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In this paper, we studied the least mean-square-based distributed adaptive filters, aiming at collectively estimating a sequence of unknown signals(or time-varying parameters) from a set of noisy measurements obtained through distributed sensors. The main contribution of this paper to relevant literature is that under a general stochastic cooperative signal condition, stability and performance bounds are established for distributed filters with general connected networks without stationarity or independency assumptions imposed on the regression signals.
In this paper, we studied the least mean-square-based distributed adaptive filters, aiming at collectively estimating a sequence of unknown signals (or time-varying parameters) from a set of noisy measurements obtained through distributed sensors. The main contribution of this paper to relevant literature is that under a general stochastic cooperative signal condition, stability and performance bounds are established for distributed filters with general connected networks without stationarity or independency assumptions imposed on the regression signals.