Dual Adaptive Factors-Based Integrated Navigation Performance Improvement for Airborne POS

Airborne position and orientation system (POS) can provide high accuracy motion parameters for multiple aerial mission loads and has become a vital equipment in aerial remote sensing system. However, the airborne POS often suffers from the systematic kinematic model and observation model inaccuracy, which further affects the accuracy of motion parameters, and the conventional innovation-based adaptive method only deals with the observation model inaccuracy and results in the limited performance improvement. In this paper, a dual adaptive factors filtering algorithm is adopted to deal with the kinematic model and observation model inaccuracy together, which has been validated by the real flight experiment and the results show that the performance improvement has achieved.