Visualization of Mini-UAV Flying Path Using GPS Log In Connection to - - PowerPoint PPT Presentation
Visualization of Mini-UAV Flying Path Using GPS Log In Connection to Surveillance Radar Target Tracking Zheng Maya 18S7B Prof Lin Zhiping NTU-NRP EEE15 Radar Tracking Image taken from: https://www.eol.ucar.edu/content/how-do-radars-work
Prof Lin Zhiping NTU-NRP EEE15
❖ What is Radar:
➢ RAdio Detection And Ranging ➢ Object-detection system ➢ Produce radio or microwaves which are Reflected off objects to receiver
Image taken from: https://www.eol.ucar.edu/content/how-do-radars-work
➢ Flying Aircrafts controlled without a pilot within the aircraft ➢ May have built in sensors and GPS for easy tracking and control
Micro/Nano-UAV Mini-UAV Large-UAVs Medium-UAVs
Small size Slow flying speed Low flying altitude Weak Echo Signal
Difficult targets to be detected by the current surveillance radar system
➢ Visualize the flying path of mini-UAVs based on GPS log ➢ Investigate into Radar target tracking errors by means of a comparison between the GPS flying path and the radar tracking results
❖ Extract Longitude, Latitude, Relative height, and Acceleration from GPS data ❖ Use MatLab
➢ Construct flying path from the extracted data ➢ Display the flight path in a 3D plot ➢ Determine Velocity of UAV at every point and alter the thickness of the trajectory to better visualize the movement of the UAV
❖ Draw a triangle to represent the UAV
➢ It moves along the trajectory to show the motion of UAV
❖ Design a more effective GUI (Graphical User Interface)
➢ GPS data is converted into KML file, which is then presented in GoogleEarth ➢ An image of the flight path presented on GoogleEarth is screenshotted ➢ Image of the flight path is displayed in GUI
❖ Convert GPS coordinates to Polar Coordinates ❖ Interpolate
➢ Alter GPS time frame and data coordinates to match those of the radar time frame
❖ Eliminate time lag
➢ Radar data lags behind the GPS data time ➢ Eliminate time lag after interpolation
Convert Radar coordinates into Cartesian coordinates
Plot the trajectory
from radar data
Visually estimate part of GPS graph that matches Radar graph Find part of GPS data that has minimum difference with radar data Calculate the time lag by using the time that corresponds to the point with
trajectory with both 2D map and 3D maps
(a) 3D trajectory from GPS data (b) 2D trajectory from Radar data
(c) 2D projection of the full GPS trajectory (d) Estimated region that matches radar data
❖ From the flying time, visualization of GPS trajectory and 2D Radar graph, the part of the GPS graph that appears to match the Radar trajectory shape is around points 550~1105 ❖ The difference between the range from radar data and that from GPS data was found and plotted into a graph. ➔ Our experiment with the three sets
time lag of 27, 26 and 33 seconds.
○ methods for reconstructing the flying trajectory and speed of mini-UAVs from GPS data and Radar data ○ tools for visualizing the trajectory and animating the flying of the mini-UAV ○ approximate method for comparing the GPS and Radar trajectories and finding the errors
radar can help find certain errors in the radar tracking, which can be used to analyze radar tracking errors and the pattern.