  This paper studies the estimation of network weights for a class of systems
with binary-valued observations. In these systems only quantized observations
are available for the network estimation. Furthermore, system states are
coupled with observations, and the quantization parts are unknown inherent
components, which hinder the design of inputs and quantizers. To fulfill the
estimation, we propose a recursive algorithm based on stochastic approximation
techniques. More precisely, to deal with the temporal dependency of
observations and achieve the recursive estimation of network weights, a
deterministic objective function is constructed based on the likelihood
function by extending the dimension of observations and applying ergodic
properties of Markov chains. It is shown that this function is strictly concave
and has unique maximum identical to the true parameter vector. Finally, the
strong consistency of the algorithm is established. Our recursive algorithm can
be applied to online tasks like real-time decision-making and surveillance for
networked systems. This work also provides a new scheme for the identification
of systems with quantized observations.
