Mehrobjekt-Zustandsschätzung mit verteilten Sensorträgern am Beispiel der Umfeldwahrnehmung im Straßenverkehr
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[1] T. Kirubarajan,et al. PHD filtering for tracking an unknown number of sources using an array of sensors , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.
[2] J. Munkres. ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .
[3] Daniel Cremers,et al. Passive multi-object localization and tracking using bearing data , 2010, 2010 13th International Conference on Information Fusion.
[4] Robin J. Evans,et al. Integrated probabilistic data association , 1994, IEEE Trans. Autom. Control..
[5] F. Jondral,et al. Wahrscheinlichkeitsrechnung und stochastische Prozesse , 2002 .
[6] Uwe D. Hanebeck,et al. Nonlinear Bayesian estimation with compactly supported wavelets , 2010, 49th IEEE Conference on Decision and Control (CDC).
[7] Klaus C. J. Dietmayer,et al. Analysis of V2X communication parameters for the development of a fusion architecture for cooperative perception systems , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).
[8] Anil K. Jain,et al. Interacting multiple model (IMM) Kalman filters for robust high speed human motion tracking , 2002, Object recognition supported by user interaction for service robots.
[9] M. King,et al. A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation , 2009 .
[10] Klaus C. J. Dietmayer,et al. Precise timestamping and temporal synchronization in multi-sensor fusion , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).
[11] Y. Bar-Shalom,et al. Track labeling and PHD filter for multitarget tracking , 2006, IEEE Transactions on Aerospace and Electronic Systems.
[12] Daniel E. Clark,et al. Particle PHD filter multiple target tracking in sonar image , 2007, IEEE Transactions on Aerospace and Electronic Systems.
[13] Simon Maskell,et al. Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation , 2006, 2006 9th International Conference on Information Fusion.
[14] S. Godsill,et al. Auxiliary Particle Implementation of the Probability Hypothesis Density Filter , 2007, 2007 5th International Symposium on Image and Signal Processing and Analysis.
[15] N. Gordon,et al. International Conference on Information Fusion ( FUSION ) A Comparison of Particle Filters for Recursive Track-before-detect , 2006 .
[16] R. Mahler,et al. PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.
[17] Ba-Ngu Vo,et al. Closed Form PHD Filtering for Linear Jump Markov Models , 2006, 2006 9th International Conference on Information Fusion.
[18] M. Hazelton,et al. Cross‐validation Bandwidth Matrices for Multivariate Kernel Density Estimation , 2005 .
[19] S. Haykin,et al. Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.
[20] Michael Thuy,et al. Objektdetektion und -verfolgung durch nichtlineare Filterung und Fusion von Lidar- und PositionsdatenObject Detection and Tracking through Non-Linear Filtering and Fusion of Lidar and Position Data , 2010 .
[21] Cornelius T. Leondes,et al. Advances in control systems : theory and applications , 1966 .
[22] Robert W. Sittler,et al. An Optimal Data Association Problem in Surveillance Theory , 1964, IEEE Transactions on Military Electronics.
[23] H. W. Sorenson,et al. Kalman filtering : theory and application , 1985 .
[24] David Suter,et al. Joint Detection and Estimation of Multiple Objects From Image Observations , 2010, IEEE Transactions on Signal Processing.
[25] Christian Hoffmann,et al. Cheap Joint Probabilistic Data Association Filters in an Interacting Multiple Model Design , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.
[26] Ba-Ngu Vo,et al. Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.
[27] Mark R. Morelande,et al. A multitarget tracking algorithm based on random sets , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.
[28] Ba-Ngu Vo,et al. The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.
[29] F. W. Cathey,et al. The iterated Kalman filter update as a Gauss-Newton method , 1993, IEEE Trans. Autom. Control..
[30] Ba-Ngu Vo,et al. Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach , 2006, IEEE Transactions on Signal Processing.
[31] Marcus Obst,et al. Tracking multiple extended objects — A Markov chain Monte Carlo approach , 2011, 14th International Conference on Information Fusion.
[32] Markus Maurer,et al. Object tracking in urban intersections: Active interacting multi model filter with handling of uncertainties of map matching , 2011, 14th International Conference on Information Fusion.
