Results 11 to 20 of about 634,036 (142)
A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter [PDF]
, 2012 Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control.
Romain Billot +9 more
core +3 more sources
A particle filter for freeway traffic estimation [PDF]
, 2004 This paper considers the traffic flow estimation problem for the purposes of on-line traffic prediction, mode detection and ramp-metering control. The solution to the estimation problem is given within the Bayesian recursive framework. A particle filter (
Mihaylova, Lyudmila +7 more
core +3 more sources
Bayes-Optimal Set-Valued Tracking of Single Point Targets
IEEE Access, 2021 Conventional single-point-target tracking algorithms are recursive point estimators with point-measurement input data. In less well-known approaches, the tracking algorithm is a recursive set estimator with point-measurement or set-measurement input data.
Ronald Paxton-Sheets Mahler
doaj +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 +9 more
core +3 more sources
The Pairwise-Markov Bernoulli Filter
IEEE Access, 2020 The Bernoulli filter is a general, Bayes-optimal solution for tracking a single disappearing and reappearing target, using a sensor whose observations are corrupted by missed detections and a general, known clutter process.
Ronald Mahler
doaj +1 more source
Dual-Band Tunable Recursive Active Filter [PDF]
, 2011 This letter presents a novel recursive active filter topology that provides dual-band performance, with independent tuning capability in both bands. The dual-band operation is achieved by using two independent feedback lines.
García Pérez, Oscar Alberto +7 more
core +1 more source
A Concept of Approximated Densities for Efficient Nonlinear Estimation
EURASIP Journal on Advances in Signal Processing, 2002 This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter.
Virginie F. Ruiz
doaj +1 more source
Convergence of the SMC implementation of the PHD filter [PDF]
, 2005 The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem.
Singh, Sumeetpal S. (Sumeetpal Sidhu) +9 more
core +1 more source
Applying Bayes linear methods to support reliability procurement decisions [PDF]
, 2008 Bayesian methods are common in reliability and risk assessment, however, such methods often demand a large amount of specification and can be computationally intensive.
Bedford, Tim +3 more
core +3 more sources
Estimation and control using sampling-based Bayesian reinforcement learning
IET Cyber-Physical Systems, 2019 Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modelling errors.
Patrick Slade +3 more
doaj +1 more source