Distributed Kalman Filtering and Control Through Embedded Average Consensus Information Fusion

Abstract

This work presents a unified framework for distributed filtering and control of state-space processes. To this end, a distributed Kalman filtering algorithm is developed via decomposition of the optimal centralized Kalman filtering operations. This decomposition is orchestrated in a fashion so that each agent retains a Kalman style filtering operation and an estimate of the state vector. In this setting, the agents mirror the operations of the centralized Kalman filter in a distributed fashion through embedded average consensus fusion of local state vector estimates and their associated covariance information. For rigor, closed-form expressions for the mean and mean square error performance of the developed distributed Kalman filter are derived. More importantly, in contrast to current approaches, due to the comprehensive framework for fusion of the covariance information, a duality between the developed distributed Kalman filter and decentralized control is established. Thus, resulting in an effective and all inclusive distributed framework for filtering and control of state-space processes over a network of agents. The introduced theoretical concepts are validated using simulations that indicate a precise match between simulation results and the theoretical analysis. In addition, simulations indicate that performance levels comparable to that of the optimal centralized approaches are attainable.

Distributed fusion Kalman filtering under binary sensors

“…Here, u(t) is firstly set as 60% and all the thresholds are 134, 134.5, 135, 135.5, 136, and 136.5, then u(t) is set as 90% and all the thresholds are 197. 5, 198, 198.5, 199, 199.5, and 200. In the simulation, the parameters i (t) and i (t) are taken as the sum of a constant and the maximum diagonal element of the right terms in (16). By implementing Algorithm 1, the trajectories of state x(t) and fusion estimationx(t) are plotted in Figure 2A, which shows that the proposed DFKF can estimate the O 2 content well.…”

“…Meantime, a novel bounded recursive optimization estimator was developed from a fusion perspective in the work of Chen for bounded noises, where the bounds of noises are unknown. As for Gaussian noises, Talebi et al developed a distributed Kalman filtering algorithm by distributing the operations of embedded average consensus information fusion filters, while Xie et al proposed a reliable distributed Kalman fusion scheme over sensor networks subject to abnormal measurements and energy constraints. The work of Chen was concerned with distributed fusion Kalman filtering problem for systems with missing sensor measurements, random transmission delays and packet dropouts, and the globally optimal distributed Kalman filtering fusion with singular covariances of filtering errors and measurement noises was designed by Song et al Notice that Kalman filtering is one of the most popular recursive Least Mean Square Error (LMSE) algorithm, and it is also reasonable to approximate the noises or disturbances in practical applications as Gaussian noises.…”

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

“…Therefore, we cannot derive a concise time update equation. What leads to the convenient expression in the exact filter (28)-(34) is the joint density in (15) and (16). Therefore, it is a reasonable choice to force the actual state and noise density into this format.…”

Mass Game Simulator: An Entertainment Application of Multiagent Control

“…First, the systems can perform tasks that cannot be achieved by single-agent systems. Examples include environmental monitoring [9] and cooperative transportation [10] using multiple agents, state estimation via data fusion [11], [12], and distributed optimization over networks [13]. Second, multiagent systems are robust against failures.…”