  This paper proposes a belief propagation (BP)-based algorithm for sequential
detection and estimation of multipath component (MPC) parameters based on radio
signals. Under dynamic channel conditions with moving transmitter/receiver, the
number of MPCs, the MPC dispersion parameters, and the number of false alarm
contributions are unknown and time-varying. We develop a Bayesian model for
sequential detection and estimation of MPC dispersion parameters, and represent
it by a factor graph enabling the use of BP for efficient computation of the
marginal posterior distributions. At each time step, a snapshot-based
parametric channel estimator provides parameter estimates of a set of MPCs
which are used as noisy measurements by the proposed BP-based algorithm. It
performs joint probabilistic data association, and estimation of the
time-varying MPC parameters and the mean number of false alarm measurements, by
means of the sum-product algorithm rules. The algorithm also exploits amplitude
information enabling the reliable detection of "weak" MPCs with very low
component signal-to-noise ratios (SNRs). The performance of the proposed
algorithm compares well to state-of-the-art algorithms for high SNR MPCs, but
it significantly outperforms them for medium or low SNR MPCs. Results using
real radio measurements demonstrate the excellent performance of the proposed
algorithm in realistic and challenging scenarios.
