Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Design and Fabrication of Unmanned Aerial Vehicle

ABSTRACT: This paper summarizes current work on theoretical and experimental cooperative tracking of moving targets by a team of UAVs. The Institution Group is leading a diverse group of researchers to develop building block foundations for cooperative tracking. The building block algorithms have been maturing through the partners, and the team led by Institutions is now pulling the technologies together for demonstration and commercialization. The work reported here focuses on cooperative tracking using multiple UAVs, with the ability for one operator to control many UAVs which are tasked to 1) provide autonomous tracking of moving and evading targets, and 2) report to a centralized database, position history, and velocity vector of the target being tracked. Flock guidance algorithms have been developed and simulated to enable a flock of UAVs to track an evading vehicle. Algorithms have been demonstrated in simulation that dynamically allocate tasks and compute near-optimal paths in real-time; minimize the probability that vehicles are destroyed due to collision or damage from threat; and accommodate moving targets, timeon-targets, and sequencing, as well as the effects of weather and terrain. Additionally Relocation estimation algorithms and software have been developed which exchange information among vehicles, process the information robustly and in real time, and have demonstrated that the joint accuracy is improved. Work has also focused on accurate probabilistic analysis of the estimates, especially considering variations across multiple vehicle sets of Scan Eagle UAVs