Kalman Filter and Joint Tracking and Classification in the TBM framework
نویسندگان
چکیده
The paper presents an approach to joint tracking and classification based on belief functions as understood in the transferable belief model (TBM). For the tracking phase, a Kalman filter in the TBM framework is derived. This filter is essentially the same as the classical Kalman filter with a diffuse prior, although it is derived in a more general context. For the classification phase, the TBM solution provides more reasonable results than the corresponding Bayesian classifier in situations where no one-toone mapping between target behaviours and classes can be established.
منابع مشابه
Kalman filter and joint tracking and classification based on belief functions in the TBM framework
The paper presents an approach to joint tracking and classification based on belief functions as understood in the transferable belief model (TBM). The TBM model is identical to the classical model except all probability functions are replaced by belief functions, which are more flexible for representing uncertainty. It is felt that the tracking phase is well handled by the classical Kalman fil...
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