The Experts below are selected from a list of 227244 Experts worldwide ranked by ideXlab platform
Jim Huang - One of the best experts on this subject based on the ideXlab platform.
-
Generalized Gromov method for stochastic Particle Flow filters
Signal Processing Sensor Information Fusion and Target Recognition XXVI, 2017Co-Authors: Fred Daum, Jim Huang, Arjang NoushinAbstract:We describe a new algorithm for stochastic Particle Flow filters using Gromov’s method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic Particle Flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for Particle Flow corresponding to Bayes’ rule.
-
Numerical experiments for Gromov’s stochastic Particle Flow filters
Signal Processing Sensor Information Fusion and Target Recognition XXVI, 2017Co-Authors: Fred Daum, Arjang Noushin, Jim HuangAbstract:We show the results of numerical experiments for a new algorithm for stochastic Particle Flow filters designed using Gromov’s method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic Particle Flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for Particle Flow corresponding to Bayes’ rule.
-
MFI - Gromov's method for Bayesian stochastic Particle Flow: A simple exact formula for Q
2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016Co-Authors: Fred Daum, Jim Huang, Arjang NoushinAbstract:We describe several new algorithms for stochastic Particle Flow using Gromov's method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic Particle Flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for Particle Flow corresponding to Bayes' rule.
-
Proof that Particle Flow corresponds to Bayes’ rule: necessary and sufficient conditions
Signal Processing Sensor Information Fusion and Target Recognition XXIV, 2015Co-Authors: Fred Daum, Jim HuangAbstract:We prove a theorem that guarantees the existence of a Particle Flow corresponding to Bayes’ rule, assuming certain regularity conditions (smooth and nowhere vanishing probability densities). This theory applies to Particle Flows to compute Bayes’ rule for nonlinear filters, Bayesian decisions and learning as well as transport. The Particle Flow algorithms reduce computational complexity by orders of magnitude compared with standard Markov chain Monte Carlo (MCMC) algorithms that achieve the same accuracy for high dimensional problems.
-
Particle Flow inspired by Knothe-Rosenblatt transport for nonlinear filters
Signal Processing Sensor Fusion and Target Recognition XXII, 2013Co-Authors: Fred Daum, Jim HuangAbstract:We derive a new algorithm for Particle Flow corresponding to Bayes’ rule that was inspired by Knothe- Rosenblatt transport, which is well known in transport theory. We emphasize that our Flow is not Knothe- Rosenblatt transport, but rather it is a completely different algorithm for Particle Flow. In particular, we pick a nearly upper triangular Jacobian matrix, but the meaning of the word “Jacobian” as used here is completely different than used in Knothe-Rosenblatt transport.
M. A. Thomson - One of the best experts on this subject based on the ideXlab platform.
-
Software compensation in Particle Flow reconstruction
The European physical journal. C Particles and fields, 2017Co-Authors: Huong Lan Tran, M. A. Thomson, John Marshall, K. Krüger, F. Sefkow, S. Green, Frank SimonAbstract:The Particle Flow approach to calorimetry benefits from highly granular calorimeters and sophisticated software algorithms in order to reconstruct and identify individual Particles in complex event topologies. The high spatial granularity, together with analogue energy information, can be further exploited in software compensation. In this approach, the local energy density is used to discriminate electromagnetic and purely hadronic sub-showers within hadron showers in the detector to improve the energy resolution for single Particles by correcting for the intrinsic non-compensation of the calorimeter system. This improvement in the single Particle energy resolution also results in a better overall jet energy resolution by improving the energy measurement of identified neutral hadrons and improvements in the pattern recognition stage by a more accurate matching of calorimeter energies to tracker measurements. This paper describes the software compensation technique and its implementation in Particle Flow reconstruction with the Pandora Particle Flow Algorithm (PandoraPFA). The impact of software compensation on the choice of optimal transverse granularity for the analogue hadronic calorimeter option of the International Large Detector (ILD) concept is also discussed.
-
Pandora Particle Flow Algorithm
2013Co-Authors: John Marshall, M. A. ThomsonAbstract:A high-energy e + e collider, such as the ILC or CLIC, is arguably the best option to comple- ment and extend the LHC physics programme. A lepton collider will allow for exploration of Standard Model Physics, such as precise measurements of the Higgs, top and gauge sectors, in addition to enabling a multitude of New Physics searches. However, physics analyses at such a collider will place unprecedented demands on calorimetry, with a required jet energy resolution of sE=E. 3:5 %. To meet these requirements will need a new approach to calorimetry. The Particle Flow approach to calorimetry requires both fine granularity detectors and sophis- ticated software algorithms. It promises to deliver unparalleled jet energy resolution by fully reconstructing the paths of individual Particles through the detector. The energies of charged Particles can then be extracted from precise inner detector tracker measurements, whilst photon energies will be measured in the ECAL, and only neutral hadron energies (10% of jet energies) will be measured in the HCAL, largely avoiding the typically poor HCAL resolution. This document introduces the Pandora Particle Flow algorithms, which offer the current state of the art in Particle Flow calorimetry for the ILC and CLIC. The performance of the algorithms is investigated by examining the reconstructed jet energy resolution and the ability to separate the hadronic decays ofW and Z bosons.
