Results 61 to 70 of about 480,134 (154)
A Labeled Multi‐Bernoulli Filter Based on Maximum Likelihood Recursive Updating
IET Signal Processing, Volume 2024, Issue 1, 2024.A labeled multi‐Bernoulli filter is used to obtain estimates of the identities and states of targets in complex environments. However, when tracking multiple targets in dense clutters, the computational complexity of the traditional labeled multi‐Bernoulli filter will increase exponentially. A labeled multi‐Bernoulli tracking algorithm based on maximum
Yuhan Song +5 more
wiley +1 more source
Comparisons of PHD Filter and CPHD Filter for Space Object Tracking
, 2014 The Probability Hypothesis Density (PHD) filter and the Cardinalized PHD (CPHD) filter are two computationally tractable approximate Bayesian multiobject filters within the Finite Set Statistics framework. The PHD filter estimates the intensity function;
Früh, Carolin +2 more
core
International Journal of Aerospace Engineering, Volume 2024, Issue 1, 2024.Considering the characteristics of Multi Aircraft Attack and Defense (MAVAD), the problem of multisensor multitarget passive tracking is researched in this article. Firstly, to solve the problem posed by incomplete or unknown knowledge about the intensity of the target’s newborns, a practical measurement–driven method of adaptive generating newborn ...
Runle Du +5 more
wiley +1 more source
Random finite sets based UPF-CPHD multi-object tracking [PDF]
, 2012 A multiple tracking method based on UPF-CPHD was proposed,in which the state and observation of the object were both described by the random finite sets (RSF).The CPHD algorithm was also introduced into the UPF framework to simultaneously deduce the ...
Xin WANG, Yu MA, Hui-bin WANG, Zhe CHEN
core +1 more source
Probability hypothesis density filtering for real-time traffic state estimation and prediction [PDF]
, 2013 The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an
Mihaylova, Lyudmila +3 more
core +2 more sources
Background agnostic CPHD tracking of dim targets in heavy clutter
, 2013 Detection and tracking of dim targets in heavy clutter environments is a daunting theoretical and practical problem. Application of the recently developed Background Agnostic Cardinalized Probability Hypothesis Density (BA-CPHD) filter provides a very ...
A. Zatezalo (23447854) +4 more
core +2 more sources
Extended Target Tracking with a Cardinalized Probability Hypothesis Density Filter
, 2011 This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan.
Orguner, Umut +2 more
core +4 more sources
CPHD filter derivation for extended targets
, 2010 This document derives the CPHD filter for extended targets. Only the update step is derived here. Target generated measurements, false alarms and prior are all assumed to be independent identically distributed cluster processes. We also prove here that the derived CPHD filter for extended targets reduce to PHD filter for extended targets and CPHD ...
openaire +2 more sources
Increasing the flexibility of the CPHD filter by forgetting
Filtr CPHD (cardinalized probability hypothesis density) je vylepšením základního filtru PHD, specificky zaměřeným na poskytování stabilnějších odhadů počtu cílů (tj. jejich kardinality).
Marek Bína
core
Whole Exome Sequencing Points towards a Multi-Gene Synergistic Action in the Pathogenesis of Congenital Combined Pituitary Hormone Deficiency. [PDF]
Cells, 2022 Sertedaki A +7 more
europepmc +1 more source