Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission

Abstract

This paper is concerned with the optimal fusion estimation problem in networked stochastic systems with bounded random delays and packet dropouts, which unavoidably occur during the data transmission in the network. The measured outputs from each sensor are perturbed by random parameter matrices and white additive noises, which are cross-correlated between the different sensors. Least-squares fusion linear estimators including filter, predictor and fixed-point smoother, as well as the corresponding estimation error covariance matrices are designed via the innovation analysis approach. The proposed recursive algorithms depend on the delay probabilities at each sampling time, but do not to need to know if a particular measurement is delayed or not. Moreover, the knowledge of the signal evolution model is not required, as the algorithms need only the first and second order moments of the processes involved. Some of the practical situations covered by the proposed system model with random parameter matrices are analyzed and the influence of the delays in the estimation accuracy are examined in a numerical example.

Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions

“…As in ref. [ 20 ], let us consider four sensors that provide scalar measurements with different random failures, which are described using random parameters according to the theoretical model ( 1 ). Namely, sensor 1 has continuous gain degradation, sensor 2 has discrete gain degradation, sensor 3 has missing measurements and sensor 4 has both missing measurements and multiplicative noise.…”

“…Hence, the estimation problem for systems with random parameter matrices has experienced increasing interest due to its diverse applications, and many estimation algorithms for such systems have been proposed over the last few years (see, e.g., refs. [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], and references therein).…”

A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities

“…Just to provide some notable examples, consider that Smart Cities can provide a new generation of real-time and time-critical, location-, social-, and context-aware services to their digital citizens, such as for emergency and health-care [ 4 ], surveillance [ 5 ], entertainment, and social good [ 6 , 7 ]. Recent research activity has been focused on many different IoT-related topics such as event forecasting [ 8 ], WSN routing protocols [ 9 ], multi-sensor information fusion [ 10 ], business model and profit maximization [ 11 ], ontologies [ 12 ], service models [ 13 ], quality of experience [ 14 ], and even advanced concepts for the prioritization of raw data processing and information dissemination such as Quality of Information (QoI) [ 15 ] and Value-of-Information [ 16 ]. Researchers have also developed a multitude of application-specific solutions for issues in diagnostics [ 17 , 18 ], environmental monitoring [ 19 , 20 ], and social interest [ 21 ].…”

A Two-Phase Distributed Filtering Algorithm for Networked Uncertain Systems with Fading Measurements under Deception Attacks

“…Regarding the correlation between the additive noises of the state and measurement equations, the estimation problem was addressed in [7] assuming that the sensor noises are correlated with the system noise at the same time step. In [32], it was considered that the measurement noise is correlated with the system noise at the previous time step; this kind of correlation arises, for example, in the linear systems obtained from the discretization of continuous-time systems, causing, at any time step, the measurement noises to be correlated with the signal at the same and subsequent time steps [33]. However, despite its practical significance, just some occasional results are reported on the subject of the distributed filter design problem in sensor networks with a fixed topological structure, in the presence of correlated noises (see, e.g., [34] and the references therein).…”