Nonlinear Information Filtering for Distributed Multisensor Data Fusion

The information filter has evolved into a key tool for distributed and decentralized multisensor estimation and control. Essentially, it is an algebraical reformulation of the Kalman filter and provides estimates on the information about an uncertain state rather than on a state itself. Whereas many practicable Kalman filtering techniques for nonlinear system and sensor models have been developed, approaches towards nonlinear information filtering are still scarce and limited. In order to deal with nonlinear systems and sensors, this paper derives an approximation technique for arbitrary probability densities that provides the same distributable fusion structure as the linear information filter. The presented approach not only constitutes a nonlinear version of the information filter, but it also points the direction to a Hilbert space structure on probability densities, whose vector space operations correspond to the fusion and weighting of information

Markov chain distributed particle filters (mcdpf

Abstract-Distributed particle filters (DPF) are known to provide robustness for the state estimation problem and can reduce the amount of information communication compared to centralized approaches. Due to the difficulty of merging multiple distributions represented by particles and associated weights, however, most uses of DPF to date tend to approximate the posterior distribution using a parametric model or to use a predetermined message path. In this paper, the Markov Chain Distributed Particle Filter (MCDPF) algorithm is proposed, based on particles performing random walks across the network. This approach maintains robustness since every sensor only needs to exchange particles and weights locally and furthermore enables more general representations of posterior distributions because there are no a priori assumptions on distribution form. The paper provides a proof of weak convergence of the MCDPF algorithm to the corresponding centralized particle filter and the optimal filtering solution, and concludes with a numerical study showing that MCDPF leads to a reliable estimation of the posterior distribution of a nonlinear system

Formation Control Driven by Cooperative Object Tracking

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles

State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties

State estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-membership uncertainties can be taken into consideration simultaneously. Different solutions for implementing these estimation algorithms in distributed networked systems are presented