28 August 2009 A BP neural network model for sea state recognition using laser altimeter
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Proceedings Volume 7382, International Symposium on Photoelectronic Detection and Imaging 2009: Laser Sensing and Imaging; 738251 (2009) https://doi.org/10.1117/12.836356
Event: International Symposium on Photoelectronic Detection and Imaging 2009, 2009, Beijing, China
Abstract
A BP neural network method for the recognition of sea state in laser altimeter is presented in this paper. Sea wave is the typical stochastic disturbance factor of laser altimeter effecting on low-altitude defense penetration of the intelligent antiship missiles, the recognition of sea state is studied in order to satisfy the practical needs of flying over the ocean. The BP neural network fed with the feature vector of laser range-measurement presents the analysis of features and outputs the estimation result of sea state. The two most distinguishing features are the mean and the variance of the sea echo, which are extracted from the distance characteristics of sea echo using general theory of statistics. The use of a feedforward network trained with the back-propagation algorithm is also investigated. The BP neural network is trained using sample data set to the neural network, and then the BP neural network trained is tested to recognize the sea state waiting for the classification. The network output shows the recognition accuracy of the model can up to 88%, and the results of tests show that the BP neural network model for the recognition of sea state is feasible and effective.
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Chun-bo Shi, Xiao-dong Jia, Sheng Li, Zhen Wang, "A BP neural network model for sea state recognition using laser altimeter", Proc. SPIE 7382, International Symposium on Photoelectronic Detection and Imaging 2009: Laser Sensing and Imaging, 738251 (28 August 2009); doi: 10.1117/12.836356; https://doi.org/10.1117/12.836356
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