Design and Implementation a Constrained Adaptive Estimation Algorithm for Low-cost SINS/GNSS in Urban Area

Due to stochastic noises, modeling uncertainties and nonlinearities in low-cost inertial measurement units (IMUs), the positioning error of Strap-down Inertial Navigation Systems (SINS) are increased exponentially. So, SINS is integrated with aiding navigation systems like a Global Navigation Satellite System (GNSS) by using an estimation algorithm to obtain an acceptable positioning accuracy. In urban area the GNSS signal may be obstructed because of tall trees and buildings. Therefore, in the paper a novel constrained adaptive integration algorithm is developed for integration the SINS/GNSS. In this algorithm, the velocities constraints in body frame in addition to altitude constraints based on a barometer data are firstly developed, and then a constrained estimation algorithm is designed based on the proposed constraints. In addition a fuzzy type 2 algorithm is used to calculate the estimator parameters based on vehicle maneuvers. The real vehicular tests are used for implantation and validation of the proposed algorithm. The experimental results indicate that, the proposed adaptive constrained estimation algorithm enhanced the estimation accuracy of the SINS steady states.