Comparison of stochastic integration filter with the Unscented Kalman filter for maneuvering targets

Sigma-Point Filtering (SPF) has become popular to increase the accuracy in estimation of tracking parameters such as the mean and variance. A recent development in SPF is the stochastic integration filter (SIF) which has shown to increase estimation over the Extended Kalman Filter (EKF) and the Unscented Kalman filter (UKF); however, we want to explore the notion of the SIF versus the UKF for maneuvering targets. In this paper, we compare the SIF method with that of the KF, EKF, and UKF, using the Average Normalized Estimation Error Square (ANEES) for non-linear, non-Gaussian tracking. When the nonlinear turn-rate model is similar to the linear constant velocity model, all methods are the same. When the turn-rate model differs from the constant-velocity model, our results show that the UKF with a large number of sigma-points performs better than the SIF.

[1]  Erik Blasch,et al.  Level 5 (User Refinement) issues supporting Information Fusion Management , 2006, 2006 9th International Conference on Information Fusion.

[2]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[3]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[4]  C. Yang,et al.  Pose angular-aiding for maneuvering target tracking , 2005, 2005 7th International Conference on Information Fusion.

[5]  C. W. Chan,et al.  Performance evaluation of UKF-based nonlinear filtering , 2006, Autom..

[6]  John Monahan,et al.  Stochastic Integration Rules for Infinite Regions , 1998, SIAM J. Sci. Comput..

[7]  Zhansheng Duan,et al.  Constrained target motion modeling — Part II: Circular track , 2013, Proceedings of the 16th International Conference on Information Fusion.

[8]  Erik Blasch,et al.  Towards unbiased evaluation of uncertainty reasoning: The URREF ontology , 2012, 2012 15th International Conference on Information Fusion.

[9]  Erik Blasch,et al.  Nonlinear tracking evaluation using absolute and relative metrics , 2006, SPIE Defense + Commercial Sensing.

[10]  Erik Blasch,et al.  Fusion of Tracks with Road Constraints , 2008, J. Adv. Inf. Fusion.

[11]  Ondrej Straka,et al.  Randomized unscented Kalman filter in target tracking , 2012, 2012 15th International Conference on Information Fusion.

[12]  Barbara F. La Scala,et al.  IMM Estimator for Ground Target Tracking with Variable Measurement Sampling Intervals , 2006, 2006 9th International Conference on Information Fusion.

[13]  Erik Blasch,et al.  Characteristic errors of the IMM algorithm under three maneuver models for an accelerating target , 2008, 2008 11th International Conference on Information Fusion.

[14]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[15]  James Llinas,et al.  High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges , 2012, IEEE Aerospace and Electronic Systems Magazine.

[16]  David Frederic Crouse,et al.  Basic tracking using nonlinear 3D monostatic and bistatic measurements in refractive environments , 2014 .

[17]  Jindrich Duník,et al.  Derivative-free estimation methods: New results and performance analysis , 2009, Autom..

[18]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[19]  Erik P. Blasch,et al.  Fusion metrics for dynamic situation analysis , 2004, SPIE Defense + Commercial Sensing.

[20]  Erik Blasch,et al.  Performance Measures of Covariance and Information Matrices in Resource Management for Target State Estimation , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[21]  S. Plano,et al.  DFIG level 5 (user refinement) issues supporting situational assessment reasoning , 2005, 2005 7th International Conference on Information Fusion.

[22]  Chun Yang,et al.  Kalman Filtering with Nonlinear State Constraints , 2009 .

[23]  Erik Blasch,et al.  Decision-Level Fusion Performance Improvement From Enhanced HRR Radar Clutter Suppression , 2011, J. Adv. Inf. Fusion.

[24]  David Crouse,et al.  Basic tracking using nonlinear 3D monostatic and bistatic measurements in refractive environments , 2014, IEEE Aerospace and Electronic Systems Magazine.

[25]  Paulo Cesar G. da Costa,et al.  Evaluating uncertainty representation and reasoning in HLF systems , 2011, 14th International Conference on Information Fusion.

[26]  Erik Blasch,et al.  Issues and Challenges in Situation Assessment (Level 2 Fusion) , 2006, J. Adv. Inf. Fusion.

[27]  Erik Blasch,et al.  Top ten trends in High-Level Information Fusion , 2012, 2012 15th International Conference on Information Fusion.

