Results 121 to 130 of about 3,989 (204)

Particle flow for sequential Monte Carlo implementation of probability hypothesis density

, 2017
Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the ...
Wang, Wenwu, Liu, Yang, Zhao, Y
core   +1 more source

Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Journal of Aerospace Technology and Management
Gaussian mixture probability hypothesis density (GM-PHD) filtering often assumes a uniform distribution of clutter in the observation area. However, in practice, clutter is often unknown and non-uniform, necessitating accurate estimation of its ...
Lifan Sun, Wenhui Xue, Dan Gao
doaj  

Automated extraction of dolphin whistles- a Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) approach

The need for automated methods to detect and extract marine mammal vocalizations from acoustic data has increased in the last few decades due to the increased availability of long-term recording systems.
Gruden, Pina, White, Paul
core   +1 more source

Multi-Target Particle Filtering for the Probability Hypothesis Density

, 2008
– When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter ...

core  

Set-membership PHD filter

, 2013
The paper proposes a novel Probability Hypothesis Density (PHD) filter for linear system in which initial state, process and measurement noises are only known to be bounded (they can vary on compact sets, e.g., polytopes).
Papi, Francesco, PAPI Francesco, Benavoli, A., BENAVOLI Alessio   +3 more
core  

Birth Density Modeling in Multi-target Tracking Using the Gaussian Mixture PHD Filter

, 2008
A recently established method for multi-target tracking which both estimates the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false ...
Zhu, Min, Chen, Rongrong
core  

Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach

, 2010
Motion tracking is an important part of the Intelligent Vision Agent System, IVAS. In this thesis, the Gaussian mixture approximation of the Probability Hypothesis Density filter (GM-PHD) was implemented to provide a reliable and computationally ...
OYEKANLU, Emmanuel- Adebomi, ONIDARE, Samuel-Olusayo   +1 more
core  

Cluster Target Tracking Based on Multi-Sensor Adaptive GLMB Filter. [PDF]

Sensors (Basel)
Zhang Z, Wei D, Xue X.
europepmc   +1 more source

Plenary Abstracts Session & Oral Presentations

HemaSphere, Volume 10, Issue S1, June 2026.
wiley   +1 more source

Multiple model trajectory poisson multi-bernoulli mixtures filter for tracking multiple maneuvering objects. [PDF]

Sci Rep
Salim I, Okasha N, Barbary M, Anis W.
europepmc   +1 more source