The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Rakesh Nagi - One of the best experts on this subject based on the ideXlab platform.
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controlling a fleet of unmanned aerial vehicles to collect uncertain information in a Threat Environment
Operations Research, 2017Co-Authors: Yan Xia, Rajan Batta, Rakesh NagiAbstract:Unmanned aerial vehicles (UAVs) have been proved to be successful and efficient for information collection in a modern battlefield, especially in areas that are considered to be dangerous for human pilots. Currently, a UAV is remotely controlled by a ground station through frequent data communications, which make the current system vulnerable in a Threat Environment. We propose a decentralized control strategy while requiring UAVs to maintain radio silence during the entire mission. The strategy is analyzed based on a scenario where a fleet of vehicles is assigned to search and collect uncertain information in a set of regions within a given mission time. We demonstrate that a region-sharing strategy is beneficial even when there is no extra reward gained from additional information collection. Implementing a region-sharing strategy requires solving a decentralized time allocation problem, which is computationally intractable. To overcome this, an approximate formulation is developed under an independence...
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clustering sensors in wireless ad hoc networks operating in a Threat Environment
Operations Research, 2005Co-Authors: Dipesh Patel, Rajan Batta, Rakesh NagiAbstract:Sensors in a data fusion Environment over hostile territory are geographically dispersed and change location with time. To collect and process data from these sensors, an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile Environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed-integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the expected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver, and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed.
Yuesheng Zhu - One of the best experts on this subject based on the ideXlab platform.
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Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform
ISPRS International Journal of Geo-Information, 2016Co-Authors: Ming Zhang, Mingyuan Hu, Chen Su, Yuan Liu, Yuesheng ZhuAbstract:Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a Threat Environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common Threat model is constructed for Threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic Threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.
Yueqian Liang - One of the best experts on this subject based on the ideXlab platform.
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online path planning of autonomous uavs for bearing only standoff multi target following in Threat Environment
IEEE Access, 2018Co-Authors: Hao Jiang, Yueqian LiangAbstract:The problem of steering multiple fixed-wing autonomous unmanned aerial vehicles (UAVs) to follow the multiple noncooperative and high agile surface targets at a specified standoff distance in a Threat Environment is studied. Two kinds of Threats and missed detection of bearing-only sensors are considered. The average nonlinear least-square estimation method is proposed to acquire the position of the targets using noisy measurements, and then, the trajectories of the targets are approximated by the quadratic functions of time. Dummy targets of the same number as UAVs are created according to the task allocation result to achieve the optimal geometric configuration of the multiple cooperative UAVs. And a case-based guidance method is established to finally accomplish the multi-target standoff following mission on-line. Simulation experiments are given to assess the proposed method.
Rajan Batta - One of the best experts on this subject based on the ideXlab platform.
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controlling a fleet of unmanned aerial vehicles to collect uncertain information in a Threat Environment
Operations Research, 2017Co-Authors: Yan Xia, Rajan Batta, Rakesh NagiAbstract:Unmanned aerial vehicles (UAVs) have been proved to be successful and efficient for information collection in a modern battlefield, especially in areas that are considered to be dangerous for human pilots. Currently, a UAV is remotely controlled by a ground station through frequent data communications, which make the current system vulnerable in a Threat Environment. We propose a decentralized control strategy while requiring UAVs to maintain radio silence during the entire mission. The strategy is analyzed based on a scenario where a fleet of vehicles is assigned to search and collect uncertain information in a set of regions within a given mission time. We demonstrate that a region-sharing strategy is beneficial even when there is no extra reward gained from additional information collection. Implementing a region-sharing strategy requires solving a decentralized time allocation problem, which is computationally intractable. To overcome this, an approximate formulation is developed under an independence...
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clustering sensors in wireless ad hoc networks operating in a Threat Environment
Operations Research, 2005Co-Authors: Dipesh Patel, Rajan Batta, Rakesh NagiAbstract:Sensors in a data fusion Environment over hostile territory are geographically dispersed and change location with time. To collect and process data from these sensors, an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile Environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed-integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the expected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver, and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed.
Ming Zhang - One of the best experts on this subject based on the ideXlab platform.
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Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform
ISPRS International Journal of Geo-Information, 2016Co-Authors: Ming Zhang, Mingyuan Hu, Chen Su, Yuan Liu, Yuesheng ZhuAbstract:Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a Threat Environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common Threat model is constructed for Threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic Threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.