-
The Pandora Particle Flow Algorithm
arXiv: Instrumentation and Detectors, 2013Co-Authors: J S Marshall, M. A. ThomsonAbstract:A high-energy e+e- collider, such as the ILC or CLIC, is arguably the best option to complement and extend the LHC physics programme. A lepton collider will allow for exploration of Standard Model Physics, such as precise measurements of the Higgs, top and gauge sectors, in addition to enabling a multitude of New Physics searches. However, physics analyses at such a collider will place unprecedented demands on calorimetry, with a required jet energy resolution of \sigma(E)/E < 3.5%. To meet these requirements will need a new approach to calorimetry. The Particle Flow approach to calorimetry requires both fine granularity detectors and sophisticated software algorithms. It promises to deliver unparalleled jet energy resolution by fully reconstructing the paths of individual Particles through the detector. The energies of charged Particles can then be extracted from precise inner detector tracker measurements, whilst photon energies will be measured in the ECAL, and only neutral hadron energies (10% of jet energies) will be measured in the HCAL, largely avoiding the typically poor HCAL resolution. This document introduces the Pandora Particle Flow algorithms, which offer the current state of the art in Particle Flow calorimetry for the ILC and CLIC. The performance of the algorithms is investigated by examining the reconstructed jet energy resolution and the ability to separate the hadronic decays of W and Z bosons.
-
Performance of Particle Flow Calorimetry at CLIC
Nuclear Instruments and Methods in Physics Research Section A: Accelerators Spectrometers Detectors and Associated Equipment, 2013Co-Authors: John Marshall, A. Münnich, M. A. ThomsonAbstract:The Particle Flow approach to calorimetry can provide unprecedented jet energy resolution at a future high energy collider, such as the International Linear Collider (ILC). However, the use of Particle Flow calorimetry at the proposed multi-TeV Compact Linear Collider (CLIC) poses a number of significant new challenges. At higher jet energies, detector occupancies increase, and it becomes increasingly difficult to resolve energy deposits from individual Particles. The experimental conditions at CLIC are also significantly more challenging than those at previous electron-positron colliders, with increased levels of beam-induced backgrounds combined with a bunch spacing of only 0.5 ns. This paper describes the modifications made to the PandoraPFA Particle Flow algorithm to improve the jet energy reconstruction for jet energies above 250 GeV. It then introduces a combination of timing and pT cuts that can be applied to reconstructed Particles in order to significantly reduce the background. A systematic study is performed to understand the dependence of the jet energy resolution on the jet energy and angle, and the physics performance is assessed via a study of the energy and mass resolution of W and Z Particles in the presence of background at CLIC. Finally, the missing transverse momentum resolution is presented, and the fake missing momentum is quantified. The results presented in this paper demonstrate that high granularity Particle Flow calorimetry leads to a robust and high resolution reconstruction of jet energies and di-jet masses at CLIC.
-
Particle Flow Calorimetry
Journal of Physics: Conference Series, 2011Co-Authors: M. A. ThomsonAbstract:The Particle Flow (PFlow) approach to calorimetry promises to deliver unprecedented jet energy resolution for experiments at future high energy colliders such as the proposed International Linear Collider (ILC). Since Calor 2008 there has been a significant improvement in understanding Particle Flow calorimetry and its potential. This contribution to the proceedings describes the current understanding of high granularity Particle Flow calorimetry in the context of the PandoraPFA algorithm and the ILD detector concept. It is shown that a jet energy resolution is achievable for 40–400GeV jets, demonstrating that high granularity PFlow calorimetry can meet the challenging ILC jet energy resolution goals. The potential of high granularity PFlow calorimetry at a multi-TeV lepton collider, such as CLIC and a possible Muon Collider, is also discussed.
Fred Daum - One of the best experts on this subject based on the ideXlab platform.
-
Generalized Gromov method for stochastic Particle Flow filters
Signal Processing Sensor Information Fusion and Target Recognition XXVI, 2017Co-Authors: Fred Daum, Jim Huang, Arjang NoushinAbstract:We describe a new algorithm for stochastic Particle Flow filters using Gromov’s method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic Particle Flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for Particle Flow corresponding to Bayes’ rule.
