American Institute of Mathematical Sciences

Advanced
June  2018, 12(3): 697-731. doi: 10.3934/ipi.2018030

SAR correlation imaging and anisotropic scattering

Kaitlyn (Voccola) Muller ,

Department of Mathematics and Statistics, Villanova University, Villanova, PA 19085, USA

Corresponding author: k.muller@villanova.edu

Received  August 2017 Revised  December 2017 Published  March 2018

Fund Project: The author was supported by AFOSR grant FA9550-14-1-0124.

In this paper we investigate the ability of correlation synthetic-aperture radar (SAR) imaging to reconstruct isotropic and anisotropic scatterers. SAR correlation imaging was suggested by the author previously in [34]. Correlation imaging algorithms produce an image of a second-order quantity describing an object an interest, for example, the reflectivity function squared. In the previous work [34] it was argued that the effects of volume scattering clutter on the image can be minimized by choosing which pairs of collected data to correlate prior to applying a backprojection-type reconstruction algorithm. This choice of pairs for the correlation process is determined by what is known as the memory effect of scattering of waves by random scatterers [42,43,40,41,7,14]. It is the goal of this current work to determine the different imaging outcomes for an isotropic or point scatterer versus an anisotropic or dipole scatterer. In addition we aim to determine if removing contributions to the image due to the memory effect is necessary for diminishing the contributions of anisotropic or clutter scatterers to the scene of interest. Finally we extend the analysis of [34] to the polarimetric SAR case to determine whether the additional data provided by this modality contributes to decreasing the effects of clutter on the SAR image.

Keywords: Synthetic aperture radar, polarimetric radar, correlation imaging, clutter mitigation.
Mathematics Subject Classification: Primary: 78A46, 94A13; Secondary: 35Q60.
Citation: Kaitlyn (Voccola) Muller. SAR correlation imaging and anisotropic scattering. Inverse Problems & Imaging, 2018, 12 (3) : 697-731. doi: 10.3934/ipi.2018030
References:
[1]

J. G. Berryman, L. Borcea, G. Papanicolaou and C. Tsogka, Statistically stable ultrasonic imaging in random media, J. Acoust. Soc. Am., 112 (2002), 1509-1522.  doi: 10.1121/1.1502266.  Google Scholar

[2]

W. M. Boerner, M. B. El-Arini, C. Y. Chan and P. M. Mastoris, Polarization dependence in electromagnetic inverse problems, IEEE Trans. on Antennas and Propagation, 29 (1981), 262-271.  doi: 10.1109/TAP.1981.1142585.  Google Scholar

[3]

L. Borcea, G. Papanicolaou and C. Tsogka, Adaptive interferometric imaging in clutter and optimal illumination, Inverse Problems, 22 (2006), 1405-1436.  doi: 10.1088/0266-5611/22/4/016.  Google Scholar

[4]

L. Borcea, G. Papanicolaou and C. Tsogka, Interferometric array imaging in clutter, Inverse Problems, 21 (2005), 1419-1460.  doi: 10.1088/0266-5611/21/4/015.  Google Scholar

[5]

L. Borcea, M. Moscoso, G. Papanicolaou and C. Tsogka, Synthetic aperture imaging of direction and frequency dependent reflectivities, SIAM J. Imaging Sci., 9 (2016), 52-81.  doi: 10.1137/15M1036063.  Google Scholar

[6]

L. Borcea, G. Papanicolaou and C. Tsogka, Theory and applications of time reversal and interferometric imaging, Inverse Problems, 19 (2003), S139-S164.  doi: 10.1088/0266-5611/19/6/058.  Google Scholar

[7]

T.-K. Chan, Y. Kuga and A. Ishimaru, Subsurface detection of a buried object using angular correlation function measurement, Waves Random Media, 7 (1997), 457-465.  doi: 10.1080/13616679709409809.  Google Scholar

[8]

R. D. Chaney, M. C. Burl and L. M. Novak, On the Performance of Polarimetric Target Detection Algorithms, IEEE International Radar Conference, 1990. doi: 10.1109/RADAR.1990.201114.  Google Scholar

