Outlier-Resistant Filtering for Networked 2-D FMLSS Systems With Dead-Zone-Like Censoring Under Asynchronous Random Delays

This paper addresses the problem of outlier-resistant estimation in a two-dimensional (2-D) Fornasini-Marchesini local state-space (FMLSS) systems with dead-zone-like censoring under asynchronous random delays. To mitigate trend outliers caused by extreme noise, a dead-zone-like censoring mechanism is introduced, where the truncation region is constrained by predetermined left and right truncation thresholds. Additionally, considering the delay phenomenon in networked systems, a new model is developed to describe asynchronous random delays under the assumption of a bounded maximum delay. To handle the incomplete information resulting from these delays, an innovative approach is proposed, using a Tobit fictitious observation sequence. By rearranging the fictitious observation sequence, the original delayed Tobit fictitious observation system is transformed into a multi-channel delay-free rearranged Tobit observation system. Based on this, an (N+1)-step extended KF is proposed, which employs adjustable scale parameters to derive a tight upper bound for the estimation error covariance. This bound is then minimized to design the estimation gain. Furthermore, the boundedness of the estimation error covariance is analyzed in the 2-norm sense. Finally, the effectiveness of the proposed outlier-resistant estimation approach is demonstrated through a numerical example