Results 31 to 40 of about 3,989 (204)

Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics

Measurement + Control, 2021
Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number.
Weijun Xu
doaj   +1 more source

Faà di Bruno’s formula and spatial cluster modelling [PDF]

, 2013
The probability generating functional (p.g.fl.) provides a useful means of compactly representing point process models. Cluster processes can be described through the composition of p.g.fl.s, and factorial moment measures and Janossy measures can be ...
Clark, Daniel E., Houssineau, Jeremie
core   +1 more source

The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability

IEEE Access, 2020
The existing Probability Hypothesis Density (PHD) filters with birth intensity estimation only operate on single or two consecutive scan data for multi-target tracking.
Qian Zhu, Tao Li, Jiameng Pan, Qinglong Bao   +3 more
doaj   +1 more source

Auxiliary particle implementation of the probability hypothesis density filter

, 2008
Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior ...
Simon Godsill, Godsill, S, Nick Whiteley, Whiteley, NP, Singh, S, Sumeetpal Singh   +5 more
core   +2 more sources

Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

IEEE Access, 2020
Aiming at improving the tracking performance of the delta-generalized labeled multi-Bernoulli (δ-GLMB) filter, we present a one time step lagged δ-GLMB smoother in this work, which also inherently outputs targets trajectories and differs ...
Guolong Liang, Quanrui Li, Bin Qi, Longhao Qiu   +3 more
doaj   +1 more source

Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities

IEEE Access, 2020
The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association.
Feihu Zhang, Le Li, Lichuan Zhang, Guang Pan   +3 more
doaj   +1 more source

Box-particle probability hypothesis density filtering [PDF]

, 2014
This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets.
Gning, Amadou, Mihaylova, Lyudmila, Cremers, Daniel, Gning, A., Koch, Wolfgang, Cremers, D., Koch, Wofgang, Mihaylova, L., Koch, W., Schikora, Marek, Schikora, M.   +10 more
core   +2 more sources

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors, 2021
Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene.
Zhentao Hu, Linlin Yang, Yong Jin, Han Wang, Shibo Yang   +4 more
doaj   +1 more source

Multi-Target Tracking for SMARTnet: Multi-Layer Probability Hypothesis Filter for Near-Earth Object Tracking [PDF]

, 2021
In this paper, a modified version of the finite set statistics-based Probability Hypothesis Density (PHD) filter is developed specifically for the optical multi-target tracking of objects in the near-Earth realm for Space Situational Awareness (SAA).
Fiedler, H., Schildknecht, Thomas, Frueh, C., Schildknecht, T., Fiedler, Hauke, Herzog, J., Herzog, Johannes, Frueh, Carolin   +7 more
core  

"Spooky action at a distance" in the cardinalized probability hypothesis density filter

, 2022
S.1657-1664The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter.
Franken, D., Schmidt, M., Ulmke, M.
core   +1 more source