[9]

M. Cheney and B. Borden, Fundamentals of Radar Imaging, SIAM, Philadelphia, 2009.  Google Scholar

[10]

S. R. Cloude and E. Pottier, A review of target decomposition theorems in radar polarimetry, IEEE Trans. on Geoscience and Remote Sensing, 34 (1996), 498-518.  doi: 10.1109/36.485127.  Google Scholar

[11]

A. J. Devaney, The inverse problem for random sources, J. Math. Phys., 20 (1979), 1687-1691.  doi: 10.1063/1.524277.  Google Scholar

[12]

R. L. Dilsavor and R. L. Moses, Fully-Polarimetric GLRTs for Detecting Scattering Centers with Unknown Amplitude, Phase, and Tilt Angle in Terrain Clutter, in SPIE's International Symposium on Optical Engineering in Aerospace Sensing, Orlando, FL, 1994. Google Scholar

[13]

D. E. Dudgeon, R. T. Lacoss, C. H. Lazott and J. G. Verly, Use of Persistent Scatterers for Model-Based Recognition, Proc. SPIE 2230, Algorithms for Synthetic Aperture Radar Imagery, 1994. Google Scholar

[14]

A. E. El-Rouby, A. Y. Nashashibi and F. T. Ulaby, Application of frequency correlation function to radar target detection, IEEE Trans. Aero. Elec. Sys., 39 (2003), 125-139.  doi: 10.1109/TAES.2003.1188898.  Google Scholar

[15]

E. Ertin, L. C. Potter and R. L. Moses, Enhanced imaging over complete circular apertures, Signals, Systems, and Computers, (2006).  doi: 10.1109/ACSSC.2006.355025.  Google Scholar

[16]

S. Feng, C. Kane, P. A. Lee and A. D. Stone, Correlations and fluctuations of coherent wave transmission through disordered media, Phys. Rev. Lett., 61 (1988), 834-837.  doi: 10.1103/PhysRevLett.61.834.  Google Scholar

[17]

L. Ferro-Famil, A. Reigber, E. Pottier and W. M. Boerner, Scene characterization using subaperture polarimetric SAR data, IEEE Trans, on Geoscience and Remote Sensing, 41 (2003), 2264-2276.  doi: 10.1109/TGRS.2003.817188.  Google Scholar

[18]

A. Freeman and S. L. Durden, A Three-Component Scattering Model for Polarimetric SAR data, IEEE Trans. Geosci. Remote Sensing, 36 (1998), 963-973.  doi: 10.1109/36.673687.  Google Scholar

[19]

A. C. Frery, H. J. Muller, C. C. F. Yanasse and S. J. S. Sant'Anna, A model for extremely heterogeneous clutter, IEEE Tran. Geosci. Remote Sensing, 35 (1997), 648-659.  doi: 10.1109/36.581981.  Google Scholar

[20]

I. Freund, Correlation Imaging through multiply scattering media, Phys. Lett. A., 147 (1990), 502-506.  doi: 10.1016/0375-9601(90)90615-U.  Google Scholar

[21]

J. Garnier and K. Solna, Coherent interferometric imaging for synthetic aperture radar in the presence of noise, Inverse Problems, 24 (2008), 055001, 23 pp.  Google Scholar

[22]

A. Grigis and J. Sjostrand, Microlocal Analysis for Differential Operators: An Introduction, London Mathematical Society Lecture Note Series, 196. Cambridge University Press, Cambridge, 1994.  Google Scholar

[23]

M. Gustafsson, Multi-static Synthetic Aperture Radar and Inverse Scattering, Technical Report LUTEDX, TEAT-7123, 2004. Google Scholar

[24]

J. R. Huynen, Phenomenological theory of radar targets, Electromagnetic Scattering, (1978), 653-712.  doi: 10.1016/B978-0-12-709650-6.50020-1.  Google Scholar

[25]

J. Jackson and R. Moses, Clutter model for VHF SAR imagery, Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery Ⅺ, 271 (September 2, 2004). Google Scholar

[26]

