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In this paper, we present a decentralized cooperative multiple target tracking method for multiple Unmanned Aerial Vehicles (UAVs). The decentralized cooperative multi-target tracking algorithm incorporates an optimal sensor management scheme and a cooperative path planner. To localize and track targets, a set of Extended Kalman Filters (EKFs) is used onboard each UAV and resulting target estimates...
The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. In this work a numerically efficient implementation of a sigma-point Kalman filter is applied to the attitude and relative position estimation of a small-size tethered unmanned helicopter. For that purpose, the state prediction is performed using a kinematic process model driven...
In order for Remotely Piloted Aircraft Systems (RPAS) to coexist seamlessly with manned aircraft in non-segregated airspace, enhanced navigational capabilities are essential to meet the Required Navigational Performance (RNP) levels in all flight phases. A Multi-Sensor Data Fusion (MSDF) framework is adopted to improve the navigation capabilities of an integrated Navigation and Guidance System (NGS)...
This paper proposed a framework for airborne multisensor management for multitarget tracking, considering each of multiple unmanned aircraft as a sensor. The purpose of the algorithm is to quantify the performance of the multitarget tracking and to determine the time and location of the sensor deployment. Utilizing the posterior Cramer-Rao lower bound, the inverse of the Fisher information matrix,...
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