Vision-based Target Geo-location using a Fixed-wing Miniature Air Vehicle

Barber, D. Blake; Redding, Joshua; McLain, Timothy W.; Beard, Randal W.; Taylor, Clark N.

doi:10.1007/s10846-006-9088-7

Cited by 188 publications

(104 citation statements)

References 16 publications

(21 reference statements)

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“…광학카메라 기반의 목표물 위치 추정 [3] 광학 카메라 기반의 목표물 위치 추정은 영상 정보 내에 서 목표물의 픽셀위치를 알고 있다는 가정을 바탕으로 한 그림4. 지상목표물(마커) 항공촬영및짐벌트래킹.…”

Section: 위치 추정 알고리즘unclassified

Experimental Verification of Multi-Sensor Geolocation Algorithm using Sequential Kalman Filter

Lee¹,

Kim²,

Bang³

2015

Journal of Institute of Control, Robotics and Systems

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Unmanned air vehicles (UAVs) are getting popular not only as a private usage for the aerial photograph but military usage for the surveillance, reconnaissance and supply missions. For an UAV to successfully achieve these kind of missions, geolocation (localization) must be implied to track an interested target or fly by reference. In this research, we adopted multi-sensor fusion (MSF) algorithm to increase the accuracy of the geolocation and verified the algorithm using two multicopter UAVs. One UAV is equipped with an optical camera, and another UAV is equipped with an optical camera and a laser range finder. Throughout the experiment, we have obtained measurements about a fixed ground target and estimated the target position by a series of coordinate transformations and sequential Kalman filter. The result showed that the MSF has better performance in estimating target location than the case of using single sensor. Moreover, the experimental result implied that multi-sensor geolocation algorithm is able to have further improvements in localization accuracy and feasibility of other complicated applications such as moving target tracking and multiple target tracking.Keywords: multi-sensor, target tracking, geo-location, localization, sensor fusion, unmanned air vehicles, multicopter

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Section: 위치 추정 알고리즘unclassified

Experimental Verification of Multi-Sensor Geolocation Algorithm using Sequential Kalman Filter

Lee¹,

Kim²,

Bang³

2015

Journal of Institute of Control, Robotics and Systems

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“…In order to illustrate the effectiveness of the modified guidance law, a simulation is conducted in which the bang-bang controller proposed in [10], as shown in Eq. (20), was used:…”

Section:  mentioning

confidence: 99%

Vision-Based Detection and Tracking of a Mobile Ground Target Using a Fixed-Wing UAV

Wang

Zhu

Zhang

et al. 2014

International Journal of Advanced Robotic Systems

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This paper presents a framework for tracking a mobile ground target (MGT) using a fixed-wing unmanned aerial vehicle (UAV). Challenges from pure theories to practical applications, including varying illumination, computational limits and a lack of clarity are considered. The procedure consists of four steps, namely: target detection, target localization, states estimation and UAV guidance. Firstly, the MGT in the wild is separated from the background using a Laplacian operator-based method. Next, the MGT is located by performing coordinate transformations with the assumption that the altitude of the ground is invariant and known. Afterwards, a Kalman filter is used to estimate the location and velocity of the MGT. Finally, a modified guidance law is developed to guide the UAV to circle and track the MGT. The performance of our framework is validated by simulations and a number of actual flight tests. The results indicate that the framework is effective and of low computational complexity, and in particular our modified guidance law can reduce the error of the tracking distance by about 75% in specified situations. With the proposed framework, such challenges caused by the actual system can be tackled effectively, and the fixed-wing UAV can track the MGT stably.

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“…For stationary target tracking, circular trajectories overhead the target are widely accepted in the literature as good trajectories. 5,38,46 However, the optimal circling radius is not known and, if the target is moving, the operator may not deduce the best vehicle trajectory. Autonomous flight path generation is therefore essential to successful target localization.…”

Section: Ia Motivationmentioning

confidence: 99%

“…Ivey and Johnson 18 propose a vision-based target tracking filter for use on board a UAV, comparing both an Extended Kalman Filter (EKF) and a Square-Root Unscented Kalman Filter (SRUKF). Barber et al 5 explore stationary target localization from small fixed-wing UAVs using a Recursive Least Squares (RLS) filter. They also explore flight path optimization by finding an optimal altitude and radius for a circular trajectory above the stationary target.…”

mentioning

confidence: 99%

Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles

Ponda

Kolacinski

Frazzoli

2009

AIAA Guidance, Navigation, and Control Conference

117

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Small unmanned aerial vehicles (UAVs) equipped with navigation and video capabilities can be used to perform target localization. Combining UAV state estimates with image data leads to bearing measurements of the target that can be processed to determine its position. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods are prone to biases, noisy estimates, and filter instabilities. The performance of the target localization is highly dependent on the vehicle trajectory, motivating the development of optimal UAV trajectories. This work presents methods for designing trajectories that increase the amount of information provided by the measurements and shows that these trajectories lead to enhanced estimation performance.The Fisher Information Matrix (FIM) is used to quantify the information provided by the measurements. Several objective functions based on the FIM are considered and the A-optimality criterion is shown to be the best suited for trajectory optimization in the 3-D bearings-only target localization problem. The resulting trajectories produce spirals, which increase the angular separation between measurements and reduce the range to the target, supporting geometric intuition. The problem of simultaneous target estimation and vehicle trajectory optimization is explored and the resulting algorithms produce vehicle trajectories that increase the information provided by the measurements, enhancing the target estimation performance by increasing accuracy, reducing uncertainty and improving filter convergence.

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Vision-based Target Geo-location using a Fixed-wing Miniature Air Vehicle

Cited by 188 publications

References 16 publications

Experimental Verification of Multi-Sensor Geolocation Algorithm using Sequential Kalman Filter

Experimental Verification of Multi-Sensor Geolocation Algorithm using Sequential Kalman Filter

Vision-Based Detection and Tracking of a Mobile Ground Target Using a Fixed-Wing UAV

Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles

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