  We study the problem of decentralized interferometric imaging over networks,
where agents have access to a subset of local radar measurements and can
compute pair-wise correlations with their neighbors. We propose a primal-dual
distributed algorithm named Distributed Generalized Wirtinger Flow (DGWF). We
use the theory of low rank matrix recovery to show when the interferometric
imaging problem satisfies the Regularity Condition, which implies the
Polyak-Lojasiewicz inequality. Moreover, we show that DGWF converges
geometrically for smooth functions. Numerical simulations for single-scattering
radar interferometric imaging demonstrate that DGWF can achieve the same
mean-squared error image reconstruction quality as its centralized counterpart
for various network connectivity and size.
