Efficient use of particle filters for multi-source data fusion in decentralized sensor networks.

Abstract

A highly‐scalable Bayesian approach to the problem of performing multi‐source data fusion and target tracking in decentralized sensor networks is presented. Previous applications of decentralized data fusion have generally been restricted to uni‐modal/uni‐source sensor networks using Gaussian based approaches, such as the Kalman or information filter. However, with recent interest to employ complex, multi‐modal/multi‐source sensors which potentially exhibit observation and/or process non‐linearities along with non‐Gaussian distributions, the need to develop a more generalized and scalable method of decentralized data fusion using particle filters is required. The probabilistic approach featured in this work provides the ability to seamlessly integrate and efficiently fuse multi‐source sensor data in the absence of any linearity and/or normality constraints. The architecture is fully decentralized and provides a methodology that scales extremely well to any growth in the number of targets or region of coverage. This multi‐source data fusion architecture is capable of providing high‐precision tracking performance in complex, non‐linear/non‐Gaussian operating environments. In addition, the architecture provides an unprecedented scaling capability for decentralized sensor networks as compared to similar architectures which communicate information using particle data, Gaussian mixture models, or Parzen density estimators.