An Enhanced Prototype Personal Inertial Navigation System

Yunqian Ma, Wayne Soehren, Wes Hawkinson, Justin Syrstad

Abstract:	Numerous pedestrian navigation applications have been proposed, including localization for a coordinating group of firefighters, first responders, or soldiers. A challenging scenario arises in GPS-denied environments when the team operates inside a building, in the urban canyon, underground, in foliage, or under the forest canopy. As an industry leader in navigation technologies, Honeywell has been researching and developing personal navigation equipment. For instance, the DRM (TM) 4000 is a dead-reckoning system based on the fusion of IMU and compass information. This system is low-cost and capable of GPS-denied navigation in the absence of large magnetic disturbances. Moreover, Honeywell has been developing advanced techniques for aiding personal navigation by estimating displacements using gait models. This paper presents an enhanced Personal Inertial Navigation System (ePINS) solution to the problem of personnel location in GPS-denied environments. The technical approach is based on a strapdown navigation solution maintained using a mid grade IMU and wavelet based motion classification algorithms. This is a follow up of the DARPA SUO SAS and iPINS programs, and is currently being applied to the US Department of Homeland Security (DHS) Geo-spatial Location Accountability and Navigation System for Emergency Responders (GLANSER) and the DARPA Robust Surface Navigation (RSN) programs. The ePINS device can achieve closed path performance of less than 2% distance traveled for both indoor and outdoor environments. The ePINS consists of a MEMS Inertial Measurement Unit (three gyroscopes and three accelerometers), a barometric pressure sensor, and a GPS receiver. The ePINS algorithms combine inertial navigation, barometric altitude, and wavelet motion classification methods to minimize the errors inherent in sensor-based systems, while achieving the system performance, size, and cost objectives. The motion classification uses a wavelet descriptor based classification on the IMU data and is capable of identifying typical human motion modes, including walking, running, walking upstairs, walking downstairs, stopping, crawling, etc. This process is generic enough to allow new motion types to be added to the classification scheme easily. The step-length model is also described, wherein the step length of each motion as a function of frequency and biometric information of the person (e.g., height). After that, we use an extended Kalman filter update using the step length calculated from the trained model to correct the navigation solution. A heading initialization process is also described, which involves walking a circular path in the presence of GPS.
Published in:	Proceedings of the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2012) September 17 - 21, 2012 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	1186 - 1194
Cite this article:	Ma, Yunqian, Soehren, Wayne, Hawkinson, Wes, Syrstad, Justin, "An Enhanced Prototype Personal Inertial Navigation System," Proceedings of the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2012), Nashville, TN, September 2012, pp. 1186-1194.
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