LEADER 04776nam 2200661 450 001 9910465184203321 005 20200520144314.0 010 $a1-5231-1701-X 010 $a1-60807-554-0 035 $a(CKB)2560000000254527 035 $a(EBL)1579691 035 $a(OCoLC)939262765 035 $a(SSID)ssj0001516390 035 $a(PQKBManifestationID)12589673 035 $a(PQKBTitleCode)TC0001516390 035 $a(PQKBWorkID)11499374 035 $a(PQKB)11152457 035 $a(MiAaPQ)EBC1579691 035 $a(Au-PeEL)EBL1579691 035 $a(CaPaEBR)ebr11069361 035 $a(CaBNVSL)mat09101112 035 $a(IEEE)9101112 035 $a(EXLCZ)992560000000254527 100 $a20200729d2013 uy 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBayesian multiple target tracking /$fLawrence D. Stone [and three others] 205 $a2nd ed. 210 1$aBoston [Massachusetts] ;$aLondon [England] :$cArtech House,$d2014. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2013] 215 $a1 online resource (315 p.) 225 1 $aArtech House Radar Series 300 $aDescription based upon print version of record. 311 $a1-60807-553-2 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aBayesian Multiple Target Tracking Second Edition; Contents; Preface; Introduction; Acknowledgments; Chapter 1 Tracking Problems; 1.1 DESCRIPTION OF TRACKING PROBLEM; 1.2 EXAMPLE 1: TRACKING A SURFACE SHIP; 1.3 EXAMPLE 2: BEARINGS-ONLY TRACKING; 1.4 EXAMPLE 3: PERISCOPE DETECTION AND TRACKING; 1.5 EXAMPLE 4: TRACKING MULTIPLE TARGETS; 1.6 SUMMARY; Chapter 2 Bayesian Inference and Likelihood Functions; 2.1 THE CASE FOR BAYESIAN INFERENCE; 2.2 THE LIKELIHOOD FUNCTION AND BAYES' THEOREM; 2.3 EXAMPLES OF LIKELIHOOD FUNCTIONS; Chapter 3 Single Target Tracking; 3.1 BAYESIAN FILTERING. 327 $a3.2 KALMAN FILTERING3.3 PARTICLE FILTER IMPLEMENTATION OF NONLINEARFILTERING; 3.4 SUMMARY; Chapter 4 Classical Multiple Target Tracking; 4.1 MULTIPLE TARGET TRACKING; 4.2 MULTIPLE HYPOTHESIS TRACKING; 4.3 INDEPENDENT MULTIPLE HYPOTHESIS TRACKING; 4.4 LINEAR-GAUSSIAN MULTIPLE HYPOTHESIS TRACKING; 4.5 NONLINEAR JOINT PROBABILISTIC DATA ASSOCIATION; 4.6 PROBABILISTIC MULTIPLE HYPOTHESIS TRACKING; 4.7 SUMMARY; 4.8 NOTES; Chapter 5 Multitarget Intensity Filters; 5.1 POINT PROCESS MODEL OF MULTITARGET STATE; 5.2 iFILTER; 5.3 PHD FILTER; 5.4 PGF APPROACH TO THE iFILTER; 5.5 EXTENDED TARGET FILTERS. 327 $a5.6 SUMMARY5.7 NOTES; Chapter 6 Multiple Target Tracking Using Tracker-Generated Measurements; 6.1 MAXIMUM A POSTERIORI PENALTY FUNCTION TRACKING; 6.2 PARTICLE FILTER IMPLEMENTATION; 6.3 LINEAR-GAUSSIAN IMPLEMENTATION; 6.4 EXAMPLES; 6.5 SUMMARY; 6.6 NOTES; 6.7 SENSOR ARRAY OBSERVATION MODEL AND SIGNALPROCESSING; Chapter 7 Likelihood Ratio Detection and Tracking; 7.1 BASIC DEFINITIONS AND RELATIONS; 7.2 LIKELIHOOD RATIO RECURSIONS; 7.3 DECLARING A TARGET PRESENT; 7.4 LOW-SNR EXAMPLES OF LRDT; 7.5 THRESHOLDED DATA WITH HIGH CLUTTER RATE; 7.6 GRID-BASED IMPLEMENTATION. 327 $a7.7 MULTIPLE TARGET TRACKING USING LRDT7.8 iLRT; 7.9 SUMMARY; 7.10 NOTES; Appendix: Gaussian Density Lemma; About the Authors; Index. 330 $aThis book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples that involve the use of particle filters. With these examples illustrating the developed concepts, algorithms, and approaches -- the book helps radar engineers develop tracking solutions when observations are non-linear functions of target state, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target. --$cEdited summary from book. 410 0$aArtech House radar library. 606 $aTracking radar$xMathematics 606 $aBayesian statistical decision theory 608 $aElectronic books. 615 0$aTracking radar$xMathematics. 615 0$aBayesian statistical decision theory. 676 $a621.3848 702 $aStone$b Lawrence D.$f1942- 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910465184203321 996 $aBayesian multiple target tracking$92245128 997 $aUNINA