Efficient Multi-Source Data Fusion for 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, multimodal/ 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 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 proposed 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 paper will illustrate that our multi-source data fusion architecture is capable of providing high-precision tracking performance in complex, non-linear/non-Gaussian operating environments. In addition, we will also show that our 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2006
- Accession Number
- ADA478867
Entities
People
- Christopher M. Lloyd
- Philip J. Haney