KEYWORDS: Detection and tracking algorithms, Hyperspectral imaging, Sensors, Reflectivity, Signal to noise ratio, Image classification, Signal detection, Target detection, Transform theory, Heads up displays
This paper describes a novel approach for the detection and classification of man-made objects using discriminating
features derived from higher-order spectra (HOS), defined in terms of higher-order moments of hyperspectral-signals.
Many existing hyperspectral analysis techniques are based on linearity assumptions. However, recent research suggests
that significant nonlinearity arises due to multipath scatter, as well as spatially varying atmospheric water vapor
concentrations. Higher-order spectra characterize subtle complex nonlinear dependencies in spectral phenomenology of
objects in hyperspectral data and are insensitive to additive Gaussian noise. By exploiting these HOS properties, we have
devised a robust method for classifying man-made objects from hyerspectral signatures despite the presence of strong
background noise, confusers with spectrally similar signatures and variable signal-to-noise ratios. We tested
classification performance hyperspectral imagery collected from several different sensor platforms and compared our
algorithm with conventional classifiers based on linear models. Our experimental results demonstrate that our HOS
algorithm produces significant reductions in false alarms. Furthermore, when HOS-based features were combined with
standard features derived from spectral properties, the overall classification accuracy is substantially improved.
Unmanned Air Vehicles (UAVs) are expected to dramatically alter the way future battles are fought. Autonomous
collaborative operation of teams of UAVs is a key enabler for efficient and effective deployment of large numbers of
UAVs under the U. S. Army's vision for Force Transformation. Autonomous Collaborative Mission Systems (ACMS)
is an extensible architecture and collaborative behavior planning approach to achieve multi-UAV autonomous
collaborative capability. Under this architecture, a rich set of autonomous collaborative behaviors can be developed to
accomplish a wide range of missions. In this article, we present our simulation results in applying various autonomous
collaborative behaviors developed in the ACMS to an integrated convoy protection scenario using a heterogeneous team
of UAVs.
UAVs are critical to the U. S. Army's Force Transformation. Large numbers of UAVs will be employed per Future
Combat System (FCS) Unit of Action (UoA). To relieve the burden of controlling and coordinating multiple UAVs in a
given UoA, UAVs must operate autonomously and collaboratively while engaging in RSTA and other missions.
Rockwell Scientific is developing Autonomous Collaborative Mission Systems (ACMS), an extensible architecture and
behavior planning/collaboration approach, to enable groups of UAVs to operate autonomously in a collaborative
environment. The architecture is modular, and the modules may be run in different locations/platforms to accommodate
the constraints of available hardware, processing resources and mission needs. The modules and uniform interfaces
provide a consistent and platform-independent baseline mission collaboration mechanism and signaling protocol across
different platforms. Further, the modular design allows for the flexible and convenient extension to new autonomous
collaborative behaviors to the ACMS. In this article, we first discuss our observations in implementing autonomous
collaborative behaviors in general and under ACMS. Second, we present the results of our implementation of two such
behaviors in the ACMS as examples.
We illustrate an approach for planning UAV sensing actions in urban or constrained domains. We plan and optimize a collection strategy for a target of interest using Design Sheet, a numeric/symbolic algebraic constraint propagation package. Once a set of sensing plans have been developed, we use a probabilistic roadmap planning algorithm to plan a route for a fixed wing UAV through urban terrain to collect that information. This planner has several novel features to improve performance for urban domains.
UAVs are a key element of the U. S. Army's vision for Force Transformation, and are expected to be employed in large numbers per FCS Unit of Action (UoA). This necessitates a multi-UAV level of autonomous collaboration behavior capability that meets RSTA and other mission needs of FCS UoAs. Autonomous Collaborative Mission Systems (ACMS) is an extensible architecture and behavior planning / collaborative approach to achieve this level of capability. The architecture is modular and the modules may be run in different locations/platforms to accommodate the constraints of available hardware, processing resources and mission needs. The modules and uniform interfaces provide a consistent and platform-independent baseline mission collaboration mechanism and signaling protocol across different platforms. Further, the modular design allows flexible and convenient extension to new autonomous collaborative behaviors to the ACMS through: adding new behavioral templates in the Mission Planner component; adding new components in appropriate ACMS modules to provide new mission specific functionality; adding or modifying constraints or parameters to the existing components, or any combination of these. We describe the ACMS architecture, its main features on extensibility, and updates on current spiral development status and future plans for simulations in this report.
UAVs are a key element of the Army’s vision for Force Transformation, and are expected to be employed in large numbers per FCS Unit of Action (UoA). This necessitates a multi-UAV level of autonomous collaboration behavior capability that meets RSTA and other mission needs of FCS UoAs. Autonomous Collaborative Mission Systems (ACMS) is a scalable architecture and behavior planning / collaborative approach to achieve this level of capability. The architecture is modular and the modules may be run in different locations/platforms to accommodate the constraints of available hardware, processing resources and mission needs. The Mission Management Module determines the role of member autonomous entities by employing collaboration mechanisms (e.g., market-based, etc.), the individual Entity Management Modules work with the Mission Manager in determining the role and task of the entity, the individual Entity Execution Modules monitor task execution and platform navigation and sensor control, and the World Model Module hosts local and global versions of the environment and the Common Operating Picture (COP). The modules and uniform interfaces provide a consistent and platform-independent baseline mission collaboration mechanism and signaling protocol across different platforms. Further, the modular design allows flexible and convenient addition of new autonomous collaborative behaviors to the ACMS through: adding new behavioral templates in the Mission Planner component, adding new components in appropriate ACMS modules to provide new mission specific functionality, adding or modifying constraints or parameters to the existing components, or any combination of these. We describe the ACMS architecture, its main features, current development status and future plans for simulations in this report.
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