Track-before-detect using expectation maximisation : the histogram probabilistic multi-hypothesis tracker: theory and applications /

Saved in:
Bibliographic Details
Author / Creator:Davey, Samuel J., author.
Imprint:Singapore : Springer, 2018.
Description:1 online resource (xxi, 352 pages) : illustrations (some color)
Language:English
Series:Signals and communication technology, 1860-4862
Signals and communication technology,
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11543425
Hidden Bibliographic Details
Other authors / contributors:Gaetjens, Han X., author.
ISBN:9789811075933
981107593X
9789811075926
9811075921
Digital file characteristics:text file PDF
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed February 13, 2018).
Summary:This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems. The book is supplemented with software that both furthers readers' understanding and acts as a toolkit for those who wish to apply the methods to their own problems.
Other form:Printed edition: 9789811075926
Standard no.:10.1007/978-981-10-7593-3

MARC

LEADER 00000cam a2200000Ii 4500
001 11543425
006 m o d
007 cr cnu|||unuuu
008 180213s2018 si a ob 001 0 eng d
005 20240628211411.1
019 |a 1023490532  |a 1027034932  |a 1029079861  |a 1048098907  |a 1048192627  |a 1059198098  |a 1066496373  |a 1066531685  |a 1081194904  |a 1086491908  |a 1112878912 
020 |a 9789811075933  |q (electronic bk.) 
020 |a 981107593X  |q (electronic bk.) 
020 |z 9789811075926  |q (print) 
020 |z 9811075921 
024 7 |a 10.1007/978-981-10-7593-3  |2 doi 
035 |a (OCoLC)1022949379  |z (OCoLC)1023490532  |z (OCoLC)1027034932  |z (OCoLC)1029079861  |z (OCoLC)1048098907  |z (OCoLC)1048192627  |z (OCoLC)1059198098  |z (OCoLC)1066496373  |z (OCoLC)1066531685  |z (OCoLC)1081194904  |z (OCoLC)1086491908  |z (OCoLC)1112878912 
035 9 |a (OCLCCM-CC)1022949379 
037 |n Title purchased via APUC SHEDL / ScopNet ebook agreement  |n Engineering (Springer-11647) 
040 |a GW5XE  |b eng  |e rda  |e pn  |c GW5XE  |d N$T  |d EBLCP  |d UPM  |d OCLCF  |d UAB  |d OCLCQ  |d MERER  |d OCLCQ  |d STF  |d YDX  |d OCLCQ  |d VT2  |d U3W  |d CNCEN  |d OCLCQ  |d WYU  |d OCLCQ  |d LVT  |d CAUOI  |d AUD  |d COO  |d UKAHL  |d OCLCQ  |d ADU  |d OCLCQ 
049 |a MAIN 
050 4 |a QA312 
072 7 |a MAT  |x 003000  |2 bisacsh 
072 7 |a MAT  |x 029000  |2 bisacsh 
072 7 |a TTBM  |2 bicssc 
072 7 |a UYS  |2 bicssc 
100 1 |a Davey, Samuel J.,  |e author. 
245 1 0 |a Track-before-detect using expectation maximisation :  |b the histogram probabilistic multi-hypothesis tracker: theory and applications /  |c Samuel J. Davey, Han X. Gaetjens. 
264 1 |a Singapore :  |b Springer,  |c 2018. 
300 |a 1 online resource (xxi, 352 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Signals and communication technology,  |x 1860-4862 
504 |a Includes bibliographical references and index. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed February 13, 2018). 
505 0 |a Intro; Foreword; Reference; Acknowledgements; Contents; About the Authors; Parlance; 1 Introduction; 1.1 Historical Development of H-PMHT; 1.2 Preliminaries; 1.3 Expectation -- Maximisation; 1.4 Notation; 1.5 Canonical Multi-target Scenario; 1.6 Measures of Performance; 1.6.1 Cardinality Measures; 1.6.2 Association Measures; 1.6.3 Accuracy Measures; 1.6.4 Track to Truth Association; 1.7 Monograph Synopsis; References; 2 Idealised Track-Before-Detect; 2.1 Single Target Comparison; 2.2 Summary; References; 3 Point Measurement Probabilistic Multi-hypothesis Tracking; 3.1 Gaussian Mixture Models. 
