Event‐triggered Kalman consensus filter for sensor networks with intermittent observations

Abstract

Summary This article investigates the event‐triggered Kalman consensus filtering (ET‐KCF) problem for distributed sensor networks with intermittent observations. First, a novel ET consensus filtering structure is designed for sensor networks with intermittent observations. With the proposed consensus filtering structure, a new ET mechanism that is more efficient than the existed ones is designed to schedule transmissions of local estimates. Then, an optimal ET‐KCF in the sense of minimum mean‐square error is developed. For reducing the computational complexity of filtering algorithm, a suboptimal ET‐KCF is further proposed. Moreover, the stability of the suboptimal ET‐KCF is analyzed. Simulation results verify the validity and superiority of the proposed method.

Finite‐time synchronization of multi‐weighted fractional‐order coupled neural networks with fixed and adaptive couplings

“…Remark The coupling weights have a significant influence on the dynamical character of CNNs, and the proper coupling weights can make it easier to achieve the required dynamical character for CNNs. Furthermore, the coupling weights are flexibly adjusted in a lot of actual networks, for instance, communication networks, 30 wireless sensor networks 42 . Accordingly, we also studied MFCNNs with adaptive coupling weights.…”

“…Furthermore, the coupling weights are flexibly adjusted in a lot of actual networks, for instance, communication networks, 30 wireless sensor networks. 42 Accordingly, we also studied MFCNNs with adaptive coupling weights. Based on the proposed adaptive scheme, the coupling weights will be adaptively changed to meet the need in (20) for any t ∈ [0, +∞).…”

Consensus‐based robust least‐squares filter for multi‐sensor systems

“…Now, by utilizing the result of Lemma 1 and applying the conversions in (22), it is straightforward to obtain the solution of optimization problem (14) as (16). ▪…”

“…A consensus algorithm determines the exchange of information between all neighbors in a network and results in reaching an agreement on a certain quantity or common value in all sensors 14 . Recently, consensus control and the integration of multiple sensors and consensus filters has been reported in considerable amount of publications 15‐18 . The theory, design method, and problem‐solving of consensus filtering in a network of systems are presented in References 19 and 20.…”

“…14 Recently, consensus control and the integration of multiple sensors and consensus filters has been reported in considerable amount of publications. [15][16][17][18] The theory, design method, and problem-solving of consensus filtering in a network of systems are presented in References 19 and 20. In general, based on the agreement parameter, consensus filtering approaches are classified into four subgroups.…”