Speedup and tracking accuracy evaluation of parallel particle filter algorithms implemented on a multicore architecture

2010 ◽  
Author(s):  
Olov Rosen ◽  
Alexander Medvedev ◽  
Mats Ekman
Keyword(s):  
Particle Filter ◽  
Accuracy Evaluation ◽  
Tracking Accuracy ◽  
Multicore Architecture ◽  
Filter Algorithms

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

scholarly journals A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries

Energies ◽  
2017 ◽  
Vol 10 (8) ◽  
pp. 1149 ◽  
Author(s):  
Bizhong Xia ◽  
Zhen Sun ◽  
Ruifeng Zhang ◽  
Deyu Cui ◽  
Zizhou Lao ◽  
...  
Keyword(s):  
Comparative Study ◽  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Soc Estimation ◽  
Filter Algorithms

scholarly journals Towards Tumor Margins Reduction: Tracking Accuracy Evaluation of an MRI-RT System

2017 ◽  
Vol 99 (2) ◽  
pp. S124-S125
Author(s):  
D. Cusumano ◽  
L. Boldrini ◽  
F. Cellini ◽  
S. Teodoli ◽  
N. Dinapoli ◽  
...  
Keyword(s):  
Accuracy Evaluation ◽  
Tracking Accuracy ◽  
Tumor Margins

State estimation in bioheat transfer: a comparison of particle filter algorithms

2017 ◽  
Vol 27 (3) ◽  
pp. 615-638 ◽  
Author(s):  
Bernard Lamien ◽  
Leonardo A.B. Varon ◽  
Helcio R.B. Orlande ◽  
Guillermo E. Elicabe
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
The Body ◽  
Computational Time ◽  
Model Parameters ◽  
Hyperthermia Treatment ◽  
Content Type ◽  
Sampling Importance Resampling ◽  
Importance Resampling ◽  
Filter Algorithms

Purpose The purpose of this paper is to focus on applications related to the hyperthermia treatment of cancer, with heating imposed either by a laser in the near-infrared range or by radiofrequency waves. The particle filter algorithms are compared in terms of computational time and solution accuracy. Design/methodology/approach The authors extend the analyses performed in their previous works to compare three different algorithms of the particle filter, as applied to the hyperthermia treatment of cancer. The particle filters examined here are the sampling importance resampling (SIR) algorithm, the auxiliary sampling importance resampling (ASIR) algorithm and Liu & West’s algorithm. Findings Liu & West’s algorithm resulted in the largest computational times. On the other hand, this filter was shown to be capable of dealing with very large uncertainties. In fact, besides the uncertainties in the model parameters, Gaussian noises, similar to those used for the SIR and ASIR filters, were added to the evolution models for the application of Liu & West’s filter. For the three filters, the estimated temperatures were in excellent agreement with the exact ones. Practical implications This work may help medical doctors in the future to prescribe treatment protocols and also opens the possibility of devising control strategies for the hyperthermia treatment of cancer. Originality/value The natural solution to couple the uncertain results from numerical simulations with the measurements that contain uncertainties, aiming at the better prediction of the temperature field of the tissues inside the body, is to formulate the problem in terms of state estimation, as performed in this work.

scholarly journals An automated method for the evaluation of the pointing accuracy of sun-tracking devices

2016 ◽  
Author(s):  
Dietmar J. Baumgartner ◽  
Werner Pötzi ◽  
Heinrich Freislich ◽  
Heinz Strutzmann ◽  
Astrid M. Veronig ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Monitoring System ◽  
Automated System ◽  
Accuracy Evaluation ◽  
Tracking Accuracy ◽  
Clear Sky ◽  
Automated Method ◽  
Pointing Accuracy ◽  
Sky Conditions ◽  
Tracking Devices

Abstract. The accuracy of solar radiation measurements (for direct and diffuse radiation) depends significantly on the precision of the operational sun-tracking device. Thus rigid targets for instrument performance and operation have been specified for international monitoring networks, such as e.g., the Baseline Surface Radiation Network (BSRN) operating under the auspices of the World Climate Research Program (WCRP). Sun-tracking devices fulfilling these accuracy requirements are available from various instrument manufacturers, however none of the commercially available systems comprises an automatic accuracy control system, allowing platform operators to independently validate the pointing accuracy of sun-tracking sensors during operation. Here we present KSO-STREAMS (KSO-SunTRackEr Accuracy Monitoring System), a fully automated, system independent and cost-effective method for evaluating the pointing accuracy of sun-tracking devices. We detail the monitoring system setup, its design and specifications and results from its application to the sun-tracking system operated at the Austrian RADiation network (ARAD) site Kanzelhöhe Observatory (KSO). Results from an evaluation campaign from March to June 2015 show that the tracking accuracy of the device operated at KSO lies for the vast majority of observations (99.8 %) within BSRN specifications (i.e., 0.1° tracking accuracy). Evaluation of manufacturer specified active tracking accuracies (0.02°), during periods with direct solar radiation exceeding 300 W m−2, shows that these are satisfied for 72.9 % of observations. Tracking accuracies are highest during clear-sky conditions and on days where prevailing clear-sky conditions are interrupted by frontal movement: in these cases we obtain complete fulfillment of BSRN requirements and 76.4 % of observations within manufacturer specified active tracking accuracies. Limitations to tracking surveillance arise during overcast conditions and periods of partial solar limb coverage by clouds. On days with variable cloud-cover 78.1 % (99.9 %) of observations meet active tracking (BSRN) accuracy requirements while for days with prevailing overcast conditions these numbers reduce to 64.3 % (99.5 %), respectively.

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

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