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The GNSS integer ambiguities: estim
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The GNSS integer ambiguities: estim
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Contents Preface v Summary vii Same
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4 Best Integer Equivariant estimati
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Preface It was in the beginning of
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• No attempt is made to fix the a
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Samenvatting (in Dutch) De GNSS geh
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gelegd door de keuze van de maximaa
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d, δ instrumental delay for code a
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Acronyms ADOP Ambiguity Dilution Of
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in practice, a user has to choose b
- Page 31 and 32: GNSS observation model and quality
- Page 33 and 34: Table 2.2: Signal and frequency pla
- Page 35 and 36: 2.2.2 Phase observations The phase
- Page 37 and 38: the propagation delay does not depe
- Page 39 and 40: 2.2.9 The geometry-based observatio
- Page 41 and 42: 2.3.2 Single difference models If o
- Page 43 and 44: and difficult to predict. Therefore
- Page 45 and 46: The double difference observation v
- Page 47 and 48: (appendix A.1): Qy = C sd pφ ⊗ D
- Page 49 and 50: The quality of the solution can the
- Page 51: is maximum. The degrees of freedom
- Page 54 and 55: * â Figure 3.1: An ambiguity pull-
- Page 56 and 57: 2 1.5 1 0.5 0 −0.5 −1 −1.5
- Page 58 and 59: estimator is given by: n Sz,B = x
- Page 60 and 61: 7 6 5 4 3 2 1 0 −1 −2 −3 −4
- Page 62 and 63: Table 3.1: Overview of ambiguity re
- Page 64 and 65: 1 0.5 0 −2 −1 0 1 z 2 1 0 −1
- Page 66 and 67: and has units of cycles. It was int
- Page 68 and 69: chosen equal to half the number of
- Page 70 and 71: Table 3.2: Two-dimensional example.
- Page 72 and 73: The bias-affected success rate is a
- Page 74 and 75: is close to one. This probability c
- Page 76 and 77: 2 0 −2 0.5 0 −0.5 2 0 −2 −4
- Page 78 and 79: error in pdf error in pdf error in
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- Page 84 and 85: Unfortunately, this relatively simp
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- Page 88 and 89: where χ2 α(m−p, 0) is the criti
- Page 90 and 91: Table 3.5: Overview of all test sta
- Page 92 and 93: with the weight function wz(â) = 1
- Page 94 and 95: IE IEU IU LU Figure 4.1: The set of
- Page 96 and 97: 4.2 Approximation of the BIE estima
- Page 98 and 99: 4.3 Comparison of the float, fixed,
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- Page 102 and 103: ambiguity residual 0.5 0.4 0.3 0.2
- Page 104 and 105: Table 4.1: Probabilities P1 = P (|
- Page 106 and 107: Table 4.2: Probabilities Ps = P (ǎ
- Page 108 and 109: Table 4.4: Probabilities that float
- Page 110 and 111: probability probability 1 0.9 0.8 0
- Page 113 and 114: Integer Aperture estimation 5 In se
- Page 115 and 116: can be distinguished: â ∈ Ωa s
- Page 117 and 118: and the hybrid distribution of ā i
- Page 119 and 120: Figure 5.3: 2-D example for EIA est
- Page 121 and 122: as follows: Ωz,R = ΩR ∩ Sz =
- Page 123 and 124: 3 2.5 2 1.5 1 0.5 0 −0.5 −1 −
- Page 125 and 126: 5.3.3 Difference test is an IA esti
- Page 127 and 128: Figure 5.9: 2-D example for DTIA es
- Page 129 and 130: Figure 5.11: 2-D example for PTIA e
- Page 131 and 132: Figure 5.12: 2-D example for IAB es
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2-D example Figure 5.13 shows in bl
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µ 200 100 10 2 1 1 1.1 1.2 1.3 1.4
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5.6 Optimal Integer Aperture estima
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The optimization problem of (5.49)
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Table 5.1: Comparison of IA estimat
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µ 1.5 1.45 1.4 1.35 1.3 1.25 1.2 1
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In order to show that the fail rate
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success rate percentage identical 0
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probability probability 1 0.9 0.8 0
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5.8.2 OIA estimation versus RTIA an
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µ µ µ 0.7 0.6 0.5 0.4 0.3 0.2 0.
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0.6 0.4 0.2 0 −0.2 −0.4 −0.6
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three vc-matrices which are scaled
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Table 5.3: Comparison of IAB and IA
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success rate if fixed 1 0.95 0.9 0.
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Table 5.4: Overview of IA estimator
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IE IA I Figure 6.1: The set of rela
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convex region symmetric with respec
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Mathematics and statistics A A.1 Kr
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The expectation, E{x}, and the disp
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Choose the tolerance ε. Determine
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Simulation and examples B Throughou
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Theory of BIE estimation C In chapt
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The first term on the right-hand si
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can be derived: L = {2h(x) T Q
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1. Choose fixed fail rate: Pf = β
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Bibliography Abidin HA (1993). Comp
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Jung J, Enge P, Pervan B (2000). Op
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Teunissen PJG (2003g). Towards a un
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Index adjacent, 33 ADOP, 42 alterna