1st International ICST Workshop on Mobile and Location-based Business Applications

Research Article

Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory

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  • @INPROCEEDINGS{10.1007/978-3-642-17758-3_24,
        author={Muhammad Alam and Mazliham Suud and Patrice Boursier and Shahrulniza Musa and Jawahir Yusuf},
        title={Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory},
        proceedings={1st International ICST Workshop on Mobile and Location-based Business Applications},
        proceedings_a={MAPPS},
        year={2012},
        month={10},
        keywords={Kalman filter Bayesian decision theory location estimation},
        doi={10.1007/978-3-642-17758-3_24}
    }
    
  • Muhammad Alam
    Mazliham Suud
    Patrice Boursier
    Shahrulniza Musa
    Jawahir Yusuf
    Year: 2012
    Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory
    MAPPS
    Springer
    DOI: 10.1007/978-3-642-17758-3_24
Muhammad Alam,*, Mazliham Suud1,*, Patrice Boursier2,*, Shahrulniza Musa1,*, Jawahir Yusuf,*
  • 1: Centre for Research and Postgraduate Studies (CRPGS) & UniKL MIIT Jln Sultan Ismail
  • 2: Université de La Rochelle
*Contact email: muhammad.unikl@gmail.com, mazliham@unikl.edu.my, patrice.boursier@univ-lr.fr, shahrulniza@miit.unikl.edu.my, jawahir@miit.unikl.edu.my

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.