Next Article in Journal
Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs
Next Article in Special Issue
Positioning Performance of BDS Observation of the Crustal Movement Observation Network of China and Its Potential Application on Crustal Deformation
Previous Article in Journal
Smartphone Heading Correction Based on Gravity Assisted and Middle Time Simulated-Zero Velocity Update Method
Previous Article in Special Issue
A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography
Open AccessArticle

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

1
Doctoral Program on Space Technology Applications, Beijing 100191, China
2
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3350; https://doi.org/10.3390/s18103350
Received: 9 September 2018 / Revised: 23 September 2018 / Accepted: 28 September 2018 / Published: 8 October 2018
(This article belongs to the Special Issue High-Precision GNSS in Remote Sensing Applications)
Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s. View Full-Text
Keywords: wind speed estimation; GNSS-reflectometry; artificial neural network; particle swarm optimization; particle filter; BeiDou GEO satellite wind speed estimation; GNSS-reflectometry; artificial neural network; particle swarm optimization; particle filter; BeiDou GEO satellite
Show Figures

Figure 1

MDPI and ACS Style

Kasantikul, K.; Yang, D.; Wang, Q.; Lwin, A. A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry. Sensors 2018, 18, 3350.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop