Abstract:

In a low-cost strapdown inertial navigation system(SINS), a multilayer feedforward neural network (NN) was designed to compensate the gyros asymmetry dynamic errors which caused attitude drift in rate oscillation. To reduce the accuracy demand of the reference signals in calibrating the NN model, the terminal attitude errors were computed as the network performance function for NN training. Unlike the supervised training, the terminal attitude errors were not the network target outputs. Therefore, the particle swarm optimization algorithm was applied to train the network.

Key words: low-cost,  , strapdown , inertial , navigation , system,  , asymmetry , dynamic , error,  , calibration,  , compensation,  , multilayer , feedforward , neural , network,  , particle , swarm , optimization