2 research outputs found
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
The information transmission between nodes in a wireless sensor networks
(WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy
limitations, and environmental factors, and the information that is
successfully transmitted can also be contaminated by non-Gaussian noise. The
presence of these two factors poses a challenge for distributed state
estimation (DSE) over WSNs. In this paper, a generalized packet drop model is
proposed to describe the packet loss phenomenon caused by DoS attacks and other
factors. Moreover, a modified maximum correntropy Kalman filter is given, and
it is extended to distributed form (DM-MCKF). In addition, a distributed
modified maximum correntropy Kalman filter incorporating the generalized data
packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the
presence of both non-Gaussian noise pollution and packet drop. A sufficient
condition to ensure the convergence of the fixed-point iterative process of the
DM-MCKF-DPD algorithm is presented and the computational complexity of the
DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility
of the proposed algorithms are verified by simulations
Two Compensation Strategies for Optimal Estimation in Sensor Networks with Random Matrices, Time-Correlated Noises, Deception Attacks and Packet Losses
Due to its great importance in several applied and theoretical fields, the signal estimation
problem in multisensor systems has grown into a significant research area. Networked systems are
known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance
of the estimators substantially. Thus, the development of estimation algorithms accounting for these
random phenomena has received a lot of research attention. In this paper, the centralized fusion linear
estimation problem is discussed under the assumption that the sensor measurements are affected
by random parameter matrices, perturbed by time-correlated additive noises, exposed to random
deception attacks and subject to random packet dropouts during transmission. A covariance-based
methodology and two compensation strategies based on measurement prediction are used to design
recursive filtering and fixed-point smoothing algorithms. The measurement differencing methodâ
typically used to deal with the measurement noise time-correlationâis unsuccessful for these kinds of
systems with packet losses because some sensor measurements are randomly lost and, consequently,
cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of
the measurement noises and the innovation technique. The two proposed compensation scenarios
are contrasted through a simulation example, in which the effect of the different uncertainties on the
estimation accuracy is also evaluated.Ministerio de Ciencia e Innovacion, Agencia Estatal de InvestigacionEuropean Commission PID2021-124486NB-I0