A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

Target tracking based on a multi-sensor covariance intersection fusion Kalman filter

In a multi-sensor target tracking system, the correlation of the sensors is unknown, and the cross-covariance between the local sensors can not be calculated. To solve the problem, the multisensor covariance intersection fusion steady-state Kalman filter is proposed. The advantage of the proposed method is that the identification and computation of cross-covariance is avoided, thus the computational burden is significantly reduced. The new algorithm gives an upper bound of the covariance intersection fused variance matrix based on the convex combination of local estimations, therefore, ensures the convergence of the fusion filter. The accuracy of the covariance intersection (CI) fusion filter is lower than and close to that of the optimal distributed fusion steady-state Kalman filter, and is far higher than that of each local estimator. A numerical example shows that the covariance intersection fusion Kalman filter has enough fused accuracy without computing the cross-covariance

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

Distributed Kalman filtering under partially heterogeneous models

Tato práce se zabývá problémem distribuovaného Kalmanovského filtrování při částečně heterogenních modelech. Je navrhnuta modifikace existujícího difuzního Kalmanova filtru, umožňující v difuzních sítích použití částečně heterogenních modelů. Výkon méně komplexních modelů je také zvýšen implementací heuristiky umožńující detekci selhávajících uzlů sítě, selhávající uzly jsou restartovány a je jim dána šance se zotavit.This thesis explores the problem of distributed Kalman filtering under partially heterogeneous models. A modification to the existing diffusion Kalman filter is proposed, enabling the employment of partially heterogeneous models in the diffusion networks. The performance of the less complex models is futher improved by the implementation of a node failure detection heuristic, resetting the failling nodes, and giving them a chance at a recovery