A personal inertial-navigation system based on multiple distributed, nine-degrees-of-freedom, inertial measurement units

The use of inertial-measurement units (IMUs) for personal navigation is investigated in this thesis. IMUs lack a position-finding algorithm that optimally blends sensor data to achieve high accuracy in a GPS-denied environment. In this research, software and a methodology for tracking position using body-mounted IMUs, building on a gait-phase detection algorithm and quaternion-based complementary filter developed at the Naval Postgraduate School, is developed. The performance of a consumer-grade nine-degrees-of-freedom IMU is characterized and alternative sensor placements evaluated to determine optimal mounting location or locations. Measurements were fused from gyroscope, accelerometer, and magnetometer sensors to create a single, virtual IMU. In addition, measurements from a distributed system of IMUs, as well as multiple co-located IMUs, were averaged to find performance enhancements. Software was developed to streamline and integrate position solutions into a larger network of capabilities. Results show that the foot is the optimal mounting location, and other placements degrade performance. Averaging measurements from multiple IMUs at one location improves performance but with diminishing returns as the number of IMUs increase. We recommend that multiple IMUs be printed on the same MEMS circuit board to achieve accuracy by fusing the measurements of co-located sensors.

Department dc:contributor.department

Electrical and Computer Engineering (ECE)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2016

Author dc:creator

• Johnson, Cole C.

Advisor dc:contributor.advisor

• Yun, Xiaoping

Statement dc:rights

• This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.

Handle dc:identifier.uri

https://hdl.handle.net/10945/51727

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/51727