Applications of CNN/Kalman Filtering Technique in Integrated Navigation System

The conventional Kalman filter assumes that the statistical properties of the noise in dynamic model and observation system are exactly known, but the noise in integrated system is uncertain. So the paper puts forward a new method using combined neural network-aided Kalman filter. Simulations suggest that the precision of complex neural network (CNN) is 2 times more than that of conventional Kalman filter, and the convergence time is 200 s less than the latter. Thus it can overcome the shortcomings of conventional neural network, such as slow learning speed and poor generalization ability, and the whole system has adaptive capability to deal with the disturbance in dynamic situation.