P. Li, An inverse random source scattering problem in inhomogeneous media, Inverse Problems, 27(2011), 035004, 22pp.  Google Scholar

[27]

A. Mahalanobis, A. Forman, M. Bower, N. Day and R. Cherry, Multi-class SAR ATR using shift-invariant correlation filters, Patter Regonition, 27 (1994), 619-626.  doi: 10.1016/0031-3203(94)90041-8.  Google Scholar

[28]

L. M. Novak, G. J. Owirka and C. M. Netishen, Radar target identification using spatial matched filters, Pattern Regonition, 27 (1994), 607-617.  doi: 10.1016/0031-3203(94)90040-X.  Google Scholar

[29]

H. L. Royden, Real Analysis, Pearson, New York, 2010. Google Scholar

[30]

M. Soumekh, Synthetic Aperture Radar Signal Processing, John Wiley and Sons Inc., New York, 1999. Google Scholar

[31]

M. E. Taylor, Pseudodiferential Operators, Princeton University Press, Princeton, NJ, 1981.  Google Scholar

[32]

L. C. Trintinalia, R. Bhalla and H. Ling, Scattering center parametrization of wide-angle backscattered data using adaptive Gaussian representation, IEEE Trans. Ant. Prop., 45(1997), 1664-1668. Google Scholar

[33]

L. Tsang, G. Zhang and K. Pak, Detection of a buried object under a single random rough surface with angular correlation function in EM wave scattering, Microw. Opt. Technol. Lett., 11 (1996).   Google Scholar

[34]

K. Voccola, Synthetic aperture radar correlation imaging, SIAM J. Imaging Sci., 8 (2015), 299-330.  doi: 10.1137/14096921X.  Google Scholar

[35]

K. Voccola, M. Cheney and B. Yazici, Polarimetric synthetic-aperture inversion for extended targets in clutter, Inverse Problems, 29 (2013).  doi: 10.1088/0266-5611/29/5/054003.  Google Scholar

[36]

K. Voccola, Statistical and Analytical Techniques in Synthetic Aperture Radar Imaging, Ph. D. Thesis, Dept. Math. Sciences, Rensselaer Polytechnic Institute, Troy, NY, 2011.  Google Scholar

[37]

T. Webster, Scalar and Vector Multistatic Radar Data Models, Ph. D. Thesis, Dept. Math. Sciences, Rensselaer Polytechnic Institute, Troy, NY, 2012.  Google Scholar

[38]

J. L. Wong, A model for the radar echo from a random collection of rotating dipole scatterers, IEEE Trans. Aerosp. Electron. Syst., 3 (1967), 171-178.  doi: 10.1109/TAES.1967.5408739.  Google Scholar

[39]

B. Yazici, M. Cheney and C. E. Yarman, Synthetic-aperture inversion in the presence of noise and clutter, Inverse Problems, 22 (2006), 1705-1729.  doi: 10.1088/0266-5611/22/5/011.  Google Scholar

[40]

G. Zhang, L. Tsang and Y. Kuga, Application of angular correlation function of clutter scattering and correlation imaging in target detection, IEEE Trans. Geosci. Remote Sensing, 36 (1998), 1485-1493.   Google Scholar

[41]

G. Zhang, L. Tsang and K. Pak, Angular correlation function and scattering coefficient of electromagnetic waves scattered by a buried object under a two-dimensional rough surface, J. Opt. Soc. Am. A, 15 (1998), 2995-3002.  doi: 10.1364/JOSAA.15.002995.  Google Scholar

[42]

G. Zhang, L. Tsang and Y. Kuga, Numerical studies of the detection of targets in clutter by using angular correlation function and angular correlation imaging, Microw. Opt. Technol. Lett., 17 (1998), 82-86.  doi: 10.1002/(SICI)1098-2760(19980205)17:2<82::AID-MOP3>3.0.CO;2-E.  Google Scholar

[43]

G. Zhang, L. Tsang and Y. Kuga, Studies of the angular correlation function of scattering by random rough surfaces with and without a buried object, IEEE Trans. Geosci. Remote Sensing, 35 (1997), 444-453.  doi: 10.1109/36.563283.  Google Scholar

show all references

References:
[1]

