@inbook{15cc5dbcda314c33a63fa4eedb864b97,
title = "Neural network aided adaptive kaiman filter for multi-sensors integrated navigation",
abstract = "The normal Kaiman filter (KF) is deficient in adaptive capability, at the same time, the estimation accuracy of the neural network (NN) filter is not very well and the performance depends on the artificial experience excessively. It is proposed to incorporate a back-propagation (BP) neural network into the adaptive federal KF configuration for the SINS/GPS/TAN (Terrain Auxiliary Navigation)/SAR (Synthetic Aperture Radar) integrated navigation system. The proposed scheme combines the estimation capability of adaptive KF and the learning capability of BP NN thus resulting in improved adaptive and estimation performance. This paper addresses operation principle, algorithm and key techniques. The simulation results show that the performance of the BP NN aided filter is better than the stand-alone adaptive Kaiman filter's.",
author = "Lin Chai and Jianping Yuan and Qun Fang and Zhiyu Rang and Liangwei Huang",
year = "2004",
doi = "10.1007/978-3-540-28648-6\_60",
language = "英语",
isbn = "3540228438",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "381--386",
editor = "Fuliang Yin and Chengan Guo and Jun Wang",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}