2-D DOA tracking using variational sparse Bayesian learning embedded with Kalman filter

Abstract

In this paper, we consider the 2-D direction-of-arrival (DOA) tracking problem. The signals are captured by a uniform spherical array and therefore can be analyzed in the spherical harmonics domain. Exploiting the sparsity of source DOAs in the whole angular region, we propose a novel DOA tracking method to estimate the source locations and trace their trajectories by using the variational sparse Bayesian learning (VSBL) embedded with Kalman filter (KF). First, a transition probabilities (TP) model is used to build the state transition process, which assumes that each source moves to its adjacent grids with equal probability. Second, the states are estimated by KF in the variational E-step of the VSBL and the variances of the state noise and measurement noise are learned in the variational M-step of the VSBL. Finally, the proposed method is extended to deal with the off-grid tracking problem. Simulations show that the proposed method has higher accuracy than VSBL and KF methods.

Q‐learning for noise covariance adaptation in extended KALMAN filter

“…With an incremental variational expectation–maximization learning framework, a robust outlier detection method for the KF is introduced in [25]. A variational sparse Bayesian learning (VSBL) embedded with the KF is presented in [26] to calculate the noise covariance matrices for linear time‐invariant systems. For nonlinear systems, a learning strategy is demonstrated in [27] to place the sigma points in the unscented Kalman filter (UKF).…”