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The problem of data fusion based on filter is studied for an integrated inertial navigation system / Beidou navigation system / global positioning system (INS/BNS/GPS) with uncertain noise and conditionality of using GPS. The integrated navigation system can be divided into two integrated navigation subsystems (INS/BNS and INS/GPS). The signals from GPS and BNS receivers are easy to be disturbed, so filter is used to estimate the subsystem errors which are transmitted to fusion center online. Then data fusion is carried out by using the fuzzy fusion algorithm. Simulation results show that the algorithm can improve the accuracy and stability of navigation system.

There is a nonlinear measurement error of the vibration-cylinder air-pressure sensor when the environment temperature changes; To solve the problem, the paper carried out a research on vibration-cylinder air-pressure sensor temperature compensation based on radial basis function(RBF) neural network; The temperature error characteristics of the sensor was studied; The sensor temperature compensation of RBF neural network structures and algorithms was designed; The centers, variances, weights, and the hidden layer neuron number of the radial basis function were determined. The experiments showed that the trained RBF neural network can approximate the input-output relationship of the vibration-cylinder air-pressure sensor in high accuracy.