Statistical Estimation and Tracking of Refractivity from Radar Clutter

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$Refractivty estimation from radar clutter - the Marine Physical ...$

$Statistical Estimation of Refractivity from Radar Sea Clutter$

$Real time refractivity from clutter using a best fit ... - CiteSeerX$

9 tally alter the propagation characteristics, the coverage area, and hence almost all of the important radar parameters as shown in Fig. 1.3. The M-profile structure seen in Fig. 1.3 (a) is a weak evaporation duct. Notice how the value of M increases at a rate of 0.118 M-units/m except for the evaporative region near the surface. Since the evaporation duct is very weak it does not affect the propagation as seen from its coverage diagram obtained using the split-step fast Fourier transform (FFT) parabolic equation (PE). The upward bending effect of the normal propagation conditions can be seen clearly. Since the signal is bend upward, it has minimal interaction with the sea surface resulting in a clear clutter map, defined as the clutter observed on the radar plan position indicator (PPI). 150 (a) -110 150 (b) -110 100 -130 100 -130 50 50 -150 300 350 20 60 100 140 Refractivity (M-unit/m) Range (km) Reflectivity image: March 11, 1998 Map # 031198-20 15:52:33.3 -250 -150 300 350 20 60 100 140 Refractivity (M-unit/m) Range (km) Reflectivity image: April 02, 1998 Map # 040298-17 18:50:00.3 40 -250 40 -200 35 -200 35 -150 -100 -50 -150 30 -100 25 -50 30 25 0 50 100 150 50 km 100 km 150 km 200 km 20 0 50 15 100 10 150 50 km 100 km 150 km 200 km 20 15 10 200 5 200 5 250 -200 -100 0 100 200 0 250 -200 -100 0 100 200 0 Figure 1.3: Vertical M-profiles, coverage diagrams, and clutter maps resulting from (a) a weak evaporation duct and (b) a strong surface-based duct.
10 Fig. 1.3 (b) shows what happens in the event of the formation a lower atmospheric duct. The coverage diagram is now entirely changed with a complex waveguide-like propagation pattern within the duct. Since the electromagnetic wave is trapped, it interacts heavily with the surface, resulting in an increase in the sea clutter observed by the radar. With each bounce, a small portion of the signal is scattered depending on the surface roughness and observed by the radar as shown on the radar PPI. Since the surface bounces occur at certain ranges as shown in the coverage diagram, it results in the formation of clutter rings around the radar on the PPI screen. The RFC techniques introduced in this dissertation uses this effect to estimate and track the atmospheric ducting conditions. Figure 1.4: Effects of ducting on naval and communication systems.(Figure taken from [1]) The propagation changes discussed above result in important deviations in the radar/communication system characteristics as shown in Fig. 1.4. An important effect of ducting is the change in the maximum radar detection/communication channel range. For example, the range can be reduced significantly outside the duct since most of the electromagnetic signal is trapped within the duct. These areas are called radar holes. On the contrary, the maximum detection range within the
Page 1 and 2: UNIVERSITY OF CALIFORNIA, SAN DIEGO
Page 3 and 4: The dissertation of Caglar Yardim i
Page 5 and 6: TABLE OF CONTENTS Signature Page .
Page 7 and 8: 4.5 Discussion . . . . . . . . . .
Page 9 and 10: Figure 2.10: Marginal posterior pro
Page 11 and 12: Figure 4.6: Case Study II: Temporal
Page 13 and 14: ACKNOWLEDGMENTS I would like to gen
Page 15 and 16: VITA 1998 B.S. in Electrical Engine
Page 17 and 18: Major Field: Electrical Engineering
Page 19 and 20: The first part of this thesis discu