-
Numerical experiments for Gromov’s stochastic Particle Flow filters
Signal Processing Sensor Information Fusion and Target Recognition XXVI, 2017Co-Authors: Fred Daum, Arjang Noushin, Jim HuangAbstract:We show the results of numerical experiments for a new algorithm for stochastic Particle Flow filters designed using Gromov’s method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic Particle Flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for Particle Flow corresponding to Bayes’ rule.
-
MFI - Gromov's method for Bayesian stochastic Particle Flow: A simple exact formula for Q
2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016Co-Authors: Fred Daum, Jim Huang, Arjang NoushinAbstract:We describe several new algorithms for stochastic Particle Flow using Gromov's method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic Particle Flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for Particle Flow corresponding to Bayes' rule.
-
Proof that Particle Flow corresponds to Bayes’ rule: necessary and sufficient conditions
Signal Processing Sensor Information Fusion and Target Recognition XXIV, 2015Co-Authors: Fred Daum, Jim HuangAbstract:We prove a theorem that guarantees the existence of a Particle Flow corresponding to Bayes’ rule, assuming certain regularity conditions (smooth and nowhere vanishing probability densities). This theory applies to Particle Flows to compute Bayes’ rule for nonlinear filters, Bayesian decisions and learning as well as transport. The Particle Flow algorithms reduce computational complexity by orders of magnitude compared with standard Markov chain Monte Carlo (MCMC) algorithms that achieve the same accuracy for high dimensional problems.
-
Particle Flow inspired by Knothe-Rosenblatt transport for nonlinear filters
Signal Processing Sensor Fusion and Target Recognition XXII, 2013Co-Authors: Fred Daum, Jim HuangAbstract:We derive a new algorithm for Particle Flow corresponding to Bayes’ rule that was inspired by Knothe- Rosenblatt transport, which is well known in transport theory. We emphasize that our Flow is not Knothe- Rosenblatt transport, but rather it is a completely different algorithm for Particle Flow. In particular, we pick a nearly upper triangular Jacobian matrix, but the meaning of the word “Jacobian” as used here is completely different than used in Knothe-Rosenblatt transport.
Mark Coates - One of the best experts on this subject based on the ideXlab platform.
-
CAMSAP - Particle Flow Particle Filter using Gromov's method
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019Co-Authors: Soumyasundar Pal, Mark CoatesAbstract:Particle Flow filters obtain impressive results in challenging high dimensional, non-linear sequential state estimation problems. In contrast to a Particle filter, which uses importance sampling to approximate the posterior distribution of the state, the Flow based algorithms solve a differential equation to migrate the Particles from the prior to the posterior distribution. However, the Particles after the Flow are not true samples of the posterior distribution due to strong model assumptions required for the derivation of the Flow and the approximations associated with the numerical solution. This affects performance adversely in many highly non-linear, non-Gaussian filtering problems. Particle Flow Particle Filters (PFPF) adapt the Particle Flow procedure to construct a proposal density inside the Particle filter. These techniques can outperform the underlying Particle Flow algorithms by compensating for the approximations in the Flow calculations via update of importance weights after the Flow, at the cost of a negligible increase in the computational complexity. Most of the PFPF approaches have focused on using a deterministic Particle Flow. In this paper, we develop a PFPF algorithm using a stochastic Particle Flow based on Gromov's method. Numerical simulations are conducted to examine when the proposed method offers advantages compared to existing techniques.
-
Particle Flow superpositional GLMB filter
Signal Processing Sensor Information Fusion and Target Recognition XXVI, 2017Co-Authors: Augustin-alexandru Saucan, Mark CoatesAbstract:In this paper we propose a Superpositional Marginalized δ-GLMB (SMδ-GLMB) filter for multi-target tracking and we provide bootstrap and Particle Flow Particle filter implementations. Particle filter implementations of the marginalized δ-GLMB filter are computationally demanding. As a first contribution we show that for the specific case of superpositional observation models, a reduced complexity update step can be achieved by employing a superpositional change of variables. The resulting SMδ-GLMB filter can be readily implemented using the unscented Kalman filter or Particle filtering methods. As a second contribution, we employ Particle Flow to produce a measurement-driven importance distribution that serves as a proposal in the SMδ-GLMB Particle filter. In high-dimensional state systems or for highly- informative observations the generic Particle filter often suffers from weight degeneracy or otherwise requires a prohibitively large number of Particles. Particle Flow avoids Particle weight degeneracy by guiding Particles to regions where the posterior is significant. Numerical simulations showcase the reduced complexity and improved performance of the bootstrap SMδ-GLMB filter with respect to the bootstrap Mδ-GLMB filter. The Particle Flow SMδ-GLMB filter further improves the accuracy of track estimates for highly informative measurements.
