Detection and Tracking of Multipath Channel Parameters Using Belief Propagation

Xuhong Li, E. Leitinger, F. Tufvesson

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


We present a belief propagation (BP) algorithm with probabilistic data association (DA) for detection and tracking of specular multipath components (MPCs). In real dynamic measurement scenarios, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for specular MPC detection and joint estimation problem, and represent it by a factor graph which enables the use of BP for efficient computation of the marginal posterior distributions. A parametric channel estimator is exploited to estimate at each time step a set of MPC parameters, which are further used as noisy measurements by the BP-based algorithm. The algorithm performs probabilistic DA, and joint estimation of the time-varying MPC parameters and mean false alarm rate. Preliminary results using synthetic channel measurements demonstrate the excellent performance of the proposed algorithm in a realistic and very challenging scenario. Furthermore, it is demonstrated that the algorithm is able to cope with a high number of false alarms originating from the prior estimation stage.
Original languageEnglish
Title of host publication2020 54th Asilomar Conference on Signals, Systems, and Computers
Place of PublicationPacifc Grove, CA, USA
Publication statusPublished - 1 Oct 2020

Fields of Expertise

  • Information, Communication & Computing


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