Diss / (Springer Series in Information Sciences 25) S. Haykin, J. Litva, T. J. Shepherd (auth.), Professor Simon Haykin, Dr. John Litva, Dr. Terence J. Shepherd (eds.)-Radar Array Processing-Springer-Verlag
.pdfEigenproblem, generalized |
72 |
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Eigenvalue |
119 |
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- analysis |
228 |
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Eigenvector |
119 |
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- two-dimensional (20) |
271 |
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End-element-clamped 159, 160 |
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Energy management |
48 |
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Error probabilities |
67 |
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Error-feedback algorithm |
172 |
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ESPRIT algorithm |
72 |
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Expected maximization (EM) algorithm 126
Exponential |
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deweighting |
236 |
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time window |
160, 161 |
Fast Givens algorithm see Givens rotations
Fast Kalman algorithm |
175, 180 |
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Fisher information matrix (FIM) |
113 |
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Forget factor |
160 |
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Forward-backward |
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averaging |
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57 |
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linear prediction (FBLP) |
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59, 69 |
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Fourier transform |
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discrete |
86 |
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fast (FFT) |
86 |
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Frequency |
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estimation |
86 |
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modulation 91 |
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Frobenius norm |
112 |
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Frozen mode |
185, 187, 195, 196,206 |
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Gain |
159, 193, 198, 211, 220, 227 |
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vector 194 |
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Gauss normal equation |
161, 164 |
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Gauss-Newton method |
126, 128 |
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Gaussian process |
174, 180 |
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General monopulse formula |
54 |
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Generalized likelihood ratio |
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12,13,20,23,27, |
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36 |
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test (GLRT) |
23, 29, 133, 134 |
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Generalized log-likelihood ratio |
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31,41 |
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Gentleman-Kung see array |
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Givens |
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fast |
172 |
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rotations |
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157, 164-166, 167, 168, 169, 173, |
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176, |
178, |
179, |
184, |
185, |
187, |
192, |
195, |
204, |
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207, 208, 209, 215, 221, 227, 232, 233 |
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Glint errors |
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50 |
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GLRT see generalized likelihood ratio |
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Gradient descent |
154, 218 |
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Gram-Schmidt |
231 |
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modified |
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153, 172, 185,229-232 |
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procedure |
71 |
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Subject Index |
313 |
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recursive modified (RMGS) |
232 |
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Grating lobe problem |
84 |
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Hadamard product |
114 |
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Hidden units |
222, 232 |
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Hilbert space |
153 |
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Householder transformations |
164 |
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Howells-Applebaum algorithm |
251 |
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two-dimensional (20) 268 |
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Hyperbolic rotations |
226,235-241 |
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Hypotheses |
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nested |
79 |
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testing |
79 |
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Identifiability of parameters 107 |
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Ill-conditioning |
162, 163, 179, 227 |
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Imaging |
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1 |
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In-phase (I) 211, 227, 308 |
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Information theoretic |
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tests |
67 |
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criteria |
80 |
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Information filter |
228 |
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Initialization |
132 |
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Instability |
295 |
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Interferometer |
301,303 |
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Internal cells (nodes) |
166, 168, 171, 172, 182, |
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185,204,207, 209,221,260 |
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Inverse |
synthetic aperture radar (ISAR) |
51, |
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87,301, 302 |
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Iterative quadratic ML (lQML) see maximum likelihood
J-orthogonal rotation |
238 |
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Jacobi rotations |
228 |
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Jamming |
154, 155, 174,213,227 |
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Joint estimator |
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221 |
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Kalman filter |
49, 228 |
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Kogbetliantz transformations |
228 |
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Kumaresan and Tufts method |
59, 69, 88, 93 |
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Kung-Gentleman see array |
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Lattice |
222 |
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multichannel, 221, 222 |
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Least-squares |
153, 156, 157, 163, 164, 166, |
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166, |
167, |
173, |
175, |
188, |
190, |
192, |
200, |
218, |
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219, 222, 223, 225, 230, 236 |
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constrained |
