7 research outputs found
High level data fusion
Get PDFWe address the question of how to obtain effective fusion of identification information such that it is robust to the quality of this information. As well as technical issues data fusion is encumbered with a collection of (potentially confusing) practical considerations. These considerations are described during the early chapters in which a framework for data fusion is developed. Following this process of diversification it becomes clear that the original question is not well posed and requires more precise specification. We use the framework to focus on some of the technical issues relevant to the question being addressed. We show that fusion of hard decisions through use of an adaptive version of the maximum a posteriori decision rule yields acceptable performance. Better performance is possible using probability level fusion as long as the probabilities are accurate. Of particular interest is the prevalence of overconfidence and the effect it has on fused performance. The production of accurate probabilities from poor quality data forms the latter part of the thesis. Two approaches are taken. Firstly the probabilities may be moderated at source (either analytically or numerically). Secondly, the probabilities may be transformed at the fusion centre. In each case an improvement in fused performance is demonstrated. We therefore conclude that in order to obtain robust fusion care should be taken to model the probabilities accurately; either at the source or centrally
Quantisation for Probability-Level Fusion on a Bandwidth Budget
No full textResults are established for a simulated data fusion architecture featuring a synthetic two-class Gaussian problem, with Bayesian recognisers. The recognisers output posterior probabilities for each class. The probabilities from two or more recognisers of identical error rate are quantised using the nearest-neighbour coding rule. The coded values are decoded at a fusion centre and fused. A decision is made from the fused probabilities. The performance of the architecture is examined experimentally using code values that are uniformly distributed and code values that are produced using the Linde-Buzo-Grey (LBG) algorithm. Results are produced for two to six sensors and two to 32 code values. These results are compared to fusing probabilities represented using 32 bit floating-point numbers. Using 32 uniform or LBG-produced code values, produces results that are at most only 1% worse than fusing the uncoded probabilities. Keywords: data fusion, quantisation, architecture 1. INTRODUCTION ..
The Importance of Models in Bayesian Data Fusion
No full textIntroduction Mark D. Bedworth Anthony J. R. Heading Presented at the 1st IEEE Conference on Control Applications, Dayton, Ohio, USA September 13-16 1992. and Defence Research Agency St. Andrews Road, Great Malvern, WR14 3PS, England. Telephone: +44 684 894521 Email: [email protected] One source of errors in automatic data fusion systems is examined for the simplest case in which the separate sensors supply independent information. Despite the apparent simplicity of this scenario, improvements in performance can still be made over the currently used methods. A theoretical technique is worked through and an approximation to it assessed. Experimental results are given both for synthetic Gaussian data and for a real data fusion problem involving ship silhouette recognition. Although data collection has to a large extent been automated during the past few decades, the task of collation and combination of data has been left primarily to people. Multi-sensor systems with automatic patt..
