This paper presents a noise adaptive variational Bayesian cubature information filter based on Wishart distribution. In the frame of recursive Bayesian estimation, the noise adaptive information filter propagating the information matrix and information state is derived. And the integration of recursive Bayesian estimation is approximated by cubature integration rule. Then, the inverse of measurement noise matrix is modeled as a Wishart distribution, so the joint distribution of posterior state and measurement noise can be approximated by the product of independent Gaussian and Wishart. Furthermore, the corresponding square root version is also derived to improve numerical characteristics. Simulation results with unknown and correlated measurement noise demonstrate the effectiveness of the proposed algorithms.

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.

Unmanned Aerial Manipulators (UAMs) have much potential for automating the cleaning of the high-rise windows. In this letter, a switchable UAM system is designed to carry out the high-rise cleaning task by installing a cleaning robot onto the window. The UAM system is divided into four phases: free-flight, attaching, delivery and detaching. Control methods are developed for all phases, and switch strategies between different phases are proposed. The results of outdoor window-cleaning robot installation experiments using the proposed UAM system demonstrate the effectiveness of our control methods and switch strategies.

This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based algorithms are effective for distributed fusion and MTT, it may be problematic when distributed sensor nodes have different FoVs. To deal with this issue, the proposed method constructs an extended label space mapping to overcome the “label space mismatching” phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step. The efficiency and robustness of the proposed algorithm are demonstrated in a distributed MTT scenario via numerical simulations.

This letter addresses the consensus-based nonlinear state estimation in distributed sensor networks with unknown measurement noise statistics. The existence of naive nodes and the communication constraint in sensor networks requires a hybrid consensus filtering method. In the frame of consensus filtering, a novel consensus nonlinear filtering approach named variational Bayesian consensus cubature Kalman filter (VB-CCKF) is proposed, in which the CKF is employed to handle the nonlinear state estimation and the VB approximation is adopted to iteratively estimate the sufficient statistics of the measurement noise covariance on each step. Simulations are performed in order to demonstrate the effectiveness of the proposed approach.