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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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A Hardware Implemented Autocorrelation Technique for Estimating Power Spectral Density for Processing Signals from a
Sameh Abdelazim1, David Santoro2, Mark Arend3
1The School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ 07666, USA. azim@fdu.edu.
Sensors (Basel, Switzerland)
|November 30, 2018
Summary
A new signal processing technique using autocorrelation enhances atmospheric signal preprocessing for wind sensing Coherent Doppler Lidar (CDL) systems. This method enables accurate wind velocity estimation up to 3 km under typical conditions.
Area of Science:
- Atmospheric Science
- Optical Remote Sensing
- Signal Processing
Background:
- Coherent Doppler Lidar (CDL) systems are crucial for remote wind velocity measurement.
- Effective signal preprocessing is essential for accurate wind speed estimation in CDL systems.
- Existing methods may face limitations under varying atmospheric conditions.
Purpose of the Study:
- To design and implement an autocorrelation-based signal processing technique for a 1.5 µm all-fiber CDL system.
- To enhance the preprocessing of atmospheric backscattered signals for improved wind velocity estimation.
- To evaluate the performance of the developed algorithm in terms of range and accuracy.
Main Methods:
- Developed an autocorrelation algorithm for signal preprocessing.
- Implemented the algorithm on a Field Programmable Gate Array (FPGA) for real-time processing.
- Utilized a 1.5 µm all-fiber CDL system with a 20 kHz PRF transmitter and 400 MHz sampling rate.
- Generated and accumulated real-time correlograms from adjustable range gates.
Main Results:
- The autocorrelation signal processing technique was successfully designed and implemented.
- The system achieved real-time accumulation of correlograms representing average autocorrelations.
- Wind velocity estimates were obtained up to 3 km range under nominal atmospheric conditions.
- The processed data yielded line-of-sight wind velocity measurements.
Conclusions:
- The autocorrelation-based signal processing technique effectively preprocesses atmospheric signals for CDL systems.
- The implemented FPGA-based design enables real-time wind velocity estimation.
- The system demonstrates reliable performance for wind sensing applications within a 3 km range.
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