Paper
28 April 2010 Fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories
Pierre Valin, Pascal Djiknavorian, Eloi Bossé, Dominic Grenier
Author Affiliations +
Abstract
We address the problem of fusing ESM reports by two evidential reasoning schemes, namely Dempster-Shafer theory and Dezert-Smarandache theory. These schemes provide results in different frames of discernment, but are able to fuse realistic ESM data. We discuss their advantages and disadvantages under varying conditions of sensor data certainty and fusion reliability, the latter coming from errors in the association process. A thresholded version of Dempster-Shafer theory is fine-tuned for performance across a wide range of values for certainty and reliability. The results are presented first for typical scenarios, and secondly for Monte-Carlo studies of scenarios under varying sensor certainty and fusion reliability. The results exhibit complex non-linear functions, but for which clear trends can nevertheless be extracted. A compromise has to be achieved between stability under occasional miss-associations, and reaction time latency under a real change of allegiance. The alternative way of reporting results through Dezert-Smarandache theory is studied under similar conditions, and shown to provide good results, which are however more dependent on the unreliability, and slightly less stable. In this case however, the frame of discernment is larger, and permits additional interpretations, which are outside the scope of Dempster-Shafer.
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Pierre Valin, Pascal Djiknavorian, Eloi Bossé, and Dominic Grenier "Fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories", Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, 771006 (28 April 2010); https://doi.org/10.1117/12.850287
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KEYWORDS
Sensors

Electronic support measures

Data fusion

Reliability

Data modeling

Monte Carlo methods

Sensor fusion

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