Mitigation of Rain Effect on Wave Height Measurement Using X-Band Radar Sensor

The presence of rain can negatively affect the performance of many sensors such as X-band radar. In this paper, an effective approach is proposed to mitigate the effect of rain on significant wave height ( {H}_{s} ) estimation from X-band radar sensor data along with a machine-learning (ML)-based me...

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Bibliographic Details
Published in:IEEE sensors journal 2022-03, Vol.22 (6), p.5929-5938
Main Authors: Yang, Zhiding, Huang, Weimin, Chen, Xinwei
Format: Article
Language:English
Subjects:
Algorithms
Estimation
Feature extraction
Haze
Haze removal algorithm
Image sensors
Machine learning
Mathematical analysis
Radar
Radar imaging
Radar measurements
Rain
Regression
Sensors
Signal to noise ratio
significant wave height (<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">H s)
Superhigh frequencies
Support vector machines
Wave height
X-band radar sensor
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Summary:The presence of rain can negatively affect the performance of many sensors such as X-band radar. In this paper, an effective approach is proposed to mitigate the effect of rain on significant wave height ( {H}_{s} ) estimation from X-band radar sensor data along with a machine-learning (ML)-based method. First of all, the haze removal algorithm is applied to rain-contaminated radar images as pre-processing. Then, three different features are extracted from the processed radar images. Different combinations of these three features are utilized to estimate {H}_{s} under the rain condition by using support vector regression (SVR)-based and temporal convolutional network (TCN)-based regression methods. It is found that the root-mean-square-errors (RMSEs) of {H}_{s} estimation results using two typical methods (signal-to-noise ratio (SNR)-based and ensemble empirical mode decomposition (EEMD)-based linear fitting methods) are decreased by 0.14 m and 0.48 m after introducing the haze removal algorithm, respectively. Also, a relatively high accuracy can be achieved using the SVR-based regression method with the combination of SNR and gray level co-occurrence matrix (GLCM) features. Compared to the SNR-based and EEMD-based linear regression methods, the proposed SVR-based method further improves the estimation accuracy, with reductions of RMSE by 0.19 m and 0.82 m, respectively.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3149852