Decentralised data fusion with particles

We aim to solve the problem of consistent Decentralised Data Fusion (DDF) with particle filters by a transformation of the sample statistics to a dierent representation that maintains an accurate summary of the particles. Two methodologies are proposed. The first method is a transformation of the particle representation to a Gaussian Mixture Model (GMM). The second algorithm approximates the particles by a Parzen representation. The two algorithms proposed dier in the accuracy of representing the particles as well as the accuracy of fusion methods and the bandwidth requirements. Our simulations results show that a transformation to GMMs requires less components and provides a more accurate summary compared to Parzen representations. However, the decentralised fusion solution for Parzen representations is more accurate than the solution for GMMs.

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