Y. Cai et al. (Eds.): Mobilware 2010, LNICST 48, pp. 313–325, 2010. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2010 Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory Muhammad Alam 1,2 , Mazliham Muhammad Suud 1 , Patrice Boursier 2 , Shahrulniza Musa 1 , and Jawahir Che Mustapha Yusuf 1,2 1 Centre for Research and Postgraduate Studies (CRPGS) & UniKL MIIT Jln Sultan Ismail, 50250, Kuala Lumpur muhammad.unikl@gmail.com, mazliham@unikl.edu.my, {shahrulniza,jawahir}@miit.unikl.edu.my 2 Laboratoire L3i, Université de La Rochelle, 17000 La Rochelle, France patrice.boursier@univ-lr.fr Abstract. The main objective of this research is to apply statistical location estimation techniques in cellular networks in order to calculate the precise location of the mobile node. Current research is focusing on the combination of Kalman filter and the Bayesian decision theory based location estimation. In this research basic four steps of Kalman filter are followed which are Estimation, Filtering, Prediction and Fusion. Estimation is done by using Receive Signal Strength (RSS), Available Signal Strength (ASS) and the Angle of Arrival (AOA). Filtering is done by calculating the average location and variation in values of location. Prediction is done by using the Bayesian decision theory. Fusion is done by combining the variances calculated in filtering step. Finally by combining the prediction and fusion results PCLEA (Predicted and Corrected Location Estimation Algorithm) is established. Timestamp is used for recursive step in kalman filter. The aim of this research is to minimize the dependence on the satellite based location estimation and increase its accuracy, efficiency and reliability. Keywords: Kalman filter, Bayesian decision theory, location estimation. 1 Introduction Location estimation of a mobile user is a very popular research area from past few years. Due to the growth of cellular architecture the mobile users originating calls are also increasing at the same time. It is estimated that more than 50% emergency calls are originated by the mobile phones [1]. Techniques which are used for location estimation are satellite based techniques, geometric techniques, statistical techniques and the mapping techniques [2], [3]. All techniques have different accuracy level, processing time, coverage and the cost. The location of the mobile node can be estimated by the mobile node itself which is known as self positioning. Otherwise it can be calculated by the server with the help of the reference points, which is known as remote positioning or network centric positioning [4]. Two different approaches are used by the researchers, the direct positioning approach and the two step-step