J. G. Berryman, L. Borcea, G. Papanicolaou and C. Tsogka, Statistically stable ultrasonic imaging in random media, J. Acoust. Soc. Am., 112 (2002), 1509-1522.  doi: 10.1121/1.1502266.  Google Scholar

[2]

W. M. Boerner, M. B. El-Arini, C. Y. Chan and P. M. Mastoris, Polarization dependence in electromagnetic inverse problems, IEEE Trans. on Antennas and Propagation, 29 (1981), 262-271.  doi: 10.1109/TAP.1981.1142585.  Google Scholar

[3]

L. Borcea, G. Papanicolaou and C. Tsogka, Adaptive interferometric imaging in clutter and optimal illumination, Inverse Problems, 22 (2006), 1405-1436.  doi: 10.1088/0266-5611/22/4/016.  Google Scholar

[4]

L. Borcea, G. Papanicolaou and C. Tsogka, Interferometric array imaging in clutter, Inverse Problems, 21 (2005), 1419-1460.  doi: 10.1088/0266-5611/21/4/015.  Google Scholar

[5]

L. Borcea, M. Moscoso, G. Papanicolaou and C. Tsogka, Synthetic aperture imaging of direction and frequency dependent reflectivities, SIAM J. Imaging Sci., 9 (2016), 52-81.  doi: 10.1137/15M1036063.  Google Scholar

[6]

L. Borcea, G. Papanicolaou and C. Tsogka, Theory and applications of time reversal and interferometric imaging, Inverse Problems, 19 (2003), S139-S164.  doi: 10.1088/0266-5611/19/6/058.  Google Scholar

[7]

T.-K. Chan, Y. Kuga and A. Ishimaru, Subsurface detection of a buried object using angular correlation function measurement, Waves Random Media, 7 (1997), 457-465.  doi: 10.1080/13616679709409809.  Google Scholar

[8]

R. D. Chaney, M. C. Burl and L. M. Novak, On the Performance of Polarimetric Target Detection Algorithms, IEEE International Radar Conference, 1990. doi: 10.1109/RADAR.1990.201114.  Google Scholar

[9]

M. Cheney and B. Borden, Fundamentals of Radar Imaging, SIAM, Philadelphia, 2009.  Google Scholar

[10]

S. R. Cloude and E. Pottier, A review of target decomposition theorems in radar polarimetry, IEEE Trans. on Geoscience and Remote Sensing, 34 (1996), 498-518.  doi: 10.1109/36.485127.  Google Scholar

[11]

A. J. Devaney, The inverse problem for random sources, J. Math. Phys., 20 (1979), 1687-1691.  doi: 10.1063/1.524277.  Google Scholar

[12]

R. L. Dilsavor and R. L. Moses, Fully-Polarimetric GLRTs for Detecting Scattering Centers with Unknown Amplitude, Phase, and Tilt Angle in Terrain Clutter, in SPIE's International Symposium on Optical Engineering in Aerospace Sensing, Orlando, FL, 1994. Google Scholar

[13]

D. E. Dudgeon, R. T. Lacoss, C. H. Lazott and J. G. Verly, Use of Persistent Scatterers for Model-Based Recognition, Proc. SPIE 2230, Algorithms for Synthetic Aperture Radar Imagery, 1994. Google Scholar

[14]

A. E. El-Rouby, A. Y. Nashashibi and F. T. Ulaby, Application of frequency correlation function to radar target detection, IEEE Trans. Aero. Elec. Sys., 39 (2003), 125-139.  doi: 10.1109/TAES.2003.1188898.  Google Scholar

[15]

E. Ertin, L. C. Potter and R. L. Moses, Enhanced imaging over complete circular apertures, Signals, Systems, and Computers, (2006).  doi: 10.1109/ACSSC.2006.355025.  Google Scholar

[16]

S. Feng, C. Kane, P. A. Lee and A. D. Stone, Correlations and fluctuations of coherent wave transmission through disordered media, Phys. Rev. Lett., 61 (1988), 834-837.  doi: 10.1103/PhysRevLett.61.834.  Google Scholar

