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Measurement selection and likelihood evaluation.
Basically, in the PF operation loop two steps must be defined: likelihood evaluation and particles propagation.
They have a mechanistic interpretation and allow for analytic manipulations that greatly reduce the numerical cost of likelihood evaluation.
The HMM-based speech recognizer is adapted to accommodate MDT by modifying the state likelihood evaluation as outlined below.
The recursion includes Track state propagation Measurement selection and likelihood evaluation Multitarget data association Track state update.
When implementing an annealed PF, several issues must be addressed: initialization, likelihood evaluation, particle propagation, and occlusion management.
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These likelihood evaluations lead to a computationally expensive implementation.
The likelihood evaluations imply solving many constrained least squares (CLSQ) optimization problems.
An urgent challenge in these research domains is to develop methods that can estimate the posterior with few likelihood evaluations.
In the initial stage, SaTScan is first used to mark the candidate cluster areas according to the significance of their maximum likelihood evaluations.
Hence, in MC MDT, solving the CLSQ problem of BG k is replaced by L k likelihood evaluations, where L k is the cardinality of Ω k.
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