Results 31 to 40 of about 4,346 (236)
Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty [PDF]
, 2012 This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty.
Gning, Amadou +5 more
core +1 more source
Remote Sensing, 2022 In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets
Chenghu Cao, Yongbo Zhao
doaj +1 more source
Particle filter implementation of the multi-Bernoulli filter for superpositional sensors [PDF]
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 The multi-Bernoulli filter is a promising method for computationally efficient and accurate multi-target tracking. Computationally tractable approximations of the multi-Bernoulli filter equations for superpositional sensors were recently derived. In this paper we present a particle filter implementation of these approximate update filter equations.
Santosh Nannuru, Mark Coates
openaire +1 more source
Intent Arabic text categorisation based on different machine learning and term frequency
IET Networks, EarlyView., 2022 Abstract The complexity of Internet network configurations has made managing networks a complicated undertaking. Intent‐Based Networking (IBN) is a potential solution to this issue. In contrast to conventional networks, where a concrete description of the settings typically conveys a network administrator's goal kept on each device, an administrator's ...
Mohammad Fadhil Mahdi +1 more
wiley +1 more source
Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Sensors, 2017 We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association.
Anthony Hoak +2 more
doaj +1 more source
Distributed Bernoulli Filtering Using Likelihood Consensus [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2019 We consider the detection and tracking of a target in a decentralized sensor network. The presence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. The state-evolution model and the measurement model may be nonlinear and non-Gaussian.
Papa, Giuseppe +4 more
openaire +2 more sources
Interaction-Aware Labeled Multi-Bernoulli Filter
IEEE Transactions on Intelligent Transportation Systems, 2023 13 pages including references, 9 figures, submitted and undergoing second round of review with IEEE Transactions on Intelligent Transportation Systems (ITS)
Nida Ishtiaq +3 more
openaire +2 more sources
Cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization
智能科学与技术学报, 2023 In the complex indoor environment, the traditional SLAM method based on random finite set theory has the problems of low robot pose accuracy and large amount of calculation.To solve these problems, a cardinalized balanced multi-Bernoulli filter SLAM ...
Zijing ZHANG, Fei ZHANG
doaj
IEEE Access, 2018 The cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter is a promising solution for multi-target tracking. However, the performance of the CBMeMBer filter will be degraded severely by outliers in the presence of heavy-tailed process noise
Mingjie Wang +3 more
doaj +1 more source
Bernoulli Race Particle Filters
, 2019 19 ...
Schmon, S, Deligiannidis, G, Doucet, A
openaire +4 more sources