New publication - Journal paper: Direct Tracking of a Wireless Transmitter based on Rao-Blackwellized Point Mass Filter

Abstract: In this paper, we address the problem of direct
tracking of a wireless transmitter. That is, the inputs given to
the Bayesian filter are the received baseband signals instead of
pre-computed ranges or angles. We first propose to use the
Rao-Blackwellized Point Mass Filter (RBPMF) to solve such
a tracking problem. As such, the resulting tracking solution
is still computationally expensive. Therefore, we propose an
approach for reducing the computational cost of the RBPMF.
More precisely, we replace the prediction step by the one of
the Linear Kalman Filter (LKF). This combination helps to
avoid expensive operations such as the weight convolution in the
prediction step. In addition, it also allows complexity reductions
in the correction step. As a result, the complexity is reduced by
one order of magnitude compared to the original RBPMF. We
compare our approach to representative direct-tracking methods,
based on Iterative Extended Kalman Filter (IEKF) and Particle
Filter (PF). The proposed solution has lower and comparable
localization error compared to IEKF and PF, respectively. In
addition, the proposed solution is of slightly less complexity than
PF. However, the complexity reduction is significant compared to
the conventional RBPMF.

Pocoma Copa, E. I., Storrer, L., Quitin, F., Vandendorpe, L., De Doncker, P., & Horlin,
F. (2023). Direct tracking of a wireless transmitter based on rao-blackwellized point mass
filter. IEEE transactions on vehicular technology, 72(12), 16469 - 16481. doi:10.1109/
TVT.2023.3291078