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Title:FILTER Z DELCI ZA LOKALIZACIJO V BREZŽIČNIH SENZORSKIH OMREŽJIH
Authors:ID Svečko, Janja (Author)
ID Gleich, Dušan (Mentor) More about this mentor... New window
ID Kotnik, Bojan (Comentor)
Files:.pdf DR_Svecko_Janja_2012.pdf (5,32 MB)
MD5: 3136AD91CC5C95B8550E53513E7F3854
PID: 20.500.12556/dkum/3d82523a-6a6a-41dc-a961-5c3e9a240ff2
 
Language:Slovenian
Work type:Dissertation
Typology:2.08 - Doctoral Dissertation
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Doktorska disertacija predstavlja algoritem za določanje razdalje med slepim vozliščem in referenčnim vozliščem v brezžičnem senzorskem omrežju (ang. Wireless Sensor Network – WSN) z zajemanjem meritev indikatorja moči sprejetega signala (ang. Received Signal Strength Indicator – RSSI) na antenskem sklopu sprejemnika. Za ocenjevanje razdalj smo v doktorski disertaciji uporabili Bayesovo sklepanje in filter z delci (ang. particle filter). Z Bayesovim sklepanjem prvega reda in s predhodno izbranim modelom širjenja signala (log-normalni model ali odbojni model) smo določili razdaljo iz zajetih meritev RSSI. Apriorno verjetnost v Bayesovem sklepanju smo modelirali z Gauss-Markovimi naključnimi polji (ang. Gauss-Markov Random Field – GMRF), za opis verjetja pa je bila uporabljena Gamma porazdelitvena funkcija. Ocena razdalje je izvedena s cenilko največje posteriorne verjetnosti (ang. Maximum a posterior – MAP). Bayesovo sklepanje drugega reda, pri katerem smo vrednotili maksimirane robne porazdelitve, smo uporabili za določitev najboljših parametrov apriorne verjetnosti in stopnjo modela oziroma števila anten antenskega sklopa. Za nadaljnjo oceno razdalje smo uporabili filter z delci z metodo prevzorčenja (ang. Sequential Importance Resampling – SIR). Znotraj filtra smo za postopek tipanja uporabili Gaussovo porazdelitveno funkcijo in za posodobitev uteži primerjali med uporabo Gamma porazdelitvene funkcije in Gaussove funkcije. Eksperimentalni rezultati v doktorski nalogi, ki zajemajo realne meritve RSSI-jev in ocenjene razdalje z Bayesovim sklepanjem in filtra z delci, nam kažejo, da je možno oceniti razdaljo med slepim in referenčnim vozliščem 0,03 m natančno. Pri tem je natančnost metode odvisna od samega prostora in odbojev v njem ter od uporabljenih modelov in strojne opreme. Natančnost oziroma napaka je podana kot absolutna vrednost razlike dejanske in ocenjene razdalje.
Keywords:filter z delci, indikator moči sprejetega signala, brezžična senzorska omrežja, več anten
Place of publishing:[Maribor
Publisher:J. Svečko
Year of publishing:2012
PID:20.500.12556/DKUM-38918 New window
UDC:681.586:528.021(043.2)
COBISS.SI-ID:264239104 New window
NUK URN:URN:SI:UM:DK:OPF0G9WT
Publication date in DKUM:20.11.2012
Views:2711
Downloads:244
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
SVEČKO, Janja, 2012, FILTER Z DELCI ZA LOKALIZACIJO V BREZŽIČNIH SENZORSKIH OMREŽJIH [online]. Doctoral dissertation. Maribor : J. Svečko. [Accessed 7 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=38918
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Secondary language

Language:English
Title:PARTICLE FILTER FOR LOCALIZATION IN WIRELESS SENSOR NETWORKS
Abstract:This dissertation presents an algorithm for distance determination between a tag node and an anchor node in the Wireless Sensor Network – WSN, with multiple antennas using the Received Signal Strength Indicator – RSSI. Bayesian inference and particle filter are used for distance estimation. With the first order Bayesian inference we determined the distance from RSSI readings, using a preselected radio propagation model (Log-normal or Ground reflection model). The prior within Bayesian inference is modeled using Gauss-Markov Random Field and the likelihood is presented with the Gamma probability density function. Distance estimation is done with Maximum a posterior (MAP) estimation. The second order Bayesian inference was used to estimate the best parameters of the priori and model order (number of antennas), which was done with evidence maximization evaluation. For further distance estimation, we used particle filter with the Sequential Importance Resampling - SIR algorithm. Gaussian probability density function was used to process importance sampling and a comparison between Gamma and Gaussian probability density function was made for importance weights update. Experimental results of the dissertation, which include real RSSI readings and the estimated distances of Bayesian inference and particle filter, show that it is possible to estimate the distance between the tag node and anchor node with 0,03 m accuracy. The accuracy of the method depends on the space itself and the reflections in it, and from the used models and hardware. Accuracy (distance error) is defined as an absolute value of the difference between the actual and estimated distance.
Keywords:particle filter, localization, received signal strength indicator, wireless sensor network, multiple antennas


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