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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 realtime 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 paper 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.

A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where robot pose is changed to reorient the sensor to areas of interest for perception. Further, egomotion such as jitter, and external effects such as wind and others affect perception requiring additional effort in software such as image stabilization. This effect is particularly pronounced in micro-air vehicles and micro-robots who typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real-time. We present a novel microelectromechanical (MEMS) mirror LiDAR system to change the field of view of the LiDAR independent of the robot motion. Our design has the potential for use on small, low-power systems where the expensive components of the LiDAR can be placed external to the small robot. We show the utility of our approach in simulation and on prototype hardware mounted on a UAV. We believe that this LiDAR and its compact movable scanning design provide mechanisms to decouple robot and sensor geometry allowing us to simplify robot perception. We also demonstrate examples of motion compensation using IMU and external odometry feedback in hardware.

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