Abstract
This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a measuring system to the platform, the texture image of platform base bulkhead can be collected in a real-time manner. Through the image registration, the displacement vector of the platform relative to its bulkhead can be calculated to further determine angular displacements. After being decomposed and superposed on the three attitude angles of the UAV, the angular displacements can reduce the coordinate transformation errors and thus improve the localization accuracy. Even a simple kind of method can improve the localization accuracy by 14.3%.
Highlights
Enemy situation reconnaissance, target localization, directing and adjusting artillery fire, and other auxiliary functions are still the main Unmanned Aerial Vehicle (UAV) applications
The common target localization process is usually done through the transformations among at least five coordinate systems, including the camera coordinate system C, UAV coordinate system B, UAV geographic coordinate system V, Earth-Centered Earth-Fixed coordinate system (ECEF) E and the geodetic coordinate system G
Based on the combination of camera and laser range finder, this paper proposes a method to improve the accuracy of angles, which could improve the accuracy of single-point localization in real time
Summary
Enemy situation reconnaissance, target localization, directing and adjusting artillery fire, and other auxiliary functions are still the main UAV applications. These methods, which are based on sensor data fusion, involve the complex issue of fleet route control [14,15,16] and need coordination among multiple UAVs, resulting in a higher hardware cost, more task time in most of the cases, and a greater risk in case of emergency These exceptions apart, methods based on Kalman filter, Recursive Least Squares (RLS) filter, nonlinear filter [3,6,17,18] and methods based on video sequence [19,20,21,22,23,24] are proposed to estimate the location. The principle and working process of the system designed in this paper are introduced in detail, followed by effectiveness analysis, experiments, simulation and verification, analysis of verification results, and a summary
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