Beyond the Kalman Filter: Particle Filters for Tracking Applications

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Artech House
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Contents

III
1
IV
3
V
4
VI
7
VIII
9
IX
10
X
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XI
14
LXXIX
150
LXXX
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LXXXI
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LXXXII
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LXXXIII
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LXXXIV
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LXXXV
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LXXXVI
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XII
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XIV
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XV
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XVI
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XIX
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XX
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XXI
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XXII
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XXIV
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XXV
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XXVI
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XXVII
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XXVIII
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XXIX
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XXXI
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XXXII
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XXXIII
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XXXIV
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XXXV
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XXXVI
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XXXVIII
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XXXIX
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XL
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XLI
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XLII
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XLIII
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XLIV
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XLVI
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XLVII
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XLVIII
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L
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LI
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LII
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LIII
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LIV
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LV
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LVI
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LVII
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LVIII
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LIX
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LX
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LXI
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LXIII
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LXV
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LXVI
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LXVII
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LXIX
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LXX
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LXXII
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LXXIII
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LXXIV
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LXXV
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LXXVI
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LXXVII
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LXXXVII
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LXXXVIII
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LXXXIX
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XC
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XCI
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XCIII
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XCIV
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XCV
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XCVI
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XCVIII
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XCIX
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C
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CI
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CII
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CIII
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CIV
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CV
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CVI
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CVII
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CVIII
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CIX
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CX
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CXI
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CXII
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CXIV
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CXVI
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CXVII
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CXVIII
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CXIX
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CXX
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CXXI
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CXXII
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CXXIII
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CXXIV
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CXXV
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CXXVII
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CXXVIII
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CXXIX
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CXXX
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CXXXI
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CXXXII
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CXXXIII
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CXXXIV
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CXXXV
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CXXXVI
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CXXXVII
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CXXXVIII
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CXXXIX
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CXL
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CXLI
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CXLII
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CXLIV
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CXLV
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CXLVI
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CXLVII
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Page 16 - Y. Bar-Shalom, XR Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation. New York: John Wiley & Sons,
Page 4 - a measurement is received. In this case a recursive filter is a convenient solution. A recursive filtering approach means that received data can be processed sequentially rather than as a batch, so that it is not necessary to store the complete data set
Page 4 - to reprocess existing data if a new measurement becomes available. Such a filter consists of essentially two stages: prediction and update. The prediction stage uses the system model to predict the state pdf
Page 4 - ideally suited for the Bayesian approach. This provides a rigorous general framework for dynamic state estimation problems. In the Bayesian approach to dynamic state estimation one attempts to construct the

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