Ternary-Event-Based State Estimation With Joint Point, Quantized, and Set-Valued Measurements

Abstract

This letter proposes a novel ternary-event-based particle filtering ( $\text{TEB-PF}$ ) framework by introducing the ternary-event-triggering mechanism coupled with a non-Gaussian fusion strategy that jointly incorporates point-valued, quantized, and set-valued measurements. In contrast to the existing binary-event-triggering solutions, the $\text{TEB-PF}$ is a distributed state estimation architecture where the remote sensor communicates its measurements to the estimator, residing at the fusion centre, in a ternary-event-based fashion, i.e., holds on to its observation during idle epochs, transfers quantized ones during the transitional epochs, and only communicates raw observations during event epochs. Due to joint utilization of quantized and set-valued measurements in addition to the point-valued ones, the proposed $\text{TEB-PF}$ simultaneously reduces the communication overhead, in comparison to its binary triggering counterparts, while also improving the estimation accuracy specially in low communication rates.