Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network
Author:
Lind Artjom1ORCID, Wu Shan1ORCID, Hadachi Amnir1ORCID
Affiliation:
1. Intelligent Transportation Systems Lab, Institute of Computer Science, University of Tartu, Narva Mnt 18, 51009 Tartu, Estonia
Abstract
Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter’s inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells’ coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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