## Tracking and Data AssociationIn this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation, - Non-Lagrange interpolation, - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filtering |

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### Contents

Introduction | 1 |

Proofs and Derivations | 7 |

State Estimation for Linear Systems | 9 |

Copyright | |

40 other sections not shown

### Common terms and phrases

50 runs acceleration algorithm Appendix approach assumed autocorrelation average chi-square distribution components computed conditional mean conditional PDF confidence region considered constant corresponding covariance matrix defined degrees of freedom denoted density dimension discrete-time discussed dynamic estimation errors event Example expected number expected value extended Kalman filter false alarms false measurements filter gain follows hypothesis IEEE independent input Kalman filter likelihood function linear estimation maneuvering model maneuvering target MAP estimate mean-square error measurement noise ments ML estimate multiple-model nonlinear nonparametric normalized innovation squared Normalized state error notation number of false number of measurements obtained optimal parameter PDAF polynomial prediction prior PDF probabilistic data association problem process noise quadratic form recursion Riccati equation sample mean scalar Section 6.4 sensor standard deviation statistic stochastic target of interest threshold total probability theorem Track initiation unbiased validated measurements validation gate validation region variance vector white noise Wiener process yields zero

### References to this book

Exploring Artificial Intelligence in the New Millennium Gerhard Lakemeyer,Bernhard Nebel Limited preview - 2003 |

Directed Sonar Sensing for Mobile Robot Navigation John J. Leonard,Hugh F. Durrant-Whyte No preview available - 1992 |