This research proposes distributed methods for jointly estimating the states and possible unknown inputs of a discrete-time system, observed through a sensor network. Traditionally, an unbiased state estimate can be obtained by using distributed Kalman filters if the system is only subject to noise with known stochastic properties. However, if the system is also subject to completely unknown inputs, which can represent additive faults, unmodeled dynamics, or unknown disturbances intentionally generated by a malicious entity, the estimated state is no longer unbiased and optimal. This study proposes three new distributed algorithms that allow carrying out an unbiased state estimation in the presence of an unknown input while providing an estimate of such unknown inputs. For more information see (Esna Ashari et. al. 2012a) and (Esna Ashari et. al. 2012b).