[28]  Michael Mertens,et al.  Tracking and Data Fusion for Ground Surveillance , 2014 .

[29]  David L. Hall,et al.  Assessing the Performance of Multisensor Fusion Processes , 2001 .

[30]  Zhansheng Duan,et al.  Analysis, design, and estimation of linear equality-constrained dynamic systems , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[31]  Erik Blasch,et al.  GRoup IMM Tracking utilizing Track and Identification Fusion , 2001 .

[32]  Ondrej Straka,et al.  Unscented Kalman Filter: Aspects and Adaptive Setting of Scaling Parameter , 2012, IEEE Transactions on Automatic Control.

[33]  Erik Blasch,et al.  Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection (PREPRINT) , 2011 .

[34]  Erik Blasch,et al.  JDL level 5 fusion model: user refinement issues and applications in group tracking , 2002, SPIE Defense + Commercial Sensing.

[35]  Li Bai,et al.  Multiple source data fusion via sparse representation for robust visual tracking , 2011, 14th International Conference on Information Fusion.

[36]  Ondrej Straka,et al.  Stochastic Integration Filter , 2013, IEEE Transactions on Automatic Control.

[37]  Pierre Valin,et al.  Information fusion measures of effectiveness (MOE) for decision support , 2011, Defense + Commercial Sensing.

[38]  Chun Yang,et al.  Relative Track Metrics to Determine Model Mismatch , 2008, 2008 IEEE National Aerospace and Electronics Conference.

[39]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[40]  Erik Blasch,et al.  Comparison of adaptive and randomized unscented Kalman filter algorithms , 2014, 17th International Conference on Information Fusion (FUSION).

[41]  Eloi Bosse,et al.  High-Level Information Fusion Management and System Design , 2012 .

[42]  Ondrej Straka,et al.  Multitarget tracking performance analysis using the non-credibility index in the Nonlinear Estimation Framework (NEF) toolbox , 2010, Proceedings of the IEEE 2010 National Aerospace & Electronics Conference.

[43]  Erik Blasch,et al.  Situation, impact, and user refinement , 2003, SPIE Defense + Commercial Sensing.

[44]  Zhansheng Duan,et al.  Multi-sensor estimation fusion for linear equality constrained dynamic systems , 2013, Proceedings of the 16th International Conference on Information Fusion.

[45]  X. Rong Li,et al.  Evaluation of Estimation Algorithms: Credibility Tests , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[46]  Zhansheng Duan,et al.  The Role of Pseudo Measurements in Equality-Constrained State Estimation , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[47]  Li Bai,et al.  Real-Time Probabilistic Covariance Tracking With Efficient Model Update , 2012, IEEE Transactions on Image Processing.

[48]  C. Yang,et al.  Nonlinear constrained tracking of targets on roads , 2005, 2005 7th International Conference on Information Fusion.

[49]  Fredrik Gustafsson,et al.  Some Relations Between Extended and Unscented Kalman Filters , 2012, IEEE Transactions on Signal Processing.

[50]  Erik Blasch,et al.  Randomized unscented transform in state estimation of non-Gaussian systems: Algorithms and performance , 2012, 2012 15th International Conference on Information Fusion.

[51]  Erik Blasch,et al.  Random-point-based filters: analysis and comparison in target tracking , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[52]  James Llinas.  Assessing the Performance of Multisensor Fusion Processes , 2001 .

[53]  Erik Blasch,et al.  Multiple sensor estimation using a high-degree cubature information filter , 2013, Defense, Security, and Sensing.

[54]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[55]  Ondrej Straka,et al.  Multiple-model filtering with multiple constraints , 2010, Proceedings of the 2010 American Control Conference.

[56]  Erik Blasch,et al.  Measures of effectiveness for high-level fusion , 2010, 2010 13th International Conference on Information Fusion.

[57]  Ming Xin,et al.  Multiple sensor estimation using the sparse Gauss-Hermite quadrature information filter , 2012, 2012 American Control Conference (ACC).

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

[59]  Zhansheng Duan,et al.  Constrained target motion modeling — Part I: Straight line track , 2013, Proceedings of the 16th International Conference on Information Fusion.

[60]  Robert R. Tenney,et al.  Dynamic tactical targeting , 2004, SPIE Defense + Commercial Sensing.