Distributed TLS over Multitask Networks With Adaptive Intertask Cooperation

Distributed estimation is an important technique for in-network signal processing. In this work, we consider a general case where the unknown parameter vectors (tasks) for different nodes can be different, which is different from the common single-task problem and is known as the multitask problem. Besides, it is assumed that there are some similarities among these tasks. Thus, the performance may be improved by performing the intertask cooperation. To improve robustness against different degrees of difference among the tasks, an adaptive intertask cooperation strategy is proposed. On the other hand, in most of the existing distributed algorithms, it is usually assumed that noise/errors are confined to the output signal. However, in many real environments, the input and output signals may be both corrupted by noise, which is described by the errors-in-variables (EIV) model. In such a case, it has been demonstrated that the total least-squares (TLS) method based on minimizing the squared total error outperforms the classical least-squares method. In this paper, we consider the EIV model with several parameter vectors to be estimated simultaneously, and we derive a distributed TLS algorithm with adaptive intertask cooperation for the in-network cooperative estimation problem. For this algorithm, theoretical performance analysis is provided. Besides, to verify its effectiveness, numerical simulations are given.