[33] Marco F. Huber,et al. Gaussian mixture reduction via clustering , 2009, 2009 12th International Conference on Information Fusion.
[34] P. Mahalanobis. On the generalized distance in statistics , 1936 .
[35] Rudolph van der Merwe,et al. The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[36] Feng Liu,et al. IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision , 2008, 2008 IEEE Intelligent Vehicles Symposium.
[37] D. Dowson,et al. The Fréchet distance between multivariate normal distributions , 1982 .
[38] H. Kuhn. The Hungarian method for the assignment problem , 1955 .
[39] Seid H. Pourtakdoust,et al. UD Covariance Factorization for Unscented Kalman Filter using Sequential Measurements Update , 2007 .
[40] Arthur Gelb,et al. Applied Optimal Estimation , 1974 .
[41] Ba-Ngu Vo,et al. A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.
[42] Thia Kirubarajan,et al. Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .
[43] Thomas B. Schön,et al. Marginalized particle filters for mixed linear/nonlinear state-space models , 2005, IEEE Transactions on Signal Processing.
[44] Klaus C. J. Dietmayer,et al. Multisensor Vehicle Tracking with the Probability Hypothesis Density Filter , 2006, 2006 9th International Conference on Information Fusion.
[45] S. Godsill,et al. Multi-Object Tracking of Sinusoidal Components in Audio with the Gaussian Mixture Probability Hypothesis Density Filter , 2007, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
[46] V. Jilkov,et al. Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.
[47] Emilio Maggio,et al. Efficient Multitarget Visual Tracking Using Random Finite Sets , 2008, IEEE Transactions on Circuits and Systems for Video Technology.
[48] Y. Bar-Shalom,et al. Tracking in a cluttered environment with probabilistic data association , 1975, Autom..
[49] Rudolph van der Merwe,et al. The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).
[50] Petar M. Djuric,et al. Resampling Algorithms for Particle Filters: A Computational Complexity Perspective , 2004, EURASIP J. Adv. Signal Process..
[51] Y. Boers,et al. Multitarget particle filter track before detect application , 2004 .
[52] Darko Musicki,et al. Joint Integrated Probabilistic Data Association - JIPDA , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).
[53] Yin Jianjun,et al. Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models , 2008 .
[54] Fredrik Gustafsson,et al. Track-before-detect algorithm for tracking extended targets , 2006 .
[55] Simon J. Julier,et al. The spherical simplex unscented transformation , 2003, Proceedings of the 2003 American Control Conference, 2003..
[56] Uwe Kiencke,et al. Signalverarbeitung: Zeit-Frequenz-Analyse und Schätzverfahren , 2008 .
[57] S.S. Blackman,et al. Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.
[58] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[59] R. Mahler. Multitarget Bayes filtering via first-order multitarget moments , 2003 .
[60] Marco F. Huber,et al. Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation , 2008 .
[61] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[62] Samuel J. Davey,et al. A Comparison of Detection Performance for Several Track-before-Detect Algorithms , 2008, 2008 11th International Conference on Information Fusion.
[63] Jouko Lampinen,et al. Rao-Blackwellized particle filter for multiple target tracking , 2007, Inf. Fusion.
[64] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[65] Ba-Ngu Vo,et al. Convergence Analysis of the Gaussian Mixture PHD Filter , 2007, IEEE Transactions on Signal Processing.
[66] Henk A. P. Blom,et al. Probabilistic data association avoiding track coalescence , 2000, IEEE Trans. Autom. Control..
[67] Ba-Ngu Vo,et al. A Random-Finite-Set Approach to Bayesian SLAM , 2011, IEEE Transactions on Robotics.
[68] Yakov Bar-Shalom,et al. Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .
[69] Ba-Ngu Vo,et al. A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.
[70] Ba-Ngu Vo,et al. Probability hypothesis density filter versus multiple hypothesis tracking , 2004, SPIE Defense + Commercial Sensing.
[71] Mark J. F. Gales,et al. Product of Gaussians for speech recognition , 2006, Comput. Speech Lang..
