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- W2050426765 abstract "A BP neural network method for the recognition of sea state in laser altimeter is presented in this paper. Sea wave is thetypical stochastic disturbance factor of laser altimeter effecting on low-altitude defense penetration of the intelligent antishipmissiles, the recognition of sea state is studied in order to satisfy the practical needs of flying over the ocean. TheBP neural network fed with the feature vector of laser range-measurement presents the analysis of features and outputsthe 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 feedforwardnetwork trained with the back-propagation algorithm is also investigated. The BP neural network is trainedusing sample data set to the neural network, and then the BP neural network trained is tested to recognize the sea statewaiting for the classification. The network output shows the recognition accuracy of the model can up to 88%, and theresults of tests show that the BP neural network model for the recognition of sea state is feasible and effective." @default.
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- W2050426765 date "2009-07-03" @default.
- W2050426765 modified "2023-09-26" @default.
- W2050426765 title "A BP neural network model for sea state recognition using laser altimeter" @default.
- W2050426765 doi "https://doi.org/10.1117/12.836356" @default.
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