Event-triggered distributed filtering for discrete-time systems with integral measurements

Abstract

This paper handles the event-triggered distributed filtering problem for a class of discrete-time systems with integral measurements over sensor networks (SNs). First of all, a plant is monitored by an SN formulated by a digraph. The integral measurement phenomenon is modelled for each node to account for the interval time taken by sample collection. Next, to mitigate the communication burden of network, an event generator function is introduced for each node to regulate the data transmission to its neighbouring nodes. This paper aims to find the suitable filter gains for each node such that the filtering error dynamics satisfies the exponentially ultimately boundedness. Via the Lyapunov stability theory, a sufficient condition is established which ensures the existence of the distributed filter under the desirable performance index. Moreover, the ultimate bound in the performance index is minimized via the robust optimization method. Then, the filter gains are acquired by solving a linear matrix inequality with the YALMIP toolbox. Finally, a numerical simulation example is employed to verify the usefulness of the distributed filtering algorithm developed in this paper.

Auxiliary Particle Filtering Over Sensor Networks Under Protocols of Amplify-and-Forward and Decode-and-Forward Relays

“…AF under Protocol 2 (AFP2). In this case, the ith source does not directly send the signal to the destination, and the signal received from the ith source is d a2,i,k = r a,i,k , where r a,i,k is defined in (3).…”

“…Note that p r a,i,k |λ m i,k can be determined based on the relay-terminal transmission model (3). However, the conditional probability p r a,i,k |λ m i,k is difficult to be accurately calculated because the multiplicative gain h RD,i,k and additive noise m i,k are both random in the relay-destination channel.…”

“…As one of the fundamental issues in disciplines of control and signal processing, the filtering problem has been receiving an ever-growing research interest for a few decades, see e.g. [3], [8], [13], [16], [22], [25], [44], [52] for some recent references. Among various types of filters, the particle filter (PF) stands out as a representative in the Bayesian framework that has been extensively investigated [9], [50].…”