[72] Ba Tuong Vo,et al. Random finite sets in Multi-object filtering , 2008 .
[73] Sumeetpal S. Singh,et al. Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.
[74] Jeffrey K. Uhlmann,et al. Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.
[75] Ingemar J. Cox,et al. An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[76] J. Vermaak,et al. A unifying framework for multi-target tracking and existence , 2005, 2005 7th International Conference on Information Fusion.
[77] Ba-Ngu Vo,et al. On the Bayes filtering equations of finite set statistics , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).
[78] Guido Sanguinetti,et al. Approximate inference of the bandwidth in multivariate kernel density estimation , 2011, Comput. Stat. Data Anal..
[79] Simon J. Julier,et al. The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).
[80] D. Salmond. Mixture reduction algorithms for target tracking , 1989 .
[81] David Suter,et al. Multi-object filtering from image sequence without detection , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[82] Jianjun Yin,et al. The Gaussian Particle multi-target multi-Bernoulli filter , 2010, 2010 2nd International Conference on Advanced Computer Control.
[83] Simon J. Godsill,et al. Gaussian Mixture implementations of Probability Hypothesis Density filters for non-linear dynamical models , 2008 .
[84] Ronald Mahler,et al. Sensor Management with Non-Ideal Sensor Dynamics , 2004 .
[85] Yaakov Bar-Shalom,et al. Sonar tracking of multiple targets using joint probabilistic data association , 1983 .
[86] Y. Boers,et al. Interacting multiple model particle filter , 2003 .
[87] Y. Bar-Shalom,et al. The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .
[88] Ronald P. S. Mahler,et al. Statistical Multisource-Multitarget Information Fusion , 2007 .
[89] B. Vo,et al. Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter , 2009, IEEE Transactions on Aerospace and Electronic Systems.
[90] Ba-Ngu Vo,et al. The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations , 2009, IEEE Transactions on Signal Processing.
[91] Niels Kjølstad Poulsen,et al. New developments in state estimation for nonlinear systems , 2000, Autom..
[92] Ba-Ngu Vo,et al. Improved SMC implementation of the PHD filter , 2010, 2010 13th International Conference on Information Fusion.
[93] Y. Bar-Shalom,et al. Design of interacting multiple model algorithm for tracking in air traffic control systems , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.
[94] Ba-Ngu Vo,et al. PHD Filtering with target amplitude feature , 2008, 2008 11th International Conference on Information Fusion.
[95] T. Schei. A finite-difference approach to linearization in nonlinear estimation algorithms , 1995, Proceedings of 1995 American Control Conference - ACC'95.
[96] Jian Qiu Zhang,et al. The polynomial predictive particle filter , 2010, 2010 2nd International Conference on Advanced Computer Control.
[97] D. Clark,et al. Group Target Tracking with the Gaussian Mixture Probability Hypothesis Density Filter , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.
[98] Weifeng Tian,et al. An interacting multiple model particle filter for manoeuvring target location , 2006 .
[99] Mirko Mählisch,et al. Filtersynthese zur simultanen Minimierung von Existenz-, Assoziations- und Zustandsunsicherheiten in der Fahrzeugumfelderfassung mit heterogenen Sensordaten , 2010 .
[100] Yaakov Bar-Shalom,et al. Kalman filter versus IMM estimator: when do we need the latter? , 2003 .
[101] John G. Proakis,et al. Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..
[102] David Suter,et al. Bayesian integration of audio and visual information for multi-target tracking using a CB-member filter , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[103] Uwe D. Hanebeck,et al. Progressive Gaussian mixture reduction , 2008, 2008 11th International Conference on Information Fusion.
[104] Nico Kämpchen,et al. Feature-level fusion of laser scanner and video data for advanced driver assistance systems , 2007 .
[105] Fernando Puente León,et al. Information fusion for automotive applications - An overview , 2011, Inf. Fusion.
[106] Fernando Puente León,et al. Radargestützte Mehrobjekt-Verfolgung mit dem PHD-Filter , 2011 .
[107] R.Y. Novoselov,et al. Mitigating the effects of residual biases with Schmidt-Kalman filtering , 2005, 2005 7th International Conference on Information Fusion.