Distributed cubature information filtering based on weighted average consensus

Privacy-preserving push-sum distributed cubature information filter for nonlinear target tracking with switching directed topologies

To address the data security issue of distributed state estimation, this paper proposes a novel consensus-based cubature information filtering algorithm for sensitive nonlinear target tracking under restricted communication. Through a privacy-preserving approach via state decomposition, the algorithm can protect the privacy of local information from adversaries without sacrificing global estimation accuracy. Based on the push-sum consensus, the distributed approach is further extended to switching directed topologies, which is more feasible for tracking with communication constraints. Our method's average convergence, privacy preservation, and stability are theoretically proved. Finally, simulations are conducted to evaluate the algorithm's performance on security, accuracy, and robustness.

FBLS-Based Fusion Method for Unmanned Surface Vessel Positioning Considering Denoising Algorithm

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

Distributed finite time cubature information filtering with unknown correlated measurement noises

This paper addresses the distributed state estimation problem for a class of discrete nonlinear system over sensor networks subject to unknown correlated measurement noises. Firstly, under the condition of network connectivity, a novel communication protocol is developed to ensure every sensor node can gather the information distributed throughout the network within finite communication time. Then a fully distributed estimator is designed by periodically fusing the local information and neighbor's information according to the covariance intersection fusion strategy. Theoretically, it is proved that the distributed estimator in each sensor node is stable with the exponentially bounded estimation error in mean square. Finally, some numerous simulations are performed to illustrate the practical effectiveness and superiority of the proposed state estimator.

Robust Consensus Nonlinear Information Filter for Distributed Sensor Networks With Measurement Outliers

The traditional consensus-based filters are widely used in distributed sensor networks. However, they suffer from divergence when outliers occur. This paper proposes a robust consensus nonlinear information filter for distributed state estimation with measurement outliers. Unlike the Gaussian assumption in traditional consensus filers, the measurement of each sensor node is modeled here as a multivariate Student- t process with unknown parameters of the sufficient statistic. The variational Bayesian method is employed to jointly estimate the state and the parameters. As the state and parameters are coupled, the updated equation can be solved by fixed point iteration. The centralized outliers robust information filter is first derived for multiple sensors. It is then extended to a distributed version to fuse information from multiple interconnected local estimators. The integral of the consensus-based nonlinear filter is approximated by Gaussian approximation under the framework of the information filter. The consensuses are based on both likelihoods and prior probability distributions. The consensus and convergence of the proposed method are also analyzed. Simulation results show that the proposed approach is effective in dealing with outliers.