Paper
3 April 2000 Truncated Dempster-Shafer optimization and benchmarking
Pierre Valin, David Boily
Author Affiliations +
Abstract
The Dempster-Shafer (DS) evidential scheme is notoriously CPU-intensive and requires a truncation mechanism for real- time operation within a realistic Multi-Sensor Data Fusion (MSDF) system. A truncation scheme consisting of at least 4 parameters has previously been proposed and shown to work well in a limited set of naval and airborne scenarios. The present study considerably expands the realism of the generated airborne scenarios (by using a simulator with ground truth), expands the related platform and emitter databases, benchmarks the CPU loading, optimizes the values of the parameters by requiring faster convergence to a single correct platform identification, and computes relevant Measures of Performance. It also compares the truncated DS scheme's method of ordering the propositions for the MSDF operator to other schemes such as possibility theory, plausibility decision rules, and the Expected Utility Interval approach. Most parameters are found to vary the database size and independence of sensor reports. In particular the need to keep more propositions than previously reported is quantified and schemes to dynamically adjust this number are proposed. The relevant thresholds also have to be simultaneously decreased as the database size increases. Furthermore the minimum amount of ignorance has to be kept at an appropriate level to recover from countermeasures included in some scenarios, or from badly trained ship classifiers.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pierre Valin and David Boily "Truncated Dempster-Shafer optimization and benchmarking", Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); https://doi.org/10.1117/12.381636
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CITATIONS
Cited by 11 scholarly publications.
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KEYWORDS
Synthetic aperture radar

Electronic support measures

Databases

Detection and tracking algorithms

Algorithm development

Data fusion

Sensors

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