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- W4386158805 abstract "Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A truncation procedure has also been provided that leverages a stochastic Gibbs sampler to truncate the birth density for scalability. In this work, we introduce a deterministic herded Gibbs sampling truncation solution for efficient multi-sensor adaptive track initialization. Removing the stochastic behavior of the track initialization procedure without impacting average tracking performance enables a more robust tracking solution more suitable for safety-critical applications. Simulation results for linear sensing scenarios are provided to verify performance." @default.
- W4386158805 created "2023-08-26" @default.
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- W4386158805 date "2023-06-28" @default.
- W4386158805 modified "2023-09-23" @default.
- W4386158805 title "Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets" @default.
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- W4386158805 doi "https://doi.org/10.23919/fusion52260.2023.10224180" @default.
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