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

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.

Tracking of moving human in different overlapping cameras using Kalman filter optimized

“…In many other papers, histogram matching like color histogram has been used as a signature [37,38]. A hidden Markov model is used in a recent study Lind et al [39], to Yousefi et al EURASIP Journal on Advances in Signal Processing (2023) 2023:114 simulate the edges of the tracked region. This model looks at several picture clues and uses a joint probability data association filter to figure out how likely it is that the picture will change.…”