In several remote sensing applications, multitarget detection/tracking (D/T) of the backscattered wavefields is a very demanding task. Wavefield signals, sampled by an array of sensors, can be described by an hidden Markov model (HMM). As a consequence, the time of delay (TOD) profiles for each of the wavefield (or target) can be estimated by any of the known methods for state-sequence estimation such as the Viterbi (VA) and the backward/forward (BFA) algorithms. Some assumptions, that arise in the wavefield separation problem, allow one to include some additional constraints that preserve the target/tracker association. When an improved resolution is required, the choice of the multitarget Viterbi algorithm (MVA) is mandatory even if its complexity increases exponentially.
Multitarget detection/tracking based on hidden Markov models
V Rampa;
2000
Abstract
In several remote sensing applications, multitarget detection/tracking (D/T) of the backscattered wavefields is a very demanding task. Wavefield signals, sampled by an array of sensors, can be described by an hidden Markov model (HMM). As a consequence, the time of delay (TOD) profiles for each of the wavefield (or target) can be estimated by any of the known methods for state-sequence estimation such as the Viterbi (VA) and the backward/forward (BFA) algorithms. Some assumptions, that arise in the wavefield separation problem, allow one to include some additional constraints that preserve the target/tracker association. When an improved resolution is required, the choice of the multitarget Viterbi algorithm (MVA) is mandatory even if its complexity increases exponentially.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.