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Namely, those involve independent tracking of multiple objects with a Kalman filter assigned to each object (which will be referred to as KF-based tracking), or joint tracking using PF based on importance sampling (shortly, SIS-based tracking).
We can conveniently categorize these population-based methods into (a) MCMC type of methods and (b) methods based on importance sampling and resampling ideas.
The three techniques are all essentially based on importance sampling.
A PF approach [7], i.e., a Monte Carlo approach based on importance sampling, is pursued.
Indeed, compared to traditional particle filtering based on importance sampling, the processing load of sampling is largely lightened.
We now propose an alternative SMC method for the unknown ARMA parameter case too, based on importance sampling principles.
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PF is a sequential Monte Carlo methodology based on sequential importance sampling (SIS).
A tool based on the importance sampling method is used to reduce the computational effort in determining the magnitude of intentional mistuning.
For navigation systems, an estimator based on sequential importance sampling (SIS) methods (particle filtering) was proposed in [20], which was shown to successfully mitigate multipath in a static channel scenario.
Attention is focused on importance sampling strategies based on the application of Girsanov's transformation method.
Here, we consider the standard particle filter (SPF) based on the sampling importance resampling (SIR) concept [7].
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