[17]

L. Ferro-Famil, A. Reigber, E. Pottier and W. M. Boerner, Scene characterization using subaperture polarimetric SAR data, IEEE Trans, on Geoscience and Remote Sensing, 41 (2003), 2264-2276.  doi: 10.1109/TGRS.2003.817188.  Google Scholar

[18]

A. Freeman and S. L. Durden, A Three-Component Scattering Model for Polarimetric SAR data, IEEE Trans. Geosci. Remote Sensing, 36 (1998), 963-973.  doi: 10.1109/36.673687.  Google Scholar

[19]

A. C. Frery, H. J. Muller, C. C. F. Yanasse and S. J. S. Sant'Anna, A model for extremely heterogeneous clutter, IEEE Tran. Geosci. Remote Sensing, 35 (1997), 648-659.  doi: 10.1109/36.581981.  Google Scholar

[20]

I. Freund, Correlation Imaging through multiply scattering media, Phys. Lett. A., 147 (1990), 502-506.  doi: 10.1016/0375-9601(90)90615-U.  Google Scholar

[21]

J. Garnier and K. Solna, Coherent interferometric imaging for synthetic aperture radar in the presence of noise, Inverse Problems, 24 (2008), 055001, 23 pp.  Google Scholar

[22]

A. Grigis and J. Sjostrand, Microlocal Analysis for Differential Operators: An Introduction, London Mathematical Society Lecture Note Series, 196. Cambridge University Press, Cambridge, 1994.  Google Scholar

[23]

M. Gustafsson, Multi-static Synthetic Aperture Radar and Inverse Scattering, Technical Report LUTEDX, TEAT-7123, 2004. Google Scholar

[24]

J. R. Huynen, Phenomenological theory of radar targets, Electromagnetic Scattering, (1978), 653-712.  doi: 10.1016/B978-0-12-709650-6.50020-1.  Google Scholar

[25]

J. Jackson and R. Moses, Clutter model for VHF SAR imagery, Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery Ⅺ, 271 (September 2, 2004). Google Scholar

[26]

P. Li, An inverse random source scattering problem in inhomogeneous media, Inverse Problems, 27(2011), 035004, 22pp.  Google Scholar

[27]

A. Mahalanobis, A. Forman, M. Bower, N. Day and R. Cherry, Multi-class SAR ATR using shift-invariant correlation filters, Patter Regonition, 27 (1994), 619-626.  doi: 10.1016/0031-3203(94)90041-8.  Google Scholar

[28]

L. M. Novak, G. J. Owirka and C. M. Netishen, Radar target identification using spatial matched filters, Pattern Regonition, 27 (1994), 607-617.  doi: 10.1016/0031-3203(94)90040-X.  Google Scholar

[29]

H. L. Royden, Real Analysis, Pearson, New York, 2010. Google Scholar

[30]

M. Soumekh, Synthetic Aperture Radar Signal Processing, John Wiley and Sons Inc., New York, 1999. Google Scholar

[31]

M. E. Taylor, Pseudodiferential Operators, Princeton University Press, Princeton, NJ, 1981.  Google Scholar

[32]

L. C. Trintinalia, R. Bhalla and H. Ling, Scattering center parametrization of wide-angle backscattered data using adaptive Gaussian representation, IEEE Trans. Ant. Prop., 45(1997), 1664-1668. Google Scholar

[33]

L. Tsang, G. Zhang and K. Pak, Detection of a buried object under a single random rough surface with angular correlation function in EM wave scattering, Microw. Opt. Technol. Lett., 11 (1996).   Google Scholar

[34]

K. Voccola, Synthetic aperture radar correlation imaging, SIAM J. Imaging Sci., 8 (2015), 299-330.  doi: 10.1137/14096921X.  Google Scholar

[35]

K. Voccola, M. Cheney and B. Yazici, Polarimetric synthetic-aperture inversion for extended targets in clutter, Inverse Problems, 29 (2013).  doi: 10.1088/0266-5611/29/5/054003.  Google Scholar

[36]

