In this paper, we focus on the problem of distributed state estimation for a target system driven by continuous-time linear time-invariant (LTI) system with unknown inputs, where the communication channels are disturbed by stochastic noise and subjected to impulsive sequential attacks. In multi-agent systems (MASs), agents usually move along relatively smooth paths, but occasionally take evasive actions or make sudden turns. This pattern of motion results in relatively small communication disturbances most of the time, but large communication disturbances can occasionally occur. First, this communication disturbances are modeled as a hybrid form: stochastic noise and impulsive sequential attacks, where stochastic noise stands for relatively small communication disturbances most of the time, and impulsive sequential attacks stand for occasionally large communication disturbances. Then, a practical/bounded distributed unknown input observer (DUIO) under stochastic noise and impulsive sequential attack is proposed. Furthermore, the sufficient conditions for the realization of the practical/bounded DUIO in the sense of mean square convergence are presented by graph theory and stochastic analysis. The error bound that depends on the coupling gain, stochastic noise intensity, and average impulsive interval is provided. Finally, the validity of the designed practical/bounded DUIO is confirmed by numerical simulations. Note to Practitioners—Information exchange among agents is a critical for achieving coordination in MASs. Most existing works usually assume that the communication is ideal, i.e., each agent is able to accurately obtain information from its neighbors or is only disturbed by stochastic noise. However, agents generally move along relatively smooth paths but may occasionally execute evasive maneuvers or abrupt turns in MASs. This motion pattern usually results in relatively small communication disturbances, though in certain instances, significant disruptions may occur. How to effectively deal with the impact of such non-ideal communication environments on system performance and ensure that agents can still maintain stable cooperation in complex dynamic environments has become one of the critical topics in the field of control. Specifically, this problem involves several challenges: First, the random and non-Gaussian characteristics of communication disturbances render traditional filtering and estimation methods difficult to apply directly; Second, the burstiness and uncertainty of impulsive noise can lead to severe fluctuations in system states, thereby affecting coordination among agents. In light of this, this paper models such communication disturbances as stochastic impulsive noise and proposes a practical/bounded DUIO under stochastic impulsive noise.