Space/time/frequency methods in adaptive radar - New Jersey ...

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75Figure 4.2 Reduced-rank GSCThis minimization results inUsing Equations 4.4 and 4.7, the overall GSC weight vector is then given bywhere I N is the N-dimensional identity matrix. The output SINR (when targetVarious choices of the rank reducing transformation U are now considered:1. One such choicewhere Qr consists of the r largest eigenvectors of R. Assuming that the(N — 1) x N signal blocking matrix A has full column rank, the elements ofU can be obtained from the solution of a least-squares problem. It is easy toshow that with this choice of U:
762. To avoid the complication, of a least-squares problem, let the matrix U berestricted to consist of r of the (N — 1) eigenvectors of R a ARA, whereA natural choice would be tolet the rank reducing transformation consist of the r principal eigenvectors of3. For a known covariance matrix R, an optimal approach that maximizes theoutput SINR for a given rank r, is suggested in [21, 22]: construct U from ther eigenvectors of Ra that maximize the quantitywhere qi, λi arerespectively eigenvectors and eigenvalues of Ra. In thereferences, this method is referred as the cross-spectral metric (CSM) method.4. Principal components decomposition, such as considered above, is datadependent. Fixed, reduced-rank transformations can be constructed byselecting the principal components of the discrete Fourier transform (DFT)or the discrete cosine transform (DCT). The cross spectral metric can also beused in conjunction with these fixed transforms [21, 22].Next, rank-reducing transformations are evaluated in the MVB framework.Consider the SINR at the MVB output, as given by Equation 4.3. When the transformationT is unitary, it has no effect on the output SINR, andany N x r rank reducing transformationSpecific examples are considered below.1. Consider how Case .1 of the GSC translates to the MVB framework. By substitutingEquation 4.10 in 4.8, we obtain the equivalent MVB weight vector
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Copyright Warning & RestrictionsThe
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ABSTRACTSPACE/TIME/FREQUENCY METHOD
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SPACE/TIME/FREQUENCY METHODS IN ADA
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APPROVAL PAGESpace/Time/Frequency M
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ACKNOWLEDGMENTI would like to begin
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ChapterPage3.2.1 Non-Adaptive Beamf
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FigurePage2.22 Scalogram of a linea
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CHAPTER 1INTRODUCTIONVarious space,
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3the target. In this work we study
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5support consists of a population o
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7processing. This is explained by t
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92.1 An Overview of Synthetic Apert
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11Figure 2.2 SAR cross-range Dopple
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13Through the expression for the on
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15Illumination pattern onEarth's su
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17Figure 2.6 Time-frequency descrip
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19Figure 2.7 Computation of the sho
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21Figure 2.9 Spectrogram of a multi
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23where 9/ is the Hilbert transform
Page 40 and 41: Figure 2.11 Gabor Transform of a si
Page 42 and 43: 272.3.3 Continuous Wavelet Transfor
Page 44 and 45: 29Figure 2.13 Time-Frequency resolu
Page 46 and 47: Figure 2.14 Scalogram of a sine wav
Page 48 and 49: Figure 2.16 Regions of influence co
Page 50 and 51: Figure 2.17 WVD transform of a sine
Page 52 and 53: 37Figure 2.18 Example of WVD cross
Page 54 and 55: 39(a) Contour plot of the WVD of a
Page 56 and 57: 41(a) Contour plot of the STFT of a
Page 58 and 59: 43(a) Contour plot of the Gabor exp
Page 60 and 61: 452.5 Time-Frequency Analysis in No
Page 62 and 63: 47Consequently, when the slope S, i
Page 64 and 65: 49where:Doppler phase shift due to
Page 66 and 67: 51(a) Contour plotFigure 2.24 SAR i
Page 68 and 69: 53(a) Contour plotFigure 2.26 SAR i
Page 70 and 71: 55calculated in each case. In this
Page 72 and 73: 57(a) Moving target with synthetic
Page 74 and 75: Figure 2.31 SAR with 2 moving targe
Page 76 and 77: 61Figure 3.1 Space-time adaptive ar
Page 78 and 79: 63Figure 3.3 Airborne radar basic g
Page 80 and 81: subspace is formed using the remain
Page 82 and 83: 67where k is a gain constant and R
Page 84 and 85: 69The scalar beamformed noise estim
Page 86 and 87: 71are the desired signal, interfere
Page 88 and 89: CHAPTER 4REDUCED-RANK PROCESSINGThe
Page 92 and 93: 77This relation establishes an equi
Page 94 and 95: 79The conditioned SNR is a random v
Page 96 and 97: 81The performance of the GSC proces
Page 98 and 99: 83For large interference eigenvalue
Page 100 and 101: 85the steering vector s, and the ve
Page 102 and 103: 87(a) CSNR vs Rank order for other
Page 104 and 105: 89Figure 5.4 CSNR vs Rank order for
Page 106 and 107: 91Figure 5.6 Theoretical and simula
Page 108 and 109: 93Figure 5.8 CSNR vs CNRThe signal
Page 110 and 111: 950.80.80.6 0.6~(5'0.4IC!J0.40.20.2
Page 112 and 113: 97the spatial domain. The target is
Page 114 and 115: 99(a) Training outside the target r
Page 116 and 117: 101(a) Training around the target r
Page 118 and 119: 103Figure 5.15 Angle scan with the
Page 120 and 121: CHAPTER 6CONCLUSIONSThis work has b
Page 122 and 123: 107also presented. The Sample Matri
Page 124 and 125: APPENDIX ADISCRETE COSINE TRANSFORM
Page 126 and 127: 111This matrix is formed using the
Page 128 and 129: REFERENCES1. Leon Cohen. Time-frequ
Page 130 and 131: 11523. Franz Hlawatsch and G. Faye
Page 132 and 133: 11750. Daniel F. Marshall. A two st
Page 134 and 135: INDEXanalytic signal, 21array imper
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