Multiobjective Robust filtering Algorithm for Integrated Navigation System Based on convex optimization

Kalman filter is widely applied in integrated navigation system.When there exists uncertainty in system parameters or the statistics of the noises,the performance of the standard Kalman filtering will be greatly degraded,the precision drops.H∞ robust filter based on the Riccati equation has robust ability,but its performance parameter is selected by experience and has no systematic method.It is difficult to carry out in practice.According to the optimal control theory and using the convex optimization techniques,an mix H2/H∞ robust filter based on LMI(Linear matrix inequality) with high accuracy GPS and the CNS measure information is presented.It can resolve model uncertainty and the noise non-Gauss question.The hardware in-the-loop simulation of SINS/CNS/GPS Integrated Navigation System is used to compare the precision.The simulation results indicate that multiobject robust filter have better robustness and more precise than H∞ filter using real apparatus data.