K. Voccola, Statistical and Analytical Techniques in Synthetic Aperture Radar Imaging, Ph. D. Thesis, Dept. Math. Sciences, Rensselaer Polytechnic Institute, Troy, NY, 2011.  Google Scholar

[37]

T. Webster, Scalar and Vector Multistatic Radar Data Models, Ph. D. Thesis, Dept. Math. Sciences, Rensselaer Polytechnic Institute, Troy, NY, 2012.  Google Scholar

[38]

J. L. Wong, A model for the radar echo from a random collection of rotating dipole scatterers, IEEE Trans. Aerosp. Electron. Syst., 3 (1967), 171-178.  doi: 10.1109/TAES.1967.5408739.  Google Scholar

[39]

B. Yazici, M. Cheney and C. E. Yarman, Synthetic-aperture inversion in the presence of noise and clutter, Inverse Problems, 22 (2006), 1705-1729.  doi: 10.1088/0266-5611/22/5/011.  Google Scholar

[40]

G. Zhang, L. Tsang and Y. Kuga, Application of angular correlation function of clutter scattering and correlation imaging in target detection, IEEE Trans. Geosci. Remote Sensing, 36 (1998), 1485-1493.   Google Scholar

[41]

G. Zhang, L. Tsang and K. Pak, Angular correlation function and scattering coefficient of electromagnetic waves scattered by a buried object under a two-dimensional rough surface, J. Opt. Soc. Am. A, 15 (1998), 2995-3002.  doi: 10.1364/JOSAA.15.002995.  Google Scholar

[42]

G. Zhang, L. Tsang and Y. Kuga, Numerical studies of the detection of targets in clutter by using angular correlation function and angular correlation imaging, Microw. Opt. Technol. Lett., 17 (1998), 82-86.  doi: 10.1002/(SICI)1098-2760(19980205)17:2<82::AID-MOP3>3.0.CO;2-E.  Google Scholar

[43]

G. Zhang, L. Tsang and Y. Kuga, Studies of the angular correlation function of scattering by random rough surfaces with and without a buried object, IEEE Trans. Geosci. Remote Sensing, 35 (1997), 444-453.  doi: 10.1109/36.563283.  Google Scholar

Figure 1.  Single point; standard reconstruction, correlation reconstruction with memory directions
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Single point; correlation reconstruction without memory directions with $\tilde{s} = 1, 25, 75$ slow-time steps (recall $\tilde{s} = s-s'$)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Single dipole, orientation $\pi/4$, length$ = .3\lambda$; standard reconstruction, correlation reconstruction with memory directions
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Single dipole, orientation $\pi/4$, length$ = .3\lambda$; correlation reconstruction without memory directions with $\tilde{s} = 1, 25, 75$ slow-time steps (recall $\tilde{s} = s-s'$)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Single dipole, orientation $\pi/4$, length$ = 3\lambda$; standard reconstruction, correlation reconstruction with memory directions
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Single dipole, orientation $\pi/4$, length$ = 3\lambda$; correlation reconstruction without memory directions with $\tilde{s} = 1, 25, 75$ slow-time steps (recall $\tilde{s} = s-s'$)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 8.  Single dipole, orientation $\pi/4$, length$ = 30\lambda$; standard reconstruction, correlation reconstruction with memory directions
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 9.  Single dipole, orientation $\pi/4$, length$ = 30\lambda$; correlation reconstruction without memory directions with $\tilde{s} = 1, 25, 75$ slow-time steps (recall $\tilde{s} = s-s'$)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Single dipole, orientation $\pi/4$, length$ = 3\lambda$; correlation reconstruction without memory directions with $\tilde{s} = 1, 25, 75$ slow-time steps (recall $\tilde{s} = s-s'$), second flight path
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 10.  Single dipole, orientation $\pi/4$, length$ = 30\lambda$; correlation reconstruction without memory directions with $\tilde{s} = 1, 25, 75$ slow-time steps (recall $\tilde{s} = s-s'$), second flight path
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 11.  Two point targets, dipole clutter, each dipole with orientation $\pi/4$, length$ = \lambda$, and Gaussian reflectivity; standard polarimetric reconstruction, HH, HV, VV images respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 12.  Two point targets, dipole clutter, each dipole with orientation $\pi/4$, length$ = \lambda$, and Gaussian reflectivity; correlation polarimetric reconstruction $\tilde{s} = 1$ (recall $\tilde{s} = s-s'$), HH-HH, HV-HV, VV-VV images respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 13.  Two point targets, dipole clutter, each dipole with orientation $\pi/4$, length$ = \lambda$, and Gaussian reflectivity; correlation polarimetric reconstruction $\tilde{s} = 50$ (recall $\tilde{s} = s-s'$), HH-HH, HV-HV, VV-VV images respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Single point; peak image value at center pixel
Standard BP 1.7158 - 0.2106i
Correlation BP with memory directions 1.5112 - 0.0000i
Correlation BP with $\tilde{s}=1$ 1.4772 - 0.0000i
Correlation BP with $\tilde{s}=25$ 1.1470 - 0.0000i
Correlation BP with $\tilde{s}=50$ 0.8461 + 0.0000i
Correlation BP with $\tilde{s}=75$ 0.5869 + 0.0000i
Table Options

