Stability of FFLS-Based Diffusion Adaptive Filter Under Cooperative Excitation Condition

In this article, we consider the distributed filtering problem over sensor networks such that all sensors cooperatively track unknown time-varying parameters by using local information. A distributed forgetting factor least squares algorithm is proposed by minimizing a local cost function formulated as a linear combination of accumulative estimation error. Stability analysis of the algorithm is provided under a cooperative excitation condition which contains spatial union information to reflect the cooperative effect of all sensors. Furthermore, we generalize theoretical results to the case of Markovian switching directed graphs. The main difficulties of theoretical analysis lie in how to analyze properties of the product of nonindependent and nonstationary random matrices. Some techniques, such as stability theory, algebraic graph theory, and Markov chain theory are employed to deal with the above issue. Our theoretical results are obtained without relying on the independence or stationarity assumptions of regression vectors which are commonly used in existing literature. Finally, numerical simulations are provided to demonstrate the effectiveness of our theoretical results.