City busses are operated on roads where the GPS signal is weak, because of the tall buildings surrounding these roads. The localization of city busses, needs to therefore rely on alternate technique in order to improve the accuracy. Recent standardization of inter vehicular communication has made this a readily available tool which can be used for localization. This thesis presents an approach towards localization of a city bus by means of vehicular ad hoc network. The two main components of localization by this approach is the initialization of location estimate component, and the real time location estimation component. In particular, the thesis develops the use of minimum mean square estimation for initialization and an extended Kalman filtering approach for real time location estimation. The localization method is mathematically described, considering the operating scenarios of a city bus. The accuracy of the proposed method is mathematically evaluated. The developed localization method is implemented in a simulation tool kit for inter vehicular communication. Simulation experiments were performed for operating scenarios of city bus. The result of initialization by minimum mean square error is compared to that of initialization by GPS, in-terms of localization accuracy. Different setups of road side units are compared in-terms of accuracy and update interval. The results show that the proposed method is feasible for localization of a city bus. This thesis was carried out in association with Scania AB, Södertälje. / Stadsbussar åker på vägar som är omgivna av byggnader, vilket försämrar stadsbussarnas GPSmottagning. Lokaliseringen av stadsbussar måste därför förlita sig på alternativ teknik för att förbättra noggrannheten. Nyligen standardiserad kommunikation mellan fordon har blivit till ett lättillgängligt verktyg som kan användas för lokalisering. Den här uppsatsen presenterar en strategi för lokalisering av en stadsbuss med hjälp av fordonets ad hoc-nätverk. Huvudkomponenterna för lokalisering är en initialiseringskomponent och realtidslägesuppskattningskomponent. Speciellt utvecklar arbetet användningen av minsta medelkvadratberäkning för initialisering och en utvidgad kalmanfiltreringsmetod för realtidslägesuppskattning. Lokaliseringsmetoden beskrivs matematiskt med tanke på driftsscenarierna för en stadsbuss. Noggrannheten hos den föreslagna metoden utvärderas matematiskt. Den utvecklade lokaliseringsmetoden implementeras i ett simuleringsverktyg för kommunikation mellan fordon. Simuleringsexperiment utfördes för driftsscenarier för stadsbussar. Resultatet av initialisering med minsta medelkvadratberäkning jämförs med initialiseringen med GPS, i termer av lokaliseringsnoggrannhet. Olika inställningar av vägrensenheter jämförs med avseende på noggrannhet och uppdateringsintervall. Resultaten visar att den föreslagna metoden är möjlig för lokalisering av en stadsbuss. Denna arbetet genomfördes i samarbete med Scania AB, Södertälje.

This dissertation mainly consists of three parts. The first part proposes generalized linear minimum mean-square error (GLMMSE) estimation for nonlinear point estimation. The second part proposes a recursive joint decision and estimation (RJDE) algorithm for joint decision and estimation (JDE). The third part analyzes the performance of sequential probability ratio test (SPRT) when the log-likelihood ratios (LLR) are independent but not identically distributed. The linear minimum mean-square error (LMMSE) estimation plays an important role in nonlinear estimation. It searches for the best estimator in the set of all estimators that are linear in the measurement. A GLMMSE estimation framework is proposed in this disser- tation. It employs a vector-valued measurement transform function (MTF) and finds the best estimator among all estimators that are linear in MTF. Several design guidelines for the MTF based on a numerical example were provided. A RJDE algorithm based on a generalized Bayes risk is proposed in this dissertation for dynamic JDE problems. It is computationally efficient for dynamic problems where data are made available sequentially. Further, since existing performance measures for estimation or decision are effective to evaluate JDE algorithms, a joint performance measure is proposed for JDE algorithms for dynamic problems. The RJDE algorithm is demonstrated by applications to joint tracking and classification as well as joint tracking and detection in target tracking. The characteristics and performance of SPRT are characterized by two important functions—operating characteristic (OC) and average sample number (ASN). These two functions have been studied extensively under the assumption of independent and identically distributed (i.i.d.) LLR, which is too stringent for many applications. This dissertation relaxes the requirement of identical distribution. Two inductive equations governing the OC and ASN are developed. Unfortunately, they have non-unique solutions in the general case. They do have unique solutions in two special cases: (a) the LLR sequence converges in distributions and (b) the LLR sequence has periodic distributions. Further, the analysis can be readily extended to evaluate the performance of the truncated SPRT and the cumulative sum test.