-
Particle Filtering With Invertible Particle Flow
IEEE Transactions on Signal Processing, 2017Co-Authors: Yunpeng Li, Mark CoatesAbstract:A key challenge when designing Particle filters in high-dimensional state spaces is the construction of a proposal distribution that is close to the posterior distribution. Recent advances in Particle Flow filters provide a promising avenue to avoid weight degeneracy; Particles drawn from the prior distribution are migrated in the state space to the posterior distribution by solving partial differential equations. Numerous Particle Flow filters have been proposed based on different assumptions concerning the Flow dynamics. Approximations are needed in the implementation of all of these filters; as a result, the Particles do not exactly match a sample drawn from the desired posterior distribution. Past efforts to correct the discrepancies involve expensive calculations of importance weights. In this paper, we present new filters which incorporate deterministic Particle Flows into an encompassing Particle filter framework. The valuable theoretical guarantees concerning Particle filter performance still apply, but we can exploit the attractive performance of the Particle Flow methods. The filters we describe involve a computationally efficient weight update step, arising because the embedded Particle Flows we design possess an invertible mapping property. We evaluate the proposed Particle Flow Particle filters' performance through numerical simulations of a challenging multitarget multisensor tracking scenario and complex high-dimensional filtering examples.
-
ICASSP - Particle Flow SMC delta-GLMB filter
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Augustin-alexandru Saucan, Mark CoatesAbstract:In this paper we derive a Particle Flow Particle filter implementation of the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter. The bootstrap Particle filter δ-GLMB suffers from weight degeneracy for high-dimensional state systems or low measurement noise. In order to avoid weight degeneracy, we employ Particle Flow to produce a measurement-driven importance distribution that serves as a proposal in the δ-GLMB Particle filter. Flow-induced proposals are developed for both types of targets encountered in the δ-GLMB filter, i.e., persistent and birth targets. Numerical simulations reflect the improved performance of the proposed filter with respect to classical bootstrap implementations.
-
FUSION - Fast Particle Flow Particle filters via clustering
2016Co-Authors: Yunpeng Li, Mark CoatesAbstract:Particle Flow filters, introduced in a series of papers by Daum and Huang, are an attractive alternative to Particle filters for filtering tasks in high-dimensional spaces or with very informative measurements. Many variants of Particle Flow filters have been developed, but all require approximations in multiple stages of the implementation, which leads to Particles deviating from the true posterior distribution. To preserve the statistical consistency of the filtering algorithm, some recent papers embed the Particle Flow techniques within a Particle filter, using them to generate a proposal distribution. In recent work, we developed such a Particle Flow Particle filter, modifying the Flow mechanism to ensure that the implemented, approximate Flow was an invertible mapping. This property allows efficient computation of the importance weights. In this paper, we strive to reduce the computational overhead of the Particle Flow Particle filter by incorporating clustering of the Particles. Results from a multi-target acoustic tracking simulation demonstrate that we can significantly reduce the computational cost of Particle Flow Particle filters with a relative small sacrifice in tracking accuracy.
Wenwu Wang - One of the best experts on this subject based on the ideXlab platform.
-
ICASSP - Labelled Non-zero Particle Flow for SMC-PHD Filtering
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Yang Liu, Yuexian Zou, Wenwu WangAbstract:The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter assisted by Particle Flows (PF) has been shown to be promising for audio-visual multi-speaker tracking. A clustering step is often employed for calculating the Particle Flow, which leads to a substantial increase in the computational cost. To address this issue, we propose an alternative method based on the labelled non-zero Particle Flow (LNPF) to adjust the Particle states. Results obtained from the AV16.3 dataset show improved performance by the proposed method in terms of computational efficiency and tracking accuracy as compared with baseline AV-NPF-SMC-PHD methods.
-
Intensity Particle Flow SMC-PHD Filter For Audio Speaker Tracking.
arXiv: Sound, 2018Co-Authors: Yang Liu, Wenwu Wang, Volkan KilicAbstract:Non-zero diffusion Particle Flow Sequential Monte Carlo probability hypothesis density (NPF-SMC-PHD) filtering has been recently introduced for multi-speaker tracking. However, the NPF does not consider the missing detection which plays a key role in estimation of the number of speakers with their states. To address this limitation, we propose to use intensity Particle Flow (IPF) in NPFSMC-PHD filter. The proposed method, IPF-SMC-PHD, considers the clutter intensity and detection probability while no data association algorithms are used for the calculation of Particle Flow. Experiments on the LOCATA (acoustic source Localization and Tracking) dataset with the sequences of task 4 show that our proposed IPF-SMC-PHD filter improves the tracking performance in terms of estimation accuracy as compared to its baseline counterparts.