505 8 |a 3.2 Dynamic Mixture Model3.2.1 Expectation Step; 3.2.2 Linear Gaussian Maximisation Step; 3.3 Non-Gaussian Mixtures; 3.4 Incorporating Clutter; 3.5 Examples of PMHT Point Measurement Tracking; 3.5.1 Two Targets; 3.5.2 Numerous Targets; 3.6 Problems with PMHT; 3.6.1 Model Order Estimation; 3.6.2 Adaptivity; 3.6.3 Optimism; 3.7 Summary; References; 4 Histogram Probabilistic Multi-hypothesis Tracking; 4.1 Histogram Data Association; 4.1.1 Expectation Step; 4.1.2 Maximisation Step; 4.2 Unobserved Pixels; 4.3 Image Quantising; 4.3.1 Quantisation in the Limit; 4.3.2 Resampled Target Prior. 
505 8 |a 4.4 Associated Images4.5 Algorithm Summary for Gaussian Appearance; 4.6 Simulated Example; 4.7 Summary; References; 5 Implementation Considerations; 5.1 Alternative Resampled Prior; 5.2 Integrals; 5.3 Vectorised Two-Dimensional Case; 5.4 Single-Target Chip Processing; 5.5 Covariance Estimates; 5.5.1 Observed Information; 5.5.2 Joint Probabilistic Data Association; 5.6 Track Management; 5.6.1 Track Quality Score; 5.6.2 Hierarchical Track Update; 5.6.3 Track Decisions; 5.6.4 Image Vetting; 5.6.5 New Candidate Formation; 5.6.6 Integrated Track Management; 5.7 Summary; References. 
505 8 |a 6 Poisson Scattering Field6.1 Hysteresis; 6.2 Poisson and Multinomial Equivalence; 6.3 Dynamic Non-homogeneous Poisson Mixture Model; 6.3.1 Point Measurement Data; 6.3.2 Image Measurement Data; 6.4 Examples; 6.4.1 Measurement Rate Estimation; 6.4.2 Average Power Estimation; 6.4.3 Track Initiation; 6.5 Clutter Mapping; 6.6 Target Life Cycle; 6.7 Summary; References; 7 Known Non-Gaussian Target Appearance; 7.1 Grid-Based Maximisation for Non-Gaussian Appearance; 7.2 Particle-Based Maximisation for Non-Gaussian Appearance; 7.3 Cell-Varying Point Spread Function. 
505 8 |a 7.4 Gaussian Mixture Appearance Approximation7.5 Simulated Examples; 7.5.1 Linear Gaussian Appearance; 7.5.2 Linear Non-Gaussian Appearance; 7.5.3 Crossing Non-Gaussian Scenario; 7.5.4 Diverging Target Scenario; 7.6 Summary; References; 8 Adaptive Appearance Models; 8.1 Deterministic Gaussian Appearance; 8.2 Stochastic Gaussian Appearance; 8.3 General Framework for Appearance Estimation; 8.4 Gaussian Mixture Appearance; 8.5 Bounding Box; 8.6 Dirichlet Appearance; 8.7 Appearance Library; 8.8 Correlated Kinematics and Appearance; 8.9 Simulated Examples; 8.9.1 Gaussian Targets. 
520 |a This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems. The book is supplemented with software that both furthers readers' understanding and acts as a toolkit for those who wish to apply the methods to their own problems. 
650 0 |a Gaussian measures.  |0 http://id.loc.gov/authorities/subjects/sh85053557 
650 7 |a MATHEMATICS  |x Applied.  |2 bisacsh 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
650 7 |a Gaussian measures.  |2 fast  |0 (OCoLC)fst00939019 
655 4 |a Electronic books. 
700 1 |a Gaetjens, Han X.,  |e author. 
776 0 8 |i Printed edition:  |z 9789811075926 
830 0 |a Signals and communication technology,  |x 1860-4862  |0 http://id.loc.gov/authorities/names/no2002055234 
903 |a HeVa 
929 |a oclccm 
999 f f |i bf7ea10d-bc0c-5edc-b4be-fd65cf15dcfc  |s 7304a3e6-7279-54c6-ab1f-8c1a01d178db 
928 |t Library of Congress classification  |a QA312  |l Online  |c UC-FullText  |u https://link.springer.com/10.1007/978-981-10-7593-3  |z Springer Nature  |g ebooks  |i 12552177