Abstract
Recently, various research on unmanned aerial vehicles (UAVs) has been carried out to meet the need of a wide range of applications. Among a number of topics undertaken on UAVs, the problems of target tracking and precision landing are the most popular and crucial in many UAVs' tasks. This paper presents a series of works on target state estimation, which play essential roles in improving the performance of such tasks. First, we introduce an algorithm to estimate the target position with respect to the flying vehicle through an infrared camera and beacon. Next, by using Kalman filter theory, we proposed a method to estimate the velocity of the target from its position information. Finally, using experimental devices, we proposed a verification system to validate the accuracy and reliability of the estimation results before applying our algorithm to the tracking and precision landing tasks. Experimental results demonstrate the effectiveness of our works.
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