Bayesian Filtering for Dynamic Systems with Applications to Tracking

This M.Sc. thesis intends to evaluate various algorithms based on Bayesian statistical theory and validates with both synthetic data as well as experimental data. The focus is given in comparing the performance of new kind of sequential Monte Carlo filter, called cost reference particle filter, with other Kalman based filters as well as the standard particle filter. Different filtering algorithms based on Kalman filters and those based on sequential Monte Carlo technique are implemented in Matlab. For all linear Gaussian system models, Kalman filter gives the optimal solution. Hence only the cases which do not have linear-Gaussian probabilistic model are analyzed in this thesis. The results of various simulations show that, for those non-linear system models whose probability model can fairly be assumed Gaussian, either Kalman like filters or the sequential Monte Carlo based particle filters can be used. The choice among these filters depends upon various factors such as degree of nonlinearity, order of system state, required accuracy, etc. There is always a tradeoff between the required accuracy and the computational cost. It is found that whenever the probabilistic model of the system cannot be approximated as Gaussian, which is the case in many real world applications like Econometrics, Genetics, etc., the above discussed statistical reference filters degrade in performance. To tackle with this problem, the recently proposed cost reference particle filter is implemented and tested in scenarios where the system model is not Gaussian. The new filter shows good robustness in such scenarios as it does not make any assumption of probabilistic model. The thesis work also includes implementation of the above discussed prediction algorithms into a real world application, where location of a moving robot is tracked using measurements from wireless sensor networks. The flexibility of the cost reference particle filter to adapt to specific applications is explored and is found to perform better than the other filters in tracking of the robot. The results obtained from various experiments show that cost reference particle filter is the best choice whenever there is high uncertainty of the probabilistic model and when these models are not Gaussian. It can also be concluded that,contrary to the general perception, the estimation techniques based on ad-hoc references can actually be more efficient than those based on the usual statistical reference

Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm

Sulautettu ohjelmistototeutus reaaliaikaiseen paikannusjärjestelmään

Asset tracking often necessitates wireless, radio-frequency identification (RFID). In practice, situations often arise where plain inventory operations are not sufficient, and methods to estimate movement trajectory are needed for making reliable observations, classification and report generation. In this thesis, an embedded software application for an industrial, resource-constrained off-the-shelf RFID reader device in the UHF frequency range is designed and implemented. The software is used to configure the reader and its air-interface operations, accumulate read reports and generate events to be reported over network connections. Integrating location estimation methods to the application facilitates the possibility to make deploying middleware RFID solutions more streamlined and robust while reducing network bandwidth requirements. The result of this thesis is a functional embedded software application running on top of an embedded Linux distribution on an ARM processor. The reader software is used commercially in industrial and logistics applications. Non-linear state estimation features are applied, and their performance is evaluated in empirical experiments.Tavaroiden seuranta edellyttää usein langatonta radiotaajuustunnistustekniikkaa (RFID). Käytännön sovelluksissa tulee monesti tilanteita joissa pelkkä inventointi ei riitä, vaan tarvitaan menetelmiä liikeradan estimointiin luotettavien havaintojen ja luokittelun tekemiseksi sekä raporttien generoimiseksi. Tässä työssä on suunniteltu ja toteutettu sulautettu ohjelmistosovellus teolliseen, resursseiltaan rajoitettuun ja kaupallisesti saatavaan UHF-taajuusalueen RFID-lukijalaitteeseen. Ohjelmistoa käytetään lukijalaitteen ja sen ilmarajapinnan toimintojen konfigurointiin, lukutapahtumien keräämiseen ja raporttien lähettämiseen verkkoyhteyksiä pitkin. Paikkatiedon estimointimenetelmien integroiminen ohjelmistoon mahdollistaa välitason RFID-sovellusten toteuttamisen aiempaa suoraviivaisemin ja luotettavammin, vähentäen samalla vaatimuksia tietoverkon kaistanleveydelle. Työn tuloksena on toimiva sulautettu ohjelmistosovellus, jota ajetaan sulautetussa Linux-käyttöjärjestelmässä ARM-arkkitehtuurilla. Lukijaohjelmistoa käytetään kaupallisesti teollisuuden ja logistiikan sovelluskohteissa. Epälineaarisia estimointiominaisuuksia hyödynnetään, ja niiden toimivuutta arvioidaan empiirisin kokein