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Abstract
Besides collecting and broadcasting aviation surveillance parameters, Automatic Dependent Surveillance-Broadcast (ADS-B) is also a novel technique to sense and share meteorological information such as wind field with high update rate and accuracy. Many ADS-B devices on aircraft can construct a real-time and dynamic sensor network. Although the ADS-B message format reserves the items especially for wind information, few aircrafts broadcast these items at present. The current solution is to use the aircraft trajectory captured by ADS-B for wind vector inversion. Nevertheless, some algorithms still have some downsides, especially the stability in small-angle turning situations. This article is committed to developing a novel algorithm capable of working in both small and large angle turning situations with high efficiency, with an emphasis on small angle situations. By virtue of the algorithm in our recent research which is derived from the Particle Filter model, this algorithm takes advantage of circle geometry property and Euclidean distance standard deviation (STD). In the simulation test, the effect of true airspeed (TAS) difference on the mean absolute error of wind estimate, the effect of true wind speed on wind estimate, the effect of maneuver turning angle on wind estimate, and the computational complexity are assessed, respectively. Moreover, for real ADS-B data, both large and small-angle turning maneuver situations are tested and compared separately. Also compared is the level of the results concentration for the wind speed and TAS along with the geometric height. Consequently, the simulation and the real data test shows that the proposed STD algorithm has performance superior to other two algorithms LS and LM especially in the small-angle turning situation such as below 40deg. STD's performance is between other two algorithms in computational complexity. This property can help improve the algorithm's stability and data utilization for small-angle turning significantly, which is very useful in real aviation surveillance operations.
Highlights
In daily operations, meteorology and aviation are inseparable because aviation is sensitive to common meteorological factors such as wind, humidity, and air pressure
We describe the wind speed vector inversion algorithms, the LS proposed by Hurter et al [22], the LM by Leege et al [19], and our novel standard deviation (STD) method
The wind speed and direction estimates are negatively affected by the true airspeed (TAS) standard deviation
Summary
Meteorology and aviation are inseparable because aviation is sensitive to common meteorological factors such as wind, humidity, and air pressure. Among these meteorology factors, wind shear ranks the most common and dangerous one because of its invisibility to naked eyes, occurrence whenever and wherever possible, rapid change, and low predictability. Headwind shear, vertical wind shear, and lateral wind shear significantly affect the lift, balance, and attitude of the aircraft. They seriously threaten the flight safety during low altitude flight, approaching, landing and take-off. Real-time detection of wind field information in aviation and early warning of wind field shear change through atmospheric detection system are two essential factors concerning aviation and airport safety operation.
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