Aircraft self-organization algorithm with redundant trend

In order to improve the accuracy of aircraft navigation systems, one error compensating model for autonomous navigation systems is established based upon inertial navigation systems by applying the Kalman filtering algorithm and other prediction algorithms. In the case of the absence of partial measurement information, linear and harmonic functions are proposed to be selected as basic functions according to characteristics of mathematic error models of navigation systems. A novel adaptive self-organization measuring complex is built on the basis of self-organization algorithm with redundant trends by utilizing some self-organization selection criteria. The mathematic simulation and real test based on actual navigation testing systems are executed. The analysis results show that self-organization algorithm with redundant trends can secure the higher prediction accuracy of navigation system errors and meet real-time requirements.