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001468428 003__ OCoLC
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001468428 019__ $$a1381096978
001468428 020__ $$a9783031322426$$q(electronic bk.)
001468428 020__ $$a3031322428$$q(electronic bk.)
001468428 020__ $$z303132241X
001468428 020__ $$z9783031322419
001468428 0247_ $$a10.1007/978-3-031-32242-6$$2doi
001468428 035__ $$aSP(OCoLC)1380994241
001468428 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP
001468428 049__ $$aISEA
001468428 050_4 $$aQA279.5
001468428 08204 $$a519.5/42$$223/eng/20230613
001468428 1001_ $$aStone, Lawrence D.,$$d1942-
001468428 24510 $$aIntroduction to Bayesian tracking and particle filters /$$cLawrence D. Stone, Roy L. Streit, Stephen L. Anderson.
001468428 260__ $$aCham, Switzerland :$$bSpringer,$$c2023.
001468428 300__ $$a1 online resource
001468428 4901_ $$aStudies in Big Data ;$$vv.126
001468428 504__ $$aIncludes bibliographical references.
001468428 5050_ $$aIntro -- Contents -- 1 Introduction -- 2 Bayesian Single Target Tracking -- 2.1 Bayesian Inference -- 2.1.1 Prior Distribution -- 2.1.2 Likelihood Function -- 2.1.3 Posterior Distribution -- 2.1.4 Basic Bayesian Recursion -- 2.1.5 Examples of Priors, Posteriors, and Likelihood Functions -- 2.2 Tracking a Moving Target -- 2.2.1 Prior Distribution on Target Motion -- 2.2.2 Single Target Tracking Problem -- 2.2.3 Bayes-Markov Recursion -- 2.3 Kalman Filtering -- 2.3.1 Discrete Time Kalman Filtering Equations -- 2.3.2 Examples of Discrete-Time Gaussian Motion Models
001468428 5058_ $$a2.3.3 Continuous-Discrete Kalman Filtering Equations -- 2.3.4 Kalman Filtering Examples -- 2.3.5 Nonlinear Extensions of Kalman Filtering -- References -- 3 Bayesian Particle Filtering -- 3.1 Introduction -- 3.2 Particle Filter Tracking -- 3.2.1 Motion Model -- 3.2.2 Bayesian Recursion -- 3.2.3 Bayesian Particle Filter Recursion -- 3.2.4 Additional Considerations -- 3.2.5 Tracking Examples -- 3.3 Bayesian Particle Filtering Applied to Other Nonlinear Estimation Problems -- 3.3.1 Nonlinear Time Series Example -- 3.4 Smoothing Particle Filters -- 3.4.1 Repeated Filtering -- 3.4.2 Smoothing Examples
001468428 5058_ $$a3.5 Notes -- References -- 4 Simple Multiple Target Tracking -- 4.1 Introduction -- 4.2 Association Probabilities -- 4.3 Soft Association -- 4.4 Simplified JPDA -- 4.4.1 Particle Filter Implementation of Simplified Nonlinear JPDA -- 4.4.2 Crossing Targets Example -- 4.4.3 Feature-Aided Tracking -- 4.5 More Complex Multiple Target Tracking Problems -- References -- 5 Intensity Filters -- 5.1 Introduction -- 5.2 Point Process Model of Multitarget State -- 5.2.1 Basic Properties of PPPs -- 5.2.2 Probability Distribution Function for a PPP -- 5.2.3 Superposition of Point Processes
001468428 5058_ $$a5.2.4 Target Motion Process -- 5.2.5 Sensor Measurement Process -- 5.2.6 Thinning a Process -- 5.2.7 Augmented Spaces -- 5.3 iFilter -- 5.3.1 Augmented State Space Modeling -- 5.3.2 Predicted Detected and Undetected Target Processes -- 5.3.3 Measurement Process -- 5.3.4 Bayes Posterior Point Process (Information Update) -- 5.3.5 PPP Approximation -- 5.3.6 Correlation Losses in the PPP Approximation -- 5.3.7 The iFilter Recursion -- 5.4 Example -- 5.5 Notes -- References
001468428 506__ $$aAccess limited to authorized users.
001468428 520__ $$aThis book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers. The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the targets behavior in a natural fashion rather than assumptions made for mathematical convenience. The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face.
001468428 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 13, 2023).
001468428 650_0 $$aBayesian statistical decision theory.
001468428 650_0 $$aParticle methods (Numerical analysis)
001468428 655_0 $$aElectronic books.
001468428 7001_ $$aStreit, Roy L.
001468428 7001_ $$aAnderson, Stephen Lynn,$$d1953-
001468428 77608 $$iPrint version: $$z303132241X$$z9783031322419$$w(OCoLC)1374814138
001468428 830_0 $$aStudies in big data ;$$vv. 126.
001468428 852__ $$bebk
001468428 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-32242-6$$zOnline Access$$91397441.1
001468428 909CO $$ooai:library.usi.edu:1468428$$pGLOBAL_SET
001468428 980__ $$aBIB
001468428 980__ $$aEBOOK
001468428 982__ $$aEbook
001468428 983__ $$aOnline
001468428 994__ $$a92$$bISE