190, 195, |
197, |
198, |
201, |
218, |
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233 |
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fast |
221 |
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fit 222 |
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formulation |
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160-163 |
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minimization |
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155, 192, 221, 229, 234 |
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weighted |
120, 173, 185,228,235-241 |
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314 Subject Index
Least-squares lattice 175, 227
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algorithm |
180,221 |
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Levenburg-Marquardt |
126 |
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Lexicographic ordering |
269 |
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Likelihood |
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function |
8, 11, 21, 76 |
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ratio test |
65, 67, 79 |
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Linear prediction 58, 70, 221, 227
LMS (least mean squares) algorithm 154,251
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two-dimensional (2D) |
266 |
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Log-likelihood |
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function |
112 |
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variable |
180 |
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Look direction |
159, 196, 198, 200, 201 |
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constraint see constraint |
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Low angle tracking |
75, 84, 93 |
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Low sidelobe |
86 |
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Lower triangular matrix |
176 |
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Main beam nulling |
50 |
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Marple method |
59 |
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Matched filter |
52, 88, 90 |
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Matrix |
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banded |
227 |
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rectangular |
182, 184, 195 |
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trapezoidal |
183, 193, 195,235 |
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triangular 166, 167, 168, 180, 196,202,205, |
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215,216, 227, 236, 239 |
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-unitary see unitary
-weighting see weighting Maximum likelihood (ML)
-deterministic (DML) 108, 112, 111-113, 116-117,125,128
-estimate 13, 15,20,22,30,53,56,66,91,94,
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115 |
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iterative quadratic (lQML) 126 |
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method |
38, 108 |
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method based on |
39 |
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principle |
73 |
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pseudo |
24, 25, 26, 28, 30, 34, 35, 36, 37, 65 |
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stochastic (SML) |
108, 109-111, 127-128 |
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true 27,73 |
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Maximum entropy (ME) 59, 88, 93
Method of direction estimation (MODE) 118
Metric tensor 238 |
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Minimum description length (MDL) |
15, 133 |
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- criterion |
59, 67, 80 |
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Minimum risk estimate |
56 |
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Minimum |
variance distortionless |
response |
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(MVDR) |
157, 200-209, 215 |
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Model order selection |
15 |
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- problem |
32, 42 |
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Modified Gram-Schmidt (MGS) algorithm see Gram-Schmidt
Monopulse |
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estimation |
52, 53 |
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ratio 54, 78 |
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Monostatic radar see radar |
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Motion compensation 88 |
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Multichannel |
221 |
Multidimensional interpolation 224 Multifunction operation 48
Multipath |
119 |
- anti-phase |
50 |
Multipath error 49
Multiple alternative hypothesis testing 14, 31, 41
Multiple constraints see constraints
MUSIC algorithm |
65, 74, 88, 93, 118, 125 |
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estimator |
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121 |
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root |
72 |
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MVDR |
see |
minimum |
variance |
distortionless |
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response |
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Narrow-band |
220, 221 |
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Narrowband signals |
103 |
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Network |
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fixed (frozen) |
181-185, 186, 187, 192,215 |
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rectangular |
182, 183 |
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trapezoidal |
183, |
194, 195 |
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triangular |
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183, 192 |
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Neural networks |
222 |
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Newton-type methods |
78, 126, 127 |
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Noise cancellation |
153 |
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Noncoherent radar detection |
16, 133-137 |
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of emitters with unknown directions, |
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deterministic signal |
22-28 |
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of emitters with unknown directions, |
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Gaussian signal |
28-33 |
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of targets of known directions |
17-22 |
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Nonlinear |
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adaptive filter |
157, 225 |
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-:: least-squares |
112 |
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Nonstationary |
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228 |
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Normal equation see Gauss |
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Null space |
119, 197 |
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Null tracker |
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79 |
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double |
80 |
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Null-steering |
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153 |
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Numerical accuracy see accuracy |
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Numerical stability see stability |
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Nyquist sampling theorem |
I |
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Observation model |
4-6 |
