Incoherent Distributed Sources Tracking Algorithm for Massive MIMO Systems

To address the time-varying direction-of-arrival (DOA) tracking problem of incoherently distributed (ID) signal sources for massive multiple-input multiple-output (MIMO) systems, which involves critical challenges of data association ambiguity and measurement uncertainty in dynamic environments, this work proposes a novel tracking algorithm for ID sources using the generalized labeled multi-Bernoulli (GLMB) filter and smoother. The algorithm first models ID sources using a uniform rectangular array (URA), and then estimates the nominal DOA, along with angular spreads, via the beamspace unitary estimating signal parameters via rotational invariance techniques (UESPRIT) algorithm. The above estimates are then used as pseudo-measurements for each time step within the GLMB filter and smoother to improve estimation accuracy. At the same time, for the uncertainty of measurement noise, Cramér-Rao bound (CRB) is used to approximate the covariance of measurement noise in the tracking process.Within the framework of the GLMB smoother, a forward filtering stage is performed to generate the association history as well as the trajectory information using the GLMB filter. Term trajectories and trajectory fragments are removed through the trajectory pruning operation. Next, the backward smoothing is applied to achieve the data association of the ID sources and the estimation of kinematic states. To verify the effectiveness of the proposed approach, Monte Carlo (MC) simulations are performed. The results confirm its superior DOA tracking capability, enhanced estimation accuracy, and high robustness.