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- W2891343441 abstract "This paper studies target classification by using both feature data and kinematic measurements. The problem is tackled in a distributed architecture, where local deciders make decisions based on their local data and the center fuses local decisions, both by a multi-hypothesis sequential test. We adopt the matrix sequential probability ratio test (SPRT) for the local tests and the fusion rule. The centralized fusion and distributed fusion are compared. A lower bound on the overall average sample number of distributed fusion is presented to help determine the thresholds of the local tests to improve the performance of distributed fusion. Numerical results are provided to demonstrate the performance of our algortthm." @default.
- W2891343441 created "2018-09-27" @default.
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- W2891343441 date "2018-07-01" @default.
- W2891343441 modified "2023-09-25" @default.
- W2891343441 title "Distributed Multi-Hypothesis Sequential Test for Tracking-Aided Target Classification" @default.
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- W2891343441 doi "https://doi.org/10.23919/icif.2018.8455844" @default.
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