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Open loop |
155 |
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Orthogonal decomposition |
163, 193,202,226, |
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227, 228 |
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Orthogonal |
triangularization see orthogonal |
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decomposition |
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Orthogonality errors |
85 |
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Oscillator, local 295 |
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Parallel |
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- processing |
155 |
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weight (vector) extraction 157, 169,215-218, |
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220,225 |
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Parallelogram network |
215, 216 |
Parametric target model fitting (PTMF) 73,91
Passive radar detection |
33-43 |
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deterministic signal 35-40 |
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of emitters with known directions |
33-35 |
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Gaussian signal |
40-43 |
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Perceptron |
222, 224 |
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Phase |
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conjugation |
298 |
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detector |
295 |
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error 295, 296, 304, 305, 309 |
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modulation |
91 |
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Phased array |
295, 296 |
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radar, ELRA |
92 |
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Pipelined |
201,221,225,227 |
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Pisarenko method |
72 |
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Polyphase codes |
91 |
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Post-detection validation |
93 |
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Post-processor |
157, 215, 226 |
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Power inversion |
158, 159 |
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Pre-processor |
157, 159, |
173, 192, |
194, 195, |
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198, 199, 223, 226,235 |
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-constraint see constraint Pre-whitening 64, 76 Prediction filter vector 58 Prediction schemes 61 Primary
-channel 159, 161, 191, 194, 218, 219,220,
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222 |
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input |
188 |
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signal |
158, |
160, 162 |
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Processor see array |
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Projection |
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alternating |
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132 |
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decomposition lemma 77 |
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Hung-Turner (HT) 67 |
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matrix |
65, 74 |
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methods |
65 |
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operators |
229 |
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Yeh-Brandwood 69 |
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Prony method |
77 |
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Pseudoinverse |
223 |
QR decomposition (QRD, QRDLS) 71, 92, 153, 156, 157, 163-175, 178, 185, 187, 189,
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Subject Index |
315 |
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193, |
195, |
197, |
200, |
201, |
202, |
204, |
205, |
215, |
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216, |
218, |
219, |
221, |
222, |
223, |
225, |
226, |
227, |
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228,229, 231, 232, 233, 234, 236, 251 |
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by Givens rotations |
163-175 |
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QR iterations |
228 |
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QR-with feedback |
218-220 |
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Quadrature (Q) |
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211, 227, 308 |
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Quiescent beam |
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profile |
200 |
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shape |
198 |
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Radar, monostatic |
301 |
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Radial basis function (RBF) 223 |
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Radio camera |
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295, 301, 307, 309 |
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Range |
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bin |
297, 305 |
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estimation |
90 |
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Range-Doppler ambiguity |
91 |
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Rayleigh |
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amplitudes |
75 |
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distribution |
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17 |
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estimate |
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62 |
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Real arithmetic |
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227 |
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Reference signal |
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158, 159 |
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channel |
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161, 304 |
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Residual |
157, |
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160, |
161, |
175, |
178, |
179, |
188, |
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215, 224, 227, 235, 236 |
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a posteriori |
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175, 180, 187, 188, 191, 194, |
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201,202,204,234,235 |
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a priori |
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175,189 |
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constrained |
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194 |
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extraction see direct residual extraction |
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least-squares |
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168, |
169, 180 |
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rationalized |
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180 |
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vector |
164,189,190,191,197,198,201,207, |
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221,223,227,234,236 |
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Right-hand column |
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1,66, 167, 168, 173, 178, |
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181, |
185, |
187, 189, 198, 204, 209, 219, 224 |
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Sample matrix inversion (SMI) |
173, 174, 175, |
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251 |
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Schreiber's algorithm |
201-204 |
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Schur product |