Download as excel

Standard BP 1.7158 - 0.2106i
Correlation BP with memory directions 1.5112 - 0.0000i
Correlation BP with $\tilde{s}=1$ 1.4772 - 0.0000i
Correlation BP with $\tilde{s}=25$ 1.1470 - 0.0000i
Correlation BP with $\tilde{s}=50$ 0.8461 + 0.0000i
Correlation BP with $\tilde{s}=75$ 0.5869 + 0.0000i
Table 2.  Peak Image Value at center pixel, single dipole target, orientation $\pi/4$, length$ = .3\lambda$
Standard BP 1.1491e-04 - 6.2788e-06i
Correlation BP with memory directions 0.6668e-08 - 0.0410e-08i
Correlation BP with $\tilde{s}=1$ 0.6575e-08 - 0.0410e-08i
Correlation BP with $\tilde{s}=25$ 0.5030e-08 - 0.0377e-08i
Correlation BP with $\tilde{s}=50$ 0.3642e-08 - 0.0290e-08i
Correlation BP with $\tilde{s}=75$ 0.2490e-08 - 0.0208e-08i
Table Options

Download as excel

Standard BP 1.1491e-04 - 6.2788e-06i
Correlation BP with memory directions 0.6668e-08 - 0.0410e-08i
Correlation BP with $\tilde{s}=1$ 0.6575e-08 - 0.0410e-08i
Correlation BP with $\tilde{s}=25$ 0.5030e-08 - 0.0377e-08i
Correlation BP with $\tilde{s}=50$ 0.3642e-08 - 0.0290e-08i
Correlation BP with $\tilde{s}=75$ 0.2490e-08 - 0.0208e-08i
Table 4.  Peak Image Value at center pixel, single dipole target, orientation $\pi/4$, length$ = 3\lambda$, second flight path
Standard BP 0.0090 + 0.0004i
Correlation BP with memory directions 0.4206e-04 + 0.0014e-04i
Correlation BP with $\tilde{s}=1$ 0.4072e-04 + 0.0014e-04i
Correlation BP with $\tilde{s}=25$ 0.2535e-04 + 0.0049e-04i
Correlation BP with $\tilde{s}=50$ 0.1363e-04 - 0.0087e-04i
Correlation BP with $\tilde{s}=75$ 0.0592e-04 - 0.0021e-04i
Table Options

