Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the on-board communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in Least Square (LS) algorithms which are widely used in RSS based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter for distance estimation. Furthermore, the constantly changing path loss factor during UAV flight can also affect the accuracy of estimation. In order to overcome this challenge, we present an adaptive algorithm to estimate the path loss factor. Given the position and velocity information, if a collision is detected we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.

In our view, customers in the future are likely to obtain their services from coalitions of service providers. These coalitions can be described as virtual organisations (VOs); they are group of service providers that form relationships to service customers' demands on an ad-hoc basis. For a VO to be effective, it must be reliable and scalable, and realistically, it must be created and maintained in a dynamic, open and competitive environment. The CONOISE-G project has focused on resolving the technology challenges that emerged from these requirements. Specifically, CONOISE-G provides mechanisms to assure effective operation of VOs in the face of failure, unexpected events and changing requirements in dynamic, open and competitive environment. In this paper, we describe the CONOISE-G system; motivated by a scenario based on mobile service provision; outline its use in the context of VO formation and perturbation and review current efforts to progress the work to deal with unreliable information sources. 1

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSOR/MONITOR'S ACRONYM(S)AFRL/AFOSR/IOE (EOARD) SPONSOR/MONITOR'S REPORT NUMBER(S) AFRL-AFOSR-UK-TR-2014-000612. DISTRIBUTION/AVAILABILITY STATEMENT Distribution A: Approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThe objective of the project reported here was to develop, implement and evaluate a model of the probability of detection of moving objects in Wide Area Motion Imagery (WAMI) that incorporates the effects of the target, the platform, and the environment. Developing situation awareness is vital for almost any kind of military operation. Through understanding the state and nature of the environment, military personnel can plan and respond accordingly. Situation awareness is often treated as the problem of knowing where all the potential targets are. Through knowing the locations of these targets, threats can be identified and countered. Another important source of awareness is to understand where targets cannot be. Regions that are free of targets can be used to constrain where targets might be. To meet these needs, Wide Area Surveillance (WAS) systems have been developed that are able to sense large swaths of an environment simultaneously and at high resolution. However, the next key challenge is to automatically analyze this image data to, for example, track the locations of targets and identify potential anomalous behavior. This report begins to explore how the output from a WAS system can be used by a state-of-the-art multi-target tracker. In particular, we considered how the output of the image processing and matching algorithms used in the Likelihood of Features Tracker (LoFT) could be combined with a Probabilistic Hypothesis Density (PHD) Filter. Using machine learning techniques, we have developed a formalism and algorithms to automatically predict how the visual appearance of a vehicle can change over time. Using this prediction model, we are then able to automatically threshold and detect potential candidate vehicle locations, and assess both probability of detection and the probability of clutter. SUBJECT TERMS Executive SummaryDeveloping situation awareness is vital for almost any kind of military operation. Through understanding the state and nature of the environment, military personnel can plan and respond accordingly. Situation awareness is often treated as the problem of knowing where all the potential targets are. Through knowing the locations of these targets, threats can be identified and countered. To meet these needs, Wide Area Su...