Event-Triggered Globally Sequential Fusion Estimation for Clustered Wireless Sensor Networks With Variational Bayesian

Novel event‐triggered distributed state estimation algorithm for nonlinear systems over wireless sensor networks

“…To further analyze the relationship between the accuracy of distributed state estimation and trigger threshold, as well as the relationship between communication frequency and trigger threshold, the variation of $$$ {\mathrm{MRMSE}}_{\mathrm{position}} $$$ of $$$ \Phi =5 $$$ and the average communication rate $$$ \gamma $$$ with trigger threshold $$$ \delta $$$ are derived by numerical simulations, respectively, as shown in Figure 7. The average communication rate $$$ \gamma $$$ is defined as 30 where is the number of samples. From Figure 7, it is observed that $$$ {\mathrm{MRMSE}}_{\mathrm{position}} $$$ increases and $$$ \gamma $$$ decreases with the increasing of $$$ \delta $$$, respectively.…”

“…To further analyze the relationship between the accuracy of distributed state estimation and trigger threshold, as well as the relationship between communication frequency and trigger threshold, the variation of MRMSE position of Φ = 5 and the average communication rate 𝛾 with trigger threshold 𝛿 are derived by numerical simulations, respectively, as shown in Figure 7. The average communication rate 𝛾 is defined as 30…”

“…In Theorem 2, the assumption we used to develop the stability analysis of the EDCKF algorithm is reasonable and is widely utilized in the state-of-the-art researches. [21][22][23][26][27][28][29][30][31]38 Moreover, although the compensation diagonal matrices 𝜂 i k and 𝜃 i k are unknown, the stability of the EDCKF algorithm is still achieved, which indicates that the stability of the proposed algorithm will not be influenced by the system model.…”

“…Therefore, in comparison to the aforementioned estimator, it is possible to achieve an improved estimation performance by designing a novel event‐triggered nonlinear state estimator. Inspired by Reference 30, a pseudo measurement expression is derived in the next content when the lastly transmitted measurement $$$ {\overline{z}}_{k-1}^i $$$ is utilized to update the state estimation at time instant $$$ k $$$. Denote $$$ {\zeta}_k^i={\overline{z}}_{k-1}^i-{z}_k^i $$$ as the error between the lastly sent measurement $$$ {\overline{z}}_{k-1}^i $$$ and the currently observed measurement $$$ {z}_k^i $$$, from (2) and (4), then $$$ {\overline{z}}_{k-1}^i $$$ is formulated as $$$$ {\overline{z}}_{k-1}^i={h}^i\left({x}_k\right)+{\overline{v}}_k^i,\kern1em i=1,\dots, N, $$$$ where …”

“…where Considering the fact that frequent data transmissions will accelerate the consumption of the sensor battery and short the service life of the WSNs, an event-triggered scheduler 24,28,30 therefore is employed in each sensor to reduce unnecessary communication cost and save energy. Measurement observed by a sensor will be sent to its remote estimator if and only if the event triggering condition is satisfied.…”