Ground Target Tracking Using UAV with Input Constraints
ABSTRACT This paper deals with the problem of adversarial ground target tracking using Unmanned Aerial Vehicles (UAVs) subject to input
constraints. For adversarial ground target tracking, tracking performance and UAV safety are two important considerations
during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking
around the target. Exposure avoidance of the UAV to the target and minimizing the exposure time are studied respectively in
terms of the initial state of the UAV. The performance of the proposed controller in both cases is also analyzed. Simulation
results demonstrate the effectiveness of the proposed approach.
- SourceAvailable from: Seunghan Lim
- "They considered an unknown moving target motion and wind, and established information architectures modeled by rigid graph theory. Zhu and Wang dealt with the problem of tracking an adversarial target using a vector field . Lawrence et al. also designed a vector field for a circular orbit  . "
Conference Paper: Vector field guidance for path following and arrival angle control[Show abstract] [Hide abstract]
ABSTRACT: This paper focuses on unmanned aircraft guidance laws for a straight path and a circular orbit following using the vector field approach. The vector fields introduced in this paper can be applied to not only path following, but also other purposes such as arrival position, angle, and time control. Therefore, they could be applied to various missions providing advantages over other previous vector fields. Stability and performance of the path following has been proved and analyzed using the classical control theory. And simulation using a six degrees-of-freedom aircraft model shows that these guidance laws are effective for the missions even under the existence of wind disturbance.Unmanned Aircraft Systems (ICUAS), 2014 International Conference on; 05/2014
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- "Dobrokhodov et al.  proposed an object tracking system for unmanned small aircrafts. Zhu and Wang  used a bang-bang heading rate controller to achieve circular tracking around the target. Wang et al.  presented a compound framework for moving target detection, recognition and tracking based on different altitude UAV captured videos. "
ABSTRACT: Fast and accurate visual tracking of ground buildings can provide unmanned aerial vehicles (UAVs) with rich perceptual information, which is very important for target recognition, navigation and system control. However, when an UAV moves fast, both background and buildings in visual scenes change relatively and rapidly. Consequently, there are no constant features for objects' appearance, which poses great challenges for visual tracking of buildings. In this paper, we first build an image manifold of buildings, which can encode the continuous variation of appearance. We then propose an efficient approach to learn this manifold and obtain more robust feature extraction results. By using a simple tracking framework, we successfully apply the extracted low-dimensional features to real-time building tracking. Experimental results demonstrate the effectiveness of the proposed method.05/2014; 756-759. DOI:10.2991/iccia.2012.240
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ABSTRACT: This paper presents strategies for standoff target tracking by a team of unmanned aircrafts using vector field. Many methods to the vector field approach were investigated in other papers, but a modified vector field is introduced to obtain new interesting characteristics in this paper. The modified vector field satisfies more constraints. We introduce two guidance modes to track a target: one is a capturing mode and the other one is a loitering mode. In the capturing mode, aircrafts can arrive at desired positions and time, i.e., unmanned aircrafts can capture a target simultaneously from all sides. After a target is captured, the guidance mode is changed to the loitering mode. Then the relative spacing among aircrafts is controlled by a standoff distance command and a speed command. Hence, they track a target with a desired formation.Journal of Intelligent and Robotic Systems 01/2013; 69(1-4). DOI:10.1007/s10846-012-9765-7 · 0.81 Impact Factor