A signal processing technique utilizing autocorrelation of backscattered signals was designed and implemented in a 1.5 µm all-fiber wind sensing Coherent Doppler Lidar (CDL) system to preprocess atmospheric signals. The signal processing algorithm’s design and implementation are presented. The system employs a 20 kHz pulse repetition frequency (PRF) transmitter and samples the return signals at 400 MHz. The logic design of the autocorrelation algorithm was developed and programmed into a field programmable gate array (FPGA) located on a data acquisition board. The design generates and accumulates real time correlograms representing average autocorrelations of the Doppler shifted echo from a series of adjustable range gates. Accumulated correlograms are streamed to a host computer for subsequent processing to yield a line of sight wind velocity. Wind velocity estimates can be obtained under nominal aerosol loading and nominal atmospheric turbulence conditions for ranges up to 3 km.
Safe and efficient marine navigation in ice-infested waters requires comprehensive and timely information on the sea ice conditions. These include information on the ice concentration and type, ice edge location, icebergs and open leads. The Canadian Ice Service is responsible for providing ice information in Canadian waters, mainly through its daily ice charts. Unfortunately, due to the difference in time between the ice chart production and its use by mariners, the ice information is always out of date. This problem might be overcome by developing a neural network-based system for predicting the ice conditions over time. A supervised neural network is trained to predict the ice conditions at a given location and time using the current ice charts, which are provided by the Canadian Ice Service. The input ice data is mapped to an output vector that gives the predicted ice conditions. The traditional non-modular feed-forward neural network structure failed to map the required function, and hence, was modularized to give better prediction performance. Each neural module was responsible for the prediction of a 5i5 km area, while the ice characteristic of interest was the total concentration.
KEY WORDS1. Sea Ice.2. Modelling. 3. Safety..
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