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A Multi-Target Graph-Constrained HMM Localisation Approach using Sparse Wi-Fi Sensor Data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesisAlternative title
Graf-baserad HMM Lokalisering med Wi-Fi Sensordata av Gångtrafikanter (Swedish)
Abstract [en]

This thesis explored the possibilities of using a Hidden Markov Model approach for multi-target localisation in an urban environment, with observations generated from Wi-Fi sensors. The area is modelled as a network of nodes and arcs, where the arcs represent sidewalks in the area and constitutes the hidden states in the model. The output of the model is the expected amount of people at each road segment throughout the day. In addition to this, two methods for analyzing the impact of events in the area are proposed. The first method is based on a time series analysis, and the second one is based on the updated transition matrix using the Baum-Welch algorithm. Both methods reveal which road segments are most heavily affected by a surge of traffic in the area, as well as potential bottleneck areas where congestion is likely to have occurred.

Abstract [sv]

I det här examensarbetet har lokalisering av gångtrafikanter med hjälp av Hidden Markov Models utförts. Lokaliseringen är byggd på data från Wi-Fi sensorer i ett område i Stockholm. Området är modellerat som ett graf-baserat nätverk där linjerna mellan noderna representerar möjliga vägar för en person att befinna sig på. Resultatet för varje individ är aggregerat för att visa förväntat antal personer på varje segment över en hel dag. Två metoder för att analysera hur event påverkar området introduceras och beskrivs. Den första är baserad på tidsserieanalys och den andra är en maskinlärningsmetod som bygger på Baum-Welch algoritmen. Båda metoderna visar vilka segment som drabbas mest av en snabb ökning av trafik i området och var trängsel är troligt att förekomma.

Place, publisher, year, edition, pages
2018.
Series
TRITA-SCI-GRU ; 2018:287
Keywords [en]
Hidden Markov Model, Localisation, Wi-Fi Sensors, Forward-Backward Algorithm, Baum-Welch Algorithm, Machine Learning, Time Series Analysis.
Keywords [sv]
Hidden Markov Model, Lokalisering, Wi-Fi Sensorer, Forward-Backward Algoritm, Baum-Welch Algoritm, Maskinlärning, Tidsserieanalys.
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-231090OAI: oai:DiVA.org:kth-231090DiVA, id: diva2:1222265
External cooperation
IBM
Subject / course
Optimization and Systems Theory
Educational program
Master of Science - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2018-06-21 Created: 2018-06-21 Last updated: 2018-06-21Bibliographically approved

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