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001432702 001__ 1432702
001432702 003__ OCoLC
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001432702 019__ $$a1225552343$$a1237462206$$a1238206043$$a1244637141
001432702 020__ $$a9783030611910$$q(electronic bk.)
001432702 020__ $$a3030611914$$q(electronic bk.)
001432702 020__ $$z3030611906
001432702 020__ $$z9783030611903
001432702 0247_ $$a10.1007/978-3-030-61191-0$$2doi
001432702 035__ $$aSP(OCoLC)1224578363
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001432702 049__ $$aISEA
001432702 050_4 $$aQA164$$b.S77 2021
001432702 08204 $$a511.6$$223
001432702 1001_ $$aStreit, Roy,$$eauthor.
001432702 24510 $$aAnalytic combinatorics for multiple object tracking /$$cRoy Streit, Robert Blair Angle, Murat Efe.
001432702 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001432702 300__ $$a1 online resource
001432702 336__ $$atext$$btxt$$2rdacontent
001432702 337__ $$acomputer$$bc$$2rdamedia
001432702 338__ $$aonline resource$$bcr$$2rdacarrier
001432702 347__ $$atext file
001432702 347__ $$bPDF
001432702 504__ $$aIncludes bibliographical references and index.
001432702 5050_ $$aIntro -- Preface -- Contents -- Acronyms -- 1 Introduction to Analytic Combinatorics and Tracking -- 1.1 Introduction -- 1.2 The Benefits of Analytic Combinatorics to Tracking -- 1.3 Sensor and Object Models in Tracking -- 1.4 Likelihood Functions and Assignments -- 1.5 A First Look at Generating Functions for Tracking Problems -- 1.5.1 Statement A-Object Existence and Detection -- 1.5.2 Statement B-Gridded Measurements -- 1.5.3 Statement C-Gridded Object State and the Genesis of Tracking -- 1.6 Generating Functions for Bayes Theorem -- 1.6.1 GF of the Bayes Posterior Distribution
001432702 5058_ $$a1.6.2 Bayes Inference in Statement A -- 1.6.3 Bayes Inference in Statement B -- 1.6.4 Bayes Inference in Statement C -- 1.7 Other Models of Object Existence and Detection -- 1.7.1 Multiple Object Existence Models -- 1.7.2 Random Number of Object Existence Models -- 1.7.3 False Alarms -- 1.8 Organization of the Book -- References -- 2 Tracking One Object -- 2.1 Introduction -- 2.2 AC and Bayes Theorem -- 2.3 Setting the Stage -- 2.4 Bayes-Markov Single-Object Filter -- 2.4.1 BM: Assumptions -- 2.4.2 BM: Generating Functional -- 2.4.3 BM: Exact Bayesian Posterior Distribution
001432702 5058_ $$a2.5 Tracking in Clutter-The PDA Filter -- 2.5.1 PDA: Assumptions -- 2.5.2 PDA: Generating Functional -- 2.5.3 PDA: Exact Bayesian Posterior Distribution -- 2.5.4 PDA: Closing the Bayesian Recursion -- 2.5.5 PDA: Gating-Conditioning on Subsets of Measurements -- 2.6 Object Existence-The IPDA Filter -- 2.6.1 IPDA: Assumptions -- 2.6.2 IPDA: Generating Functional -- 2.6.3 IPDA: Exact Bayesian Posterior Distribution -- 2.6.4 IPDA: Closing the Bayesian Recursion -- 2.7 Linear-Gaussian Filters -- 2.7.1 The Classical Kalman Filter -- 2.7.2 Linear-Gaussian PDA: Without Gating
001432702 5058_ $$a2.7.3 Linear-Gaussian PDA: With Gating -- 2.8 Numerical Example: IPDA -- References -- 3 Tracking a Specified Number of Objects -- 3.1 Introduction -- 3.2 Joint Probabilistic Data Association (JPDA) Filter -- 3.2.1 Multivariate Generating Functional -- 3.2.2 Exact Bayes Posterior Probability Distribution via AC -- 3.2.3 Measurement Assignments and Cross-Derivative Terms -- 3.2.4 Closing the Bayesian Recursion -- 3.2.5 Number of Assignments -- 3.2.6 Measurement Gating -- 3.3 Joint Integrated Probabilistic Data Association (JIPDA) Filter -- 3.3.1 Integrated State Space
001432702 5058_ $$a3.3.2 Generating Functional -- 3.3.3 Exact Bayes Posterior Probability Distribution via AC -- 3.3.4 Closing the Bayesian Recursion -- 3.4 Resolution/Merged Measurement Problem-JPDA/Res Filter -- 3.5 Numerical Examples: Tracking with Unresolved Objects -- 3.5.1 JPDA/Res Filter with Weak and Strong Crossing Tracks -- 3.5.2 JPDA/Res with Parallel Object Tracks -- 3.5.3 Discussion of Results -- References -- 4 Tracking a Variable Number of Objects -- 4.1 Introduction -- 4.2 Superposition of Multiple Object States -- 4.2.1 General Considerations -- 4.2.2 Superposition with Non-identical Object Models
001432702 506__ $$aAccess limited to authorized users.
001432702 520__ $$aThe book shows that the analytic combinatorics (AC) method encodes the combinatorial problems of multiple object tracking--without information loss--into the derivatives of a generating function (GF). The book lays out an easy-to-follow path from theory to practice and includes salient AC application examples. Since GFs are not widely utilized amongst the tracking community, the book takes the reader from the basics of the subject to applications of theory starting from the simplest problem of single object tracking, and advancing chapter by chapter to more challenging multi-object tracking problems. Many established tracking filters (e.g., Bayes-Markov, PDA, JPDA, IPDA, JIPDA, CPHD, PHD, multi-Bernoulli, MBM, LMBM, and MHT) are derived in this manner with simplicity, economy, and considerable clarity. The AC method gives significant and fresh insights into the modeling assumptions of these filters and, thereby, also shows the potential utility of various approximation methods that are well established techniques in applied mathematics and physics, but are new to tracking. These unexplored possibilities are reviewed in the final chapter of the book.
001432702 588__ $$aOnline resource; title from digital title page (viewed on February 08, 2021).
001432702 650_0 $$aCombinatorial analysis.
001432702 650_6 $$aAnalyse combinatoire.
001432702 655_0 $$aElectronic books.
001432702 7001_ $$aAngle, Robert Blair,$$eauthor.
001432702 7001_ $$aEfe, Murat,$$eauthor.
001432702 77608 $$iPrint version:$$aStreit, Roy.$$tAnalytic combinatorics for multiple object tracking.$$dCham, Switzerland : Springer, [2021]$$z3030611906$$z9783030611903$$w(OCoLC)1193122383
001432702 852__ $$bebk
001432702 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-61191-0$$zOnline Access$$91397441.1
001432702 909CO $$ooai:library.usi.edu:1432702$$pGLOBAL_SET
001432702 980__ $$aBIB
001432702 980__ $$aEBOOK
001432702 982__ $$aEbook
001432702 983__ $$aOnline
001432702 994__ $$a92$$bISE