Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches
This paper deals with model-based approaches for joint target tracking and identification. In a Bayesian framework, parametric state-space model classes are introduced as a generalization of the widespread state-space models. In addition to the dynamic state, they include a hyper-parameter, which takes into account target features or behaviors. For such model classes, sequential Monte Carlo approaches, also known as particle filtering, provide a powerful tool to perform sequentially on-line estimation and model selection. The paper focuses on the ergodicity concern of fixed hyper-parameter estimation and model selection. Indeed, the infinite memory of such a system may lead to the particle filter degeneracy or divergence. It reviews various methods to solve this problem, from the common and basic trick of adding an artificial noise to more complex methods, such as the introduction of reversible jump Markov chain Monte Carlo moves.