Modeling decision uncertainties in total situation awareness using cloud computation theory

Zein-Sabatto, Saleh; Khoshnaw, Abdulqadir; Shetty, Sachin; Malkani, Mohan; Mitra, Atindra K.

doi:10.1117/12.886968

Cited by 2 publications

(1 citation statement)

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“…Additional details with regard to potential sources and mitigation techniques for these type of relatively large platform-dependent errors will be reported in future correspondence. Also, additional interesting concepts for potential future research include the investigation of passive and active position-adaptive radar/RF systems concepts that incorporate multiple/distributed wireless/mobile-integrated EO and RF sensors for intelligent scattering-based position-adaptive multiplatform control via design and implementation with computational resources that incorporate a number of recent advances in mobile and distributed computing architectures [16] [17] [18].…”

Section: Results and Scope For Future Researchmentioning

confidence: 99%

PADF RF localization experiments with multi-agent caged-MAV platforms

et al. 2011

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This paper provides a summary of preliminary RF direction finding results generated within an AFOSR funded testbed facility recently developed at Louisiana Tech University. This facility, denoted as the Louisiana Tech University MicroAerial Vehicle/Wireless Sensor Network (MAVSeN) Laboratory, has recently acquired a number of state-of-the-art MAV platforms that enable us to analyze, design, and test some of our recent results in the area of multiplatform position-adaptive direction finding (PADF) [1] [2] for localization of RF emitters in challenging embedded multipath environments. Discussions within the segmented sections of this paper include a description of the MAVSeN Laboratory and the preliminary results from the implementation of mobile platforms with the PADF algorithm. This novel approach to multi-platform RF direction finding is based on the investigation of iterative path-loss based (i.e. path loss exponent) metrics estimates that are measured across multiple platforms in order to develop a control law that robotically/intelligently positionally adapt (i.e. self-adjust) the location of each distributed/cooperative platform. The body of this paper provides a summary of our recent results on PADF and includes a discussion on state-of-the-art Sensor Mote Technologies as applied towards the development of sensor-integrated caged-MAV platform for PADF applications. Also, a discussion of recent experimental results that incorporate sample approaches to real-time singleplatform data pruning is included as part of a discussion on potential approaches to refining a basic PADF technique in order to integrate and perform distributed self-sensitivity and self-consistency analysis as part of a PADF technique with distributed robotic/intelligent features. These techniques are extracted in analytical form from a parallel study denoted as "PADF RF Localization Criteria for Multi-Model Scattering Environments". The focus here is on developing and reporting specific approaches to self-sensitivity and self-consistency within this experimental PADF framework via the exploitation of specific single-agent caged-MAV trajectories that are unique to this experiment set.

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Section: Results and Scope For Future Researchmentioning

confidence: 99%