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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 Micro-
Aerial 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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