DVPIO method, 2n particles with poor fitness are selected for the following variation, with 2n dimension vectors when it is judged that the position is premature, where n represents the number of parameters to be identified; a dimension vector only represents the positive or negative change of a parameter, whose change amount is d can be adjusted adaptively. DVPIO method has better stability, faster convergence speed, and higher accuracy. This work has potential to reduce the need for the disassembly and assembly of the INS and return it to the manufacturer for calibration.