Vision Based Mobile Target Geo-localization and Target Discrimination Using Bayes Detection Theory
In this paper, we develop a technique to discriminate ground moving targets when viewed from cameras mounted on different fixed wing unmanned aerial vehicles (UAVs). First, we develop a extended kalman filter (EKF) technique to estimate position and veloc
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Abstract. In this paper, we develop a technique to discriminate ground moving targets when viewed from cameras mounted on different fixed wing unmanned aerial vehicles (UAVs). First, we develop a extended kalman filter (EKF) technique to estimate position and velocity of ground moving targets using images taken from cameras mounted on UAVs. Next, we use Bayesian detection theory to derive a log likelihood ratio test to determine if the estimates of moving targets computed at two different UAVs belong to a same target or to two different targets. We show the efficacy of the log likelihood ratio test using several simulation results.
1 Introduction Over the past decade, there has been an increase in the use of Unmanned Aerial Vehicles (UAVs) in several military and civil application that are considered dangerous for human pilots. These applications include surveillance [1], reconnaissance [2], search [3], and fire monitoring [4, 5]. Among the suite of possible sensors, a video camera is inexpensive, lightweight, fits the physical requirements of small fixed wing UAVs, and has a high information to weight ratio. One of the important applications of camera equipped fixed wing UAVs is determining the location of a ground target when imaged from the UAV. The target is geo-localized using the pixel location of the target in the image plane, the position and attitude of the air vehicles, the camera’s pose angles, and knowledge of the terrain elevation. Previous target localization work using a camera equipped UAV is reported in [6, 7, 8, 9] and references therein. Barber et al. [7] used a camera, mounted on a fixed-wing UAV, to geolocalize a stationary target. They discussed recursive least square (RLS) filtering, bias estimation, flight path selection, and wind estimation to reduce the localization Rajnikant Sharma · Josiah Yoder · Hyukseong Kwon · Daniel Pack Academy Center for UAS Research, US Air Force Academy, Colorado e-mail: {rajnikant.sharma.ctr.in,josiah.yoder.ctr, hyukseong.kwon,daniel.pack}@usafa.edu M.A. Hsieh and G. Chirikjian (eds.), Distributed Autonomous Robotic Systems, Springer Tracts in Advanced Robotics 104, c Springer-Verlag Berlin Heidelberg 2014 DOI: 10.1007/978-3-642-55146-8_5,
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errors. Pachter at el. [6] developed a vision-based target geo-location technique that uses camera equipped unmanned air vehicles. They jointly estimate the target’s position and the vehicles’s attitude errors using linear regression resulting in improved target geo-localization. A salient feature of target geo-localization using bearing and range based sensors is the dependence of the measurement uncertainty on the position of the sensor relative to the target. Therefore, the influence of input parameters on nonlinear estimation problems, can be exploited to derive the optimal geometric configurations of a team of sensing platforms. However, maintenance of optimal configurations is not feasible given constraints on the kinematics of typical fixed wing aircraft. Frew [8] evaluated the sensitivit
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