The unified cardinalized probability hypothesis density (CPHD) filters for extended targets and unresolved targets are proposed. The theoretically rigorous measurementupdate equations for the proposed filters are derived according to the theory of random finite set (RFS) and finite-set statistics (FISST). By assuming that the predicted distributions of the extended targets and unresolved target...

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The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed. The first of which, called the Particle PHD filter, req...

The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally impleme...

This paper develops a box-particle implementation of cardinalized probability hypothesis density filter to track multiple targets and estimate the unknown number of targets. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. In box-particle filter the huge number of traditional point observations is instead by...

The Probability Hypothesis Density (PHD) filter has been recently received a lot of attention by the estimation and data fusion community for its ability to provide a useful solution to the Bayesian filter problem (i.e., implementation issue). Its core foundation to other parallel directions, such as the Sequential Monte Carlo PHD, the Gaussian Mixture PHD and others, offers a viable path to pr...

Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology science. In this work, we investigate the fusion multiple probability density functions (pdfs) continuous random variable or vector. Although case variables problem pdf frequently arise multisensor processing, statistical inference, machine learning, universally accep...

Background The risk-adjusted mortality rate (RAMR) is used widely by healthcare agencies to evaluate hospital performance. The RAMR is insensitive to case volume and requires a confidence interval for proper interpretation, which results in a hypothesis testing framework. Unfamiliarity with hypothesis testing can lead to erroneous interpretations by the public and other stakeholders. We argue t...

In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show that both the PHD and CPHD filters fit in the context of assumed density filtering and implicitly perform Kullback-Leibler divergence (KLD) minimisations after the prediction and update ste...