On hybrid consensus-based extended Kalman filtering with random link failures over sensor networks

summary:This paper is concerned with the distributed filtering problem for nonlinear time-varying systems over wireless sensor networks under random link failures. To achieve consensus estimation, each sensor node is allowed to communicate with its neighboring nodes according to a prescribed communication topology. Firstly, a new hybrid consensus-based filtering algorithm under random link failures, which affect the information exchange between sensors and are modeled by a set of independent Bernoulli processes, is designed via redefining the interaction weights. Second, a novel observability condition, called parameterized jointly uniform observability, is proposed to ensure the stochastic boundedness of the error covariances of the hybrid consensus-based filtering algorithm. Finally, an example is given to demonstrate the effectiveness of the derived theoretical results

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Unifying Consensus and Covariance Intersection for Efficient Distributed State Estimation over Unreliable Networks

This thesis studies the problem of recursive distributed state estimation over unreliable networks. The main contribution is to fuse the independent and dependent information separately. Local estimators communicate directly only with their immediate neighbors and nothing is assumed about the structure of the communication network, specifically it need not be connected at all times. The proposed estimator is a Hybrid one that fuses independent and dependent (or correlated) information using a distributed averaging and iterative conservative fusion rule respectively. It will be discussed how the hybrid method can improve estimators's performance and make it robust to network failures. The content of the thesis is divided in two main parts. In the first part I study how this idea is applied to the case of dynamical systems with continuous state and Gaussian noise. I establish bounds for estimation performance and show that my method produces unbiased conservative estimates that are better than Iterative Covariance Intersection (ICI). I will test the proposed algorithm on an atmospheric dispersion problem, a random linear system estimation and finally a target tracking problem. In the second part, I will discuss how the hybrid method can be applied to distributed estimation on a Hidden Markov Model. I will discuss the notion of conservativeness for general probability distributions and use the appropriate cost function to achieve improvement similar to the first part. The performance of the proposed method is evaluated in a multi-agent tracking problem and a high dimensional HMM and it is shown that its performance surpasses the competing algorithms

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Distributed target tracking in wireless camera networks

PhDDistributed target tracking (DTT) is desirable in wireless camera networks to achieve scalability and robustness to node or link failures. DTT estimates the target state via information exchange and fusion among cameras. This thesis proposes new DTT algorithms to handle five major challenges of DTT in wireless camera networks, namely non-linearity in the camera measurement model, temporary lack of measurements (benightedness) due to limited field of view, redundant information in the network, limited connectivity of the network due to limited communication ranges and asynchronous information caused by varying and unknown frame processing delays. The algorithms consist of two phases, namely estimation and fusion. In the estimation phase, the cameras process their captured frames, detect the target, and estimate the target state (location and velocity) and its uncertainty using the Extended Information Filter (EIF) that handles non-linearity. In the fusion phase, the cameras exchange their local target information with their communicative neighbours and fuse the information. The contributions of this thesis are as follows. The target states estimated by the EIFs undergo weighted fusion. The weights are chosen based on the estimated uncertainty (error covariance) and the number of nodes with redundant information such that the information of benighted nodes and the redundant information get lower weights. At each time step, only the cameras having the view of the target and the cameras that might have the view of the target in the next time step participate in the fusion (tracking). This reduces the energy consumption of the network. The algorithm selects the cameras dynamically by using a threshold on their shortest distances (in the communication graph) from the cameras having the view of the target. Before fusion, each camera predicts the target information of other cameras to temporally align its information with the (asynchronous) information received from other cameras. The algorithm predicts the target state using the latest estimated velocity of the target. The experimental results show that the proposed algorithms achieve higher tracking accuracy than the state of the art under the five DTT challenges