Recently, Android-based smartphones have been enabled to acquire the GNSS chipset receiver signals (the so called raw data), which makes it possible for users to develop their own positioning algorithms in addition to the solution produced by the chipset itself. In a broader scope, it allows programmers to integrate GNSS measurements in other estimation algorithms using complementary sources of information (ultrasonic signals, wifi detections, inertial motion data, map-matching, etc). However, information about GNSS measurements is complex and scattered in a number of sources, many of which are not particularly friendly for beginners, thus driving away potential users of this system. This talk will describe a step-by-step methodology which will permit the attendants to acquire and process their own GNSS signals, and produce estimates of the location or trajectory of the smartphone
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Fernando Alvarez is here as a member of the PhD committe of Vincent Thio who will defend his thesis on 13.5 entitled "Sensor fusion for indoor positioning and navigation systems". His trial lecture's title is "Future trends in principles, sensors, and machine learning for indoor positioning" https://www.mn.uio.no/fysikk/english/research/news-and-events/events/disputations/2022/Vincent_Thio
His work has been in the project "Infrastructure-less indoor positioning in 3D" https://www.mn.uio.no/ifi/forskning/prosjekter/iip3/index.html
Prof. Alvarez' homepage is: https://giss.unex.es/testimonial/fernando-javier-alvarez-franco-en/