Multi-Sensor Integration Based on a New Quantum Neural Network Model for Land-Vehicle Navigation

摘要

This paper aims to develop an efficient and accurate multi-sensor integration method for land vehicle navigation. For this purpose, a novel multi-sensor integration model was created based on quantum neural networks (QNNs) and back-propagation training. According to the information interaction mode of biological neurons and the theory on the QNNs, the author firstly put forward a QNN consisting of weighting, aggregation, activation and prompting, and then built a QNN model based on the proposed network. Then, the multi-layer feedforward QNN was combined with back-propagation learning to form a multi-sensor integration approach for land-vehicle navigation. Finally, the efficiency and accuracy of the proposed approach was verified through simulation and field test. This research sheds new light on the integration of data from multiple sensors and the improvement of land-vehicle navigation.