Multi-sensor Bayesian Estimation Interior Positioning for Stationary and Mobile Structures

B. Tanju, S. Sarkani, T. Mazzuchi

Abstract:	Indoor positioning has been a burgeoning technology which has been researched and, to date, sparsely implemented using a variety of sensors. This paper addresses theoretical and practical considerations of a continuous outboard inboard positioning system with emphasis on its future implementation aboard Navy or commercial ships. The need for positioning systems aboard platforms, such as aircraft carriers, to reduce onboard manning, for damage control and maintenance, as well as for general personnel and high value asset tracking, is a Naval research initiative. The paper evaluates, via analysis and demonstration, the utility of very low cost MEMS (gyroscopes and accelerometers) sensors, along with GPS, to augment the WiFi. This unique combination of sensors, which has been implemented in hardware and modeled in software, is used to explore the tradeoffs among positioning accuracy, availability and complexity. The first test site for the experimental validation of the prototype system hardware and software was chosen as the George Washington University Ashburn campus in Northern Virginia. This site was chosen based on its extensive WiFi capability as well as it having certain areas with topological and WiFi transmission characteristics resembling that of a ship. A description of the WiFi network’s access points, received signal strength maps, and overall topology is included. The generation of detailed apriori WiFi RSS maps along with the overall test methodology employed, necessary to demonstrate a horizontal performance accuracy goal of two meters and deck level vertical discrimination, are described. As a consequence of the non-linearities involved in the RSS maps and topological constraints, as well as the non- Gaussian nature of some of the noise sources, emphasis has been placed on a novel implementation of a regularized particle filter. This paper considers navigation in both an absolute and relative sense. For most applications, absolute and relative solutions can be straightforwardly related to each other. A complication, however, arises when a user on a mobile platform employs inertial sensors as auxiliary sensors. These sensors inherently measure acceleration and angular rate with respect to inertial space and thus are subject to the total motion of the user. When the total motion of the user includes both a relative motion with respect to the mobile vehicle and a vehicle motion with respect to inertial space, then algorithms must attempt to separate the user´s relative motion with respect to the mobile platform. This topic is analytically addressed in a preliminary fashion and will be the subject of further research. The paper concludes with the plans and schedule for a second prototype demonstration aboard a US Navy ship in the Fall of 2009.
Published in:	Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009) September 22 - 25, 2009 Savannah International Convention Center Savannah, GA
Pages:	564 - 581
Cite this article:	Tanju, B., Sarkani, S., Mazzuchi, T., "Multi-sensor Bayesian Estimation Interior Positioning for Stationary and Mobile Structures," Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009, pp. 564-581.
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