Bidirectional feedback dynamic particle filter with big data for the particle degeneracy problem

Particle Filter (PF) is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF (B3DPF) is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies (sensor data management, parameters used in the weight computation, resampling) of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.

[1]  Fasheng Wang,et al.  Improving particle filter with a new sampling strategy , 2009, 2009 4th International Conference on Computer Science & Education.

[2]  Wenjing Liu,et al.  Improved particle filter algorithms based on partial systematic resampling , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[3]  Yan Zhai,et al.  Unscented Particle Filter Algorithm for Ballistic Target Tracking , 2011 .

[4]  Greg Welch,et al.  HISTORY: The Use of the Kalman Filter for Human Motion Tracking in Virtual Reality , 2009, PRESENCE: Teleoperators and Virtual Environments.

[5]  Yunbo Kong,et al.  Particle filter algorithm based on adaptive resampling strategy , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[6]  John P. Kerekes,et al.  Adaptive Optical Sensing in an Object Tracking DDDAS , 2012, ICCS.

[7]  Jihong Zhu,et al.  Ant estimator with application to target tracking , 2010, Signal Process..

[8]  P. Fearnhead,et al.  Improved particle filter for nonlinear problems , 1999 .

[9]  Xiaolong Wang,et al.  The Improved Particle Filter Algorithm Based on Weight Optimization , 2013, 2013 International Conference on Information Science and Cloud Computing Companion.

[10]  Puneet Singla,et al.  International Conference on Computational Science, ICCS 2012 , 2012, ICCS.

[11]  Jun S. Liu,et al.  Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..

[12]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[13]  R. Adnan,et al.  Flood water level prediction and tracking using particle filter algorithm , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[14]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[15]  Pingyuan Cui,et al.  Adaptive MCMC Particle Filter for Nonlinear and Non-Gaussian State Estimation , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[16]  Haris N. Koutsopoulos,et al.  Dynamic data-driven local traffic state estimation and prediction , 2013 .

[17]  Giorgio Battistelli,et al.  Data-driven communication for state estimation with sensor networks , 2012, Autom..

[18]  Xiaolin Hu,et al.  Dynamic Data Driven Particle Filter for Agent-Based Traffic State Estimation , 2015, ICCCS.

[19]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[20]  Seungho Lee,et al.  DDDAS-based multi-fidelity simulation framework for supply chain systems , 2010 .

[21]  Bin Wang,et al.  Study on simulation and application of Kalman filter arithmetic in integrated navigation system , 2013, Proceedings of 2013 2nd International Conference on Measurement, Information and Control.

[22]  J. Tinsley Oden,et al.  A Dynamic Data Driven Application System for Real-time Monitoring of Stochastic Damage , 2013, ICCS.

[23]  J. Hammersley,et al.  Poor Man's Monte Carlo , 1954 .

[24]  Meng Zhang,et al.  Real Time Application for Traffic State Estimation Based on Large Sets of Floating Car Data , 2009 .

[25]  Euntai Kim,et al.  A New Particle Filter Inspired by Biological Evolution: Genetic Filter , 2007 .

[26]  Christian Poellabauer,et al.  Applying DDDAS Principles to Command, Control and Mission Planning for UAV Swarms , 2012, ICCS.

[27]  Frederica Darema.  Introduction to the ICCS2006 Workshop on Dynamic Data Driven Applications Systems , 2006, International Conference on Computational Science.

[28]  Hiromitsu Kumamoto,et al.  Random sampling approach to state estimation in switching environments , 1977, Autom..

[29]  S. Y. Chen,et al.  Kalman Filter for Robot Vision: A Survey , 2012, IEEE Transactions on Industrial Electronics.

[30]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[31]  D. Mayne,et al.  Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering† , 1969 .

[32]  Debasish Roy,et al.  Variance-Reduced Particle Filters for Structural System Identification Problems , 2013 .

[33]  Juan M. Corchado,et al.  Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches , 2013, Expert Syst. Appl..