Research on an Adaptive Filtering Algorithm of Exponentially-Weighted Fading Memory Used for Measurement Range of Hydrometric Monitoring Sensors

The complex hydrological status affected by the water surface rock, floats debris and other random disturbances, leading to the faults of monitoring automation, the current hydrological monitoring in monitoring automation, measurement accuracy, measurement cost, scope of application and other aspect...

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Bibliographic Details
Published in:2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA) pp. 697 - 701
Main Authors: He Jincan, Feng Maoyan
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2015
Subjects:
Area measurement
Fading channels
fading memory filter
Filtering algorithms
flood monitoring
Kalman filter
Measurement uncertainty
Monitoring
range adaptation
sensor
Time measurement
Ultrasonic variables measurement
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Summary:The complex hydrological status affected by the water surface rock, floats debris and other random disturbances, leading to the faults of monitoring automation, the current hydrological monitoring in monitoring automation, measurement accuracy, measurement cost, scope of application and other aspects in current flood monitoring. To ensure the accuracy and completeness of flood information, make this research on the sensor range adaptation in the case of the water level measurement. It is difficult for accurately hydrological modeling in accord of the flood change, therefore, the filter divergence will soon appear by means of Kalman filtering technique. But the use of filter algorithm based on the weighted fading memory will estimate whether the divergence can be syncretized by the divergence overcoming. It can be seen through the simulation that by the means of filter algorithm based on the weighted fading memory will better restrain the divergence and have stronger adaptability. It can reduce the measuring blind area at the same time of increasing the measuring range, effectively filter the random error and gross error, reduce the average measurement cycle and enhance the real-time of the system.
DOI:10.1109/ICICTA.2015.177