Algorithm 1. Event-driven- maximum correntropy filter based on Cauchy kernel (ED-MCFCK)
1	Initialization: ${\widehat{\mathit{x}}}_0$, ${\mathit{P}}_0$
2	For k = 1,2,…
	{
3	Compute ${\widehat{\mathit{X}}}_{k/k-1}$ and ${\mathit{P}}_{k/k-1}$ as (22) and (23) of Kalman filtering procedures;
4	Calculate filter’s innovation vector as well as its covariance as (44) and (45), and further construct the normalized innovation vector as (47).
5	Compute and conduct the event driven condition by (48).
6	If ${‖{\overline{\mathit{Z}}}_k‖}_{\infty }<{\kappa }_{\beta }$ and ${\tau }_k=0$, set the state prediction ${\widehat{\mathit{X}}}_{k/k-1}$ and ${\mathit{P}}_{k/k-1}$ as the final state estimation.
7	If ${\kappa }_{\beta }\le {‖{\overline{\mathit{Z}}}_k‖}_{\infty }\le {\kappa }_{\alpha }$ and ${\tau }_k=1$, execute (24)–(26) to obtain system state esti-mation ${\widehat{\mathit{X}}}_k$ and ${\mathit{P}}_k$.
8	If ${‖{\overline{\mathit{Z}}}_k‖}_{\infty }>{\kappa }_{\alpha }$ and ${\tau }_k=0$, (indicating the presence of non-Gaussian noise in the measurement information), the MCFCK is driven.
	{
9	Let the iteration index t = 1 and ${\widehat{\mathit{X}}}_k^1={\widehat{\mathit{X}}}_{k/k-1}$;
10	Compute ${\tilde{\mathit{P}}}_{k/k-1}$ and ${\tilde{\mathit{R}}}_k$ according to (40) and (41), and further calcu late filter gain ${\tilde{\mathit{K}}}_k$ by (39).
11	Compute ${\widehat{\mathit{X}}}_k^t$ by (38) and test the Condition (42);
12	If Condition $\ge \epsilon$
13	Let t = t + 1 (iterations) and ${\widehat{\mathit{X}}}_k={\widehat{\mathit{X}}}_k^{t+1}$, and conduct the next iteration.
14	Else, the iterative process is terminated and ${\widehat{\mathit{X}}}_k={\widehat{\mathit{X}}}_k^t$.
	}
15	Compute the posterior error covariance matrix by (43).
	}