Page 21 and 22: 2 models. 2. Electromagnetic Theory
Page 23 and 24: 4 in nature. On the other hand if t
Page 25 and 26: 6 over the coastal regions and the
Page 27: 8 is no more bounded below by the s
Page 31 and 32: 12 Doppler spread radars [5] are an
Page 33 and 34: 14 1.3.1 Tropospheric Propagation U
Page 35 and 36: 16 Now the wave equation can readil
Page 37 and 38: 18 solution for U(r, p) as −p 2
Page 39 and 40: 20 where C accounts for all the con
Page 41 and 42: 22 Figure 1.5: Sea surface grazing
Page 43 and 44: 24 atmospheric index of refraction
Page 45 and 46: 26 implementation of the PF. Both K
Page 47 and 48: 28 [12] Amalia E. Barrios. Paraboli
Page 49 and 50: 2 Estimation of Radio Refractivity
Page 51 and 52: 32 requiring any additional measure
Page 53 and 54: 34 sity (PPD), which can be accompl
Page 55 and 56: 36 Posterior density of any specifi
Page 57 and 58: 38 set up the MCMC such that the de
Page 59 and 60: 40 2.2.4 Monte Carlo Integration Mo
Page 61 and 62: 42 parameter axes, it is hard to sa
Page 63 and 64: 44 2.3.4 Post-Processing After the
Page 65 and 66: 46 Figure 2.5: Initial sampling pha
Page 67 and 68: 48 0.04 c 1 −2.6 −2.4 −2.2 0.
Page 69 and 70: 50 VA Wallops Is. SPANDAR Helicopte
Page 71 and 72: 52 0.05 c 1 0 −1 −0.5 0 M−uni
Page 73 and 74: 54 (a) 200 −100 (d) 200 150 100 5
Page 75 and 76: 56 (a) 0.2 (b) 0.2 0.15 0.15 PPD(F)
Page 77 and 78: 58 d m f(·) f(m) φ(m) n N R N N e
Page 79 and 80:
60 Bibliography [1] S. Vasudevan an
Page 81 and 82:
[25] Julius Goldhirsh and Daniel Do
Page 83 and 84:
64 Hastings (M-H) and Gibbs samplin
Page 85 and 86:
66 dimensional integrals of the joi
Page 87 and 88:
68 where C d is the data error cova
Page 89 and 90:
70 By introducing a weight function
Page 91 and 92:
72 entire search space and there is
Page 93 and 94:
74 3. Gibbs Resampling: Run a fast
Page 95 and 96:
76 Figure 3.2: Voronoi cells and a
Page 97 and 98:
78 (a) 5 c 1 5 5 5 h 2 (b) 0 0.1 0.
Page 99 and 100:
80 density. However, it is sampling
Page 101 and 102:
82 (a) 5 c 1 5 5 5 h 2 (b) 0 0.1 0.
Page 103 and 104:
84 Figure 3.9: An example of range-
Page 105 and 106:
86 full PPD is 16-D, only 1-D (diag
Page 107 and 108:
88 Figure 3.11: Marginal and condit
Page 109 and 110:
90 Table 3.3: Wallops’98 Experime
Page 111 and 112:
92 Propagation Factor, F (dB) Propa
Page 113 and 114:
94 [11] L. Ted Rogers, Michael Jabl
Page 115 and 116:
4 Tracking Refractivity From Clutte
Page 117 and 118:
98 structure. These techniques can
Page 119 and 120:
100 the filter performance still he
Page 121 and 122:
102 ing autocorrelation function. T
Page 123 and 124:
104 in range using the following re
Page 125 and 126:
106 4.3.2 Unscented Kalman Filter T
Page 127 and 128:
108 4.3.3 Particle Filter The last
Page 129 and 130:
110 Let J k be the inverse of the C
Page 131 and 132:
112 Table 4.1: Case Study I: Compar
Page 133 and 134:
Table 4.2: Performance Comparison f
Page 135 and 136:
116 performance. Therefore, it can
Page 137 and 138:
118 error larger than 5 m for any 5
Page 139 and 140:
120 Table 4.3: Performance Comparis
Page 141 and 142:
122 Table 4.4: Case Study III: Coas
Page 143 and 144:
124 Since the layer is thin it has
Page 145 and 146:
126 Bibliography [1] J. R. Rowland
Page 147 and 148:
128 [23] R. A. Paulus. An overview
Page 149 and 150:
5 Conclusions and Future Work 5.1 C
Page 151 and 152:
132 • A hybrid GA-MCMC method bas
Page 153 and 154:
134 Kalman filters performed remark
Page 155:
136 mance predictions of the EKF, U
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Statistical Estimation and Tracking of Refractivity from Radar Clutter

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