Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

Approximate Decentralized Bayesian Inference

This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents. The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines i...

Decentralized Approximate Bayesian Inference for Distributed Sensor Network

Bayesian models provide a framework for probabilistic modelling of complex datasets. Many such models are computationally demanding, especially in the presence of large datasets. In sensor network applications, statistical (Bayesian) parameter estimation usually relies on decentralized algorithms, in which both data and computation are distributed across the nodes of the network. In this paper ...

Decentralized data fusion with inverse covariance intersection

In distributed and decentralized state estimation systems, fusion methods are employed to systematically combine multiple estimates of the state into a single, more accurate estimate. An often encountered problem in the fusion process relates to unknown common information that is shared by the estimates to be fused and is responsible for correlations. If the correlation structure is unknown to ...

Fusion of Heterogeneous Multisensor Data

One of the main future tasks in the field of reconnaissance and surveillance is the data fusion of heterogeneous systems. For this purpose, sensor data of airborne and spaceborne systems, which acquire the data from the same scene with different spatial and spectral coverage, are used. The employment of different sensors offers the opportunity to take benefit of the specific sensor characterist...

Exact and approximate . . .