ION GNSS+, Portland, Oregon, 12-16 September 2016 Situational Awareness for Tactical Applications Ruotsalainen L., Guinness R., Gröhn S., Chen L., Kirkko-Jaakkola M., Kuusniemi H., Department of Navigation and Positioning, Finnish Geospatial Research Institute, Finland BIOGRAPHIES Dr. Laura Ruotsalainen is a Research Manager and Deputy Director of the Department of Navigation and Positioning at the Finnish Geospatial Research Institute (FGI), Finland, where she leads the research group on Sensors and indoor navigation. She received her doctoral degree in 2013 from the Department of Pervasive Computing, Tampere University of Technology (TUT), Finland. Her doctoral studies were partly conducted at the Department of Geomatics Engineering at the University of Calgary, Canada. Her doctoral research was focused on vision-aided seamless indoor/outdoor pedestrian navigation. Her current research interests cover adaptive integration of sensors and radio positioning means for robust navigation, situational awareness and Global Navigation Satellite systems (GNSS) interference mitigation. Robert Guinness is a Research Manager at the Finnish Geospatial Research Institute, where he leads the research group on Intelligent Mobility and Geospatial Computing. He received his M.Sc. in 2006 from the International Space University, and he is currently a doctoral candidate at Tampere University of Technology, Finland. His research interests include context awareness for navigation applications, machine learning, and privacy- preserving location technologies. Simo Gröhn is a research scientist and a PhD student at the Department of Navigation and Positioning at FGI since 2015. The topic of his thesis is the integration of vision based positioning and sensor data for indoor positioning. His research focus consists of vision-aiding in positioning, sensor error modelling and integration algorithms. Mr. Gröhn holds a Master of Science (tech) degree and before joining FGI has worked in research and development projects related to optical sensors in industry. Dr. Martti Kirkko-Jaakkola is a Senior Research Scientist at the Finnish Geospatial Research Institute. He received his M.Sc. and D.Sc. (Tech.) degrees from Tampere University of Technology, Finland, in 2008 and 2013, respectively. His research interests include various precise GNSS positioning and inertial navigation applications using low-cost equipment. Dr. Liang Chen received the M.Sc. degree in control theory and control engineering from Jiangsu University, China, in 2004, and the Ph.D. degree in signal and information processing from Southeast University, China, in 2009. He was a Post-Doctoral Research Scientist with the Department of Mathematics, Tampere University of Technology, Finland, from 2009 to 2011. He is a Senior Research Scientist with the Department of Navigation and Positioning, Finnish Geospatial Research Institute, Finland. His research interests include statistical signal processing for positioning, wireless positioning using signals of opportunity, and sensor fusion algorithms for indoor positioning. Prof. Heidi Kuusniemi is the Director at the Department of Navigation and Positioning at FGI. She is also an Adjunct Professor at TUT, Finland, and Aalto University, Finland, where she also is a lecturer on GNSS technologies. She is the President of the Nordic Institute of Navigation. She received her M.Sc. and Doctor of Technology degrees from TUT in 2002 and 2005, respectively. Her doctoral studies on reliability monitoring in personal satellite navigation were partly conducted at the Department of Geomatics Engineering at the University of Calgary, Canada. Kuusniemi's research interests cover various aspects of GNSS and sensor fusion. ABSTRACT This paper presents the results of detecting various motion states, essential for infrastructure-free tactical situational awareness, using Machine Learning (ML) for motion classification. We investigated if the use of multiple IMUs will bring additional information and improve the sensing of motion contexts. Namely, we used three different Inertial Measurement Units (IMUs) with the first attached to the user’s torso, the second to the foot, and the third to the helmet. We also studied the best combination of sensors, measurements, and features used for detecting the contexts and for obtaining an accurate position solution. The full set of sensors studied included the three IMUs, a camera, a barometer, and an ultrasonic sensor. The method was tested with data collected in two experiments encompassing various motion patterns. Results showed that all sensors bring added value to the motion detection and that the motions having multiple instances in the data, even the ones considered as difficult to be identified like crawling, could be accurately classified. Also, the selection of features used for the classification process is discussed and evaluated as well as few different ML algorithms for classification. This research is a significant step towards infrastructure-free situational awareness for tactical applications.