UNCERTAINTY QUANTIFICATION TOWARDS FILTERING OPTIMIZATION IN SCENE MATCHING AIDED NAVIGATION SYSTEMS

There exist many uncertain sources for positioning process of scene matching aided navigation systems, and filter architecture and random parameters are considered mainly in this paper for the goal of trajectory optimization. In order to reduce the uncertainty of filter architecture, a practical scene matching optimization scheme is proposed, where fusion architecture and filtering parameters are both considered, in the circumstance of different sampling rates without transmission delay. The matching interval is determined by adjustment time of matching, matching points are settled from back to front, a cost function for the matching number is designed according to optimization criteria, and finally an appropriate matching number is determined by required mapping accuracy. The optimization scheme is validated by the simulation. On the other hand the filtering parameters are identified and validated by reducing parameter space in advance and local sensitivity analysis method. Simulation results illustrate that the systems are more sensitive for the measurement noise, which provides a theoretical basis for engineering applications.