  Distributed phased arrays are comprised of separate, smaller antenna systems
that coordinate with each other to support coherent beamforming towards a
destination. However, due to the frequency drift and phase jitter of the
oscillators, as well as the frequency and phase estimation errors induced at
the nodes, there exists decoherence that degrades the beamforming process. A
decentralized frequency and phase consensus (DFPC) algorithm was proposed in
prior work for undirected networks in which the nodes locally share their
frequencies and phases with their neighbors to reach synchronization. Kalman
filtering (KF) was also integrated with DFPC (KF-DFPC) to lower the total
residual phase error upon convergence. Since these DFPC-based algorithms rely
on the average consensus protocol, they do not converge for directed networks.
In this paper, we propose a push-sum based frequency and phase consensus
(PsFPC) algorithm for the directed networks. The residual phase error of PsFPC
is theoretically derived as well. Kalman filtering is also integrated with
PsFPC and the resulting KF-PsFPC algorithm shows a significant reduction in the
residual phase error upon convergence. KF assumes that the model parameters,
i.e., the measurement noise and innovation noise covariance matrices, are
known. Since they may not be known in practice, we develop an online
expectation maximization (EM) based algorithm that iteratively computes the
maximum likelihood (ML) estimate of the unknown matrices in an online manner.
EM is integrated with KF-PsFPC to propose the EM-KF-PsFPC algorithm. Simulation
results are included where the performance of the PsFPC-based algorithms is
analyzed for different distributed phased arrays and is compared to other
algorithms.
