منابع مشابه
Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter
The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effec...
متن کاملHybrid multi-Bernoulli CPHD filter for superpositional sensors
We propose, for the superpositional sensor scenario, a hybrid between the multi-Bernoulli filter and the cardinalized probability hypothesis density (CPHD) filter. We use a multi-Bernoulli random finite set (RFS) to model existing targets and we use an independent and identically distributed cluster (IIDC) RFS to model newborn targets and targets with low probability of existence. Our main cont...
متن کاملAmplitude-Aided CPHD Filter for Multitarget Tracking in Infrared Images
The cardinalized probability hypothesis density (CPHD) filter is a powerful tool for multitarget tracking (MTT). However, conventional CPHD filter discriminates targets from clutter only via the motion information, which is not reasonable in the situation of dense clutter. In the tracking, the amplitude of target returns is usually stronger than those coming from clutter, so the amplitude infor...
متن کاملMulti-Bernoulli filter and hybrid multi-Bernoulli CPHD filter for superpositional sensors
Superpositional sensor model can characterize the observations in many different applications such as radio frequency tomography, acoustic sensor network based tracking and wireless communications. In this paper we present two filters based on the random finite set (RFS) theory the multi-Bernoulli filter and its variant the hybrid multi-Bernoulli CPHD filter for superpositional sensors. We prov...
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For the multisensor stochastic control systems with different measurement matrices and correlated noises, a new weighted measurement fusion (WMF) estimation algorithm is presented by using full-rank decomposition of matrix and weighted least squares theory. The newly presented algorithm can handle the fused filtering, smoothing, and prediction problems for the state in a unified framework and p...
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ژورنال
عنوان ژورنال: IEEE Transactions on Aerospace and Electronic Systems
سال: 2016
ISSN: 0018-9251
DOI: 10.1109/taes.2016.150265