Алгоритми дистрибуиране детекције и естимације засновани на консензусу

Abstract

Ова дисертација разматра неколико важних проблема у дистрибуираној обради сигнала, то јест, у дистрибуираној детекцији и естимацији сигнала...

This work deals with several important problems of distributed signal processing, i.e., distributed signal detection and estimation. Novel algorithms for distributed change detection and target tracking using sensor networks, as well as algorithm for fault detection and isolation using overlapping system decomposition are proposed. All the proposed algorithms are based on the introduction of a consensus strategy, whose dynamics is combined in parallel with detection or estimation dynamics, enforcing in such a way agreement between the sensors of the used sensor networks or between overlapping subsystems of the monitored systems, and obtaining robust and ecient solutions of the considered problems. In the rst part a novel distributed algorithm derived from the Generalized Likelihood Ratio is proposed for real time change detection using sensor networks. The algorithm is based on a combination of recursively generated local statistics and a global consensus strategy, and does not require any fusion center. The problem of detection of an unknown change in the mean of an observed random process is discussed and the performance of the algorithm is analyzed in the sense of a measure of the error with respect to the corresponding centralized algorithm. The analysis encompasses asymmetric constant and randomly time varying matrices describing communications in the network, as well as constant and time varying forgetting factors in the underlying recursions. An analogous algorithm for detection of an unknown change in the variance is also proposed. Simulation results illustrate characteristic properties of the algorithms including detection performance in terms of detection delay and false alarm rate. They also show that the theoretical analysis connected to the problem of detecting change in the mean can be extended to the problem of detecting change in the variance. A new distributed fault detection and isolation (FDI) methodology is proposed in the second part, in the form of a multi-agent network representing a combination of a consensus based FDI observer for residual generation and a consensus based decision making strategy for change detection, applicable in real time. The proposed observer is based on overlapping system decomposition and a combination between the local optimal stochastic FDI observers and a dynamic consensus strategy. It is shown how the proposed algorithm can generate residuals which provide, under general conditions concerning local models and the network topology, high eciency, scalability and robustness. The proposed decision making strategy provides solutions for two particular cases: a) local detection for non-overlapping parts of the identied subsystems; b) a consensus based strategy for FDI in the overlapping parts. Selected examples illustrate the applicability of the proposed methodology in practice...