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114 |
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Scoring method |
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127, 130 |
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Self-calibrating antennas |
85 |
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Sequential detector |
48 |
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Sequential multi-hypotheses test |
80 |
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Serial weight flushing |
169 |
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Sidelobe |
158,295, 306, 307, 308 |
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cancellation |
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158, 160 |
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generalized - canceller 196-200 |
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Signal |
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demodulated |
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296 |
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fading |
50 |
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316 |
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Subject Index |
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Signal flow graph |
181, 182, 183, 192, 194,224 |
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Signal and noise model |
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for noncoherent radar detection |
16-17 |
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for passive radar detection |
33 |
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Signal to noise ratio |
174, 213, 307 |
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Signal subspace methods |
64 |
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Simple array see array |
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Singular values |
163,228 |
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Singular value decomposition (SVD) |
71,228 |
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Skew (time-, temporal ciock-) see clock |
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SMI see sample matrix inversion |
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SML see maximum likelihood |
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Spatial processing |
I |
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Spatial smoothing |
57 |
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Sphericity test |
65 |
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Square-root-free |
185, 187, 189, 192, 196,207, |
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232,233 |
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- |
algorithm |
169-175,179,180,184,185,195, |
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|
208,209,235,240,241,262 |
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Stability |
227 |
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- |
numerical |
179, 203, 228 |
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Standard monopulse formulas |
55 |
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Steering vector |
52 |
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Stochastic approximation |
83 |
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- |
methods |
78 |
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Stochastic Newton method 78 |
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Sub-residual |
188, 189 |
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Subarray |
188, 250 |
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Subarraying |
247 |
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Subspace |
1I8 |
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- |
fitting |
129 |
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- |
noise |
|
119, 120 |
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- |
signal |
119 |
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Superarray |
84 |
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Superresolution |
1, 48 |
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Swerling II target |
19 |
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- |
fluctuating target model |
17, 19,22 |
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Synthetic aperture radar (SAR) |
51, 87 |
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Systolic |
168,210,211,221,222,228,229,236 |
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- |
array |
153,155-157, 166, 168, 171, 172, 175, |
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|
178, |
179, |
181, |
186, |
195, |
200, |
201, |
204, |
207, |
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209, 210, 213, 225,226, 249 |
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- |
Kalman filtering |
218 |
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- |
McWhirter 71,92 |
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- |
processor |
71, 197,227 |
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- |
structure |
153 |
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- |
triangular see array |
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Target complex amplitude |
52 |
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Target location |
160 |
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Target power estimation |
94 |
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Test-bed |
157, 210-215 |
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Thermal noise |
174 |
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Thermal noise algorithm |
62 |
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Three-dimensional (3D) systolic structure 278 Threshold tests 60
Time-staggered see ciock Time-staggered 207 Toeplitz structure 57 Track processor 49 Tracking 160 Triangular
-array see array
-matrix see matrix Triangulation 50 TWAP
-triangular WAP see WAP Two-dimensional
- |
adaptive beamforming 249 |
- |
antenna arrays 226 |
-eigenvector beam see eigenvector
-Howells-Applebaum see Howells-Applebaum
-LMS algorithm see LMS
- |
modelling MEjAR 61 |
- |
prediction 61 |
- |
Wiener-Hopf equation 267 |
Unambiguous
-array see array
-parameterization 107 Unconditional stability 179 Uncorrelated targets 85
Uniform linear array (ULA) 104, 106, 129-130 Uniformly most powerful test (UMP) 10 Unitary
-matrix 163, 164, 165, 166, 188, 193, 203, 216,231,234
-transformation 163, 165
Upper triangular 165, 232 |
||
- |
matrix |
163, 164, 169, 177, 181, 185 |
- |
unit 171, 185 |
|
Vandermonde |
||
- |
matrix |
129, 196 |
- |
vectors |
105 |
VLSI (very large scale integration) |
155, 156 |
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Wavefront array see array |
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Wavefront array processor (WAP) |
210-215 |
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Weight freezing |
180-189 |
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Weight flushing |
180-189,220 |
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Weight |
158, 304 |
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- |
optimum (least-squares) |
157, 162, 164, 166, |
|||||||
|
173, |
174, |
175, |
178, |
179, |
185, |
187, |
190, |
194, |
|
204, 215, 218, 219, 230 |
|
|
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- |
vector 154, 159, |
161, |
162, |
164, |
168, |
169, |
|||
|
174, |
175, |
180, |
181, |
185, |
186, |
189, |
190, |
191, |
Weight (continued)
192, 193, 194, 197, 215, 223, 234, 236, 298, 305
Weighted least-squares see least-squares Weighted subspace fitting (WSF) 118, 125,
130, 132, 135 - detection 136
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|
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Subject Index |
317 |
Weighting matrix |
236 |
|
||
White noise test |
66, 80, 83 |
|
||
Window functions |
86 |
|
||
- |
Hamming 86 |
|
|
|
- |
Hanning |
86 |
|
|
- |
Parzen |
86 |
|
|
WSF see weighted subspace fitting