Protocol‐based extended Kalman filtering with quantization effects: The Round‐Robin case

Abstract

This article investigates the extended Kalman filtering problem for a class of stochastic nonlinear systems with quantization effects and Round-Robin (RR) communication protocols. The uniform quantization is considered and the resulting quantization error is characterized as an additive white noise sequence obeying the uniform distribution over certain intervals. For the sake of reducing communication traffic of the network as well as alleviating data collisions, the RR mechanism is introduced to schedule the data transmission from the sensors to the filter. By combining the periodic property of the RR protocol and the zero-order holder strategy, the input signal of the filter is modeled by a sequence of delayed quantized measurements. The main purpose of this article is to design an extended Kalman filter for the stochastic nonlinear systems, in the simultaneous presence of quantization errors, stochastic nonlinearities, and stochastic noises, such that an optimized upper bound for the filtering error covariance is derived. By solving two coupled Riccati-like difference equations, the filter gain matrix is explicitly formulated. An RR protocol-based recursive filtering algorithm is developed for the online implementation. Furthermore, a sufficient condition is established to ensure the uniform boundedness of the filtering error in the mean-square sense. Finally, a simulation example is given to demonstrate the practical validity of the designed filter algorithm.

Event‐based finite‐time H∞ synchronization control for switched complex networks with average dwell time

“…Further research topics include, but are not limited to, extending the main results of this paper to (1) multi-agent systems, sensor networks or neural networks where the topology structure plays a pivotal role and may also face the problems discussed in this paper 23,[36][37][38] and (2) more general CNs with uncertain parameters or with stochastic parameters 39 as well as (3) CNs with other communication schemes. [40][41][42][43]…”

Network Slicing on 5G Vehicular Cloud Computing Systems

“…Well known non-QoS aware algorithms for resource allocation are the Proportional Fairness (PF) [11], the Round Robin (RR) [12], the Maximum Throughput (MT) [13], the Blind Equal Throughput (BET) [13] and the Throughput to Average (TTA) [14].…”

A Two-Filter Approach for State Estimation Utilizing Quantized Output Data

“…In spite of the loss of information, the benefits of quantization have led to a number of applications in which quantized measurements arise. This occurs due to fundamental limitations on measuring equipment and bandwidth resources [34], digital and analog converters [35], and experimental designs where it is necessary to quantize the data in order to store it or minimize communication resource utilization [36]. In particular, estimation problems utilizing quantized measurements arise in networked control over limited-/finite-capacity communication channels, where usually, encoder-decoder state estimation schemes are used [37][38][39][40].…”

“…As mentioned above, the quantization process is a nonlinear map that results in a significant loss of information on the system dynamics, which produces a biased state estimation and incorrect characterization of the filtering and smoothing probability density functions (PDFs). In this context, several suboptimal filtering and smoothing algorithms for state-space systems with quantized data have been developed, for instance, standard- [49,54], unscented- [55], and extended- [36] Kalman filters for quantized data, in which some structural elements of the state-space models and the quantizer are exploited. Sequential Monte Carlo methods have been also used for filtering and smoothing with quantized data, where complex integrals are approximated by a set of weighted samples called particles [31], which define (approximately) a desired PDF.…”