On Minimax Robust Data Fusion
Abstract
In this paper, minimax robust data fusion schemes based on discrete-time observations with statistical uncertainty aie considered. The observations are assumed to be i.i.d and the decisions of all sensors independent when conditioned on the either of two hypotheses. The statistics of the observations are only known to belong to uncertainty classes deternined by 2-alternating Choquet capacities. Both cases of fixed-sample-size (block) data fusion and sequential data fusion are examined. For specific performance measures, three robust fusion rules: suboptimal, optimal and asymptotically optimal --as the number of sensors increases--are derived for the block data fusion case, and an asymptotically robust fusion rule is derived for the sequential data fusion case; these fusion rules are optimal in the class of rules employing likelihood ratio tests. In all situations the robust ftsion rule makes use of likelihood ratios and thresholds which depend on the least-favorable probability distributions in the uncertainty class. In the limit of a large ruimber of sensors, it is shown that the same threshold can be used by all sensors, which in turn simplifies the overall computation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1988
- Accession Number
- ADA454730
Entities
People
- E. Geraniotis
- Y. A. Chau
Organizations
- Weill Cornell Medicine