Download as excel

Standard BP 0.0090 + 0.0004i
Correlation BP with memory directions 0.4206e-04 + 0.0014e-04i
Correlation BP with $\tilde{s}=1$ 0.4072e-04 + 0.0014e-04i
Correlation BP with $\tilde{s}=25$ 0.2535e-04 + 0.0049e-04i
Correlation BP with $\tilde{s}=50$ 0.1363e-04 - 0.0087e-04i
Correlation BP with $\tilde{s}=75$ 0.0592e-04 - 0.0021e-04i
Table 3.  Peak Image Value at center pixel, single dipole target, orientation $\pi/4$, length$ = 3\lambda$
Standard BP 0.0012 - 0.0002i
Correlation BP with memory directions 0.7152e-06 - 0.1448e-06i
Correlation BP with $\tilde{s}=1$ 0.6983e-06 - 0.1448e-06i
Correlation BP with $\tilde{s}=25$ 0.3869e-06 - 0.1183e-06i
Correlation BP with $\tilde{s}=50$ 0.2159e-06 + 0.0199e-06i
Correlation BP with $\tilde{s}=75$ 0.1153e-06 + 0.0139e-06i
Table Options

Download as excel

Standard BP 0.0012 - 0.0002i
Correlation BP with memory directions 0.7152e-06 - 0.1448e-06i
Correlation BP with $\tilde{s}=1$ 0.6983e-06 - 0.1448e-06i
Correlation BP with $\tilde{s}=25$ 0.3869e-06 - 0.1183e-06i
Correlation BP with $\tilde{s}=50$ 0.2159e-06 + 0.0199e-06i
Correlation BP with $\tilde{s}=75$ 0.1153e-06 + 0.0139e-06i
Table 5.  Peak Image Value at center pixel, single dipole target, orientation $\pi/4$, length$ = 30\lambda$
Standard BP 0.0010 - 0.0023i
Correlation BP with memory directions 0.3106e-05 - 0.0308e-05i
Correlation BP with $\tilde{s}=1$ 0.3053e-05 - 0.0308e-05i
Correlation BP with $\tilde{s}=25$ 0.2214e-05 - 0.0087e-05i
Correlation BP with $\tilde{s}=50$ 0.1554e-05 - 0.0159e-05i
Correlation BP with $\tilde{s}=75$ 0.1115e-05 - 0.0061e-05i
Table Options

Download as excel

Standard BP 0.0010 - 0.0023i
Correlation BP with memory directions 0.3106e-05 - 0.0308e-05i
Correlation BP with $\tilde{s}=1$ 0.3053e-05 - 0.0308e-05i
Correlation BP with $\tilde{s}=25$ 0.2214e-05 - 0.0087e-05i
Correlation BP with $\tilde{s}=50$ 0.1554e-05 - 0.0159e-05i
Correlation BP with $\tilde{s}=75$ 0.1115e-05 - 0.0061e-05i
Table 6.  Peak Image Value at center pixel, single dipole target, orientation $\pi/4$, length$ = 30\lambda$, second flight path
Standard BP 1.7528 + 0.2160i
Correlation BP with memory directions 1.6668 - 0.3576i
Correlation BP with $\tilde{s}=1$ 1.5072 - 0.3576i
Correlation BP with $\tilde{s}=25$ 0.1084 - 0.1624i
Correlation BP with $\tilde{s}=50$ 0.0157 - 0.0024i
Correlation BP with $\tilde{s}=75$ 0.0045 - 0.0001i
Table Options

Download as excel

Standard BP 1.7528 + 0.2160i
Correlation BP with memory directions 1.6668 - 0.3576i
Correlation BP with $\tilde{s}=1$ 1.5072 - 0.3576i
Correlation BP with $\tilde{s}=25$ 0.1084 - 0.1624i
Correlation BP with $\tilde{s}=50$ 0.0157 - 0.0024i
Correlation BP with $\tilde{s}=75$ 0.0045 - 0.0001i
Table 7.  Input vs. ouput SCR in dB, scalar SAR, two point targets in dipole clutter
Input SCR Output SCR Standard BP Output SCR Correlation BP $\tilde{s}=1$ Output SCR Correlation $\tilde{s}=10$ Output SCR Correlation $\tilde{s}=50$
20 17.3087 51.3593 52.0820 51.9498
10 7.3087 31.3593 32.0820 31.9498
0 -2.6913 11.3593 12.0820 11.9498
-10 -12.6913 -8.6407 -7.9180 -8.0502
-20 -22.6913 -28.6407 -27.9180 -28.0502
Table Options

Download as excel

Input SCR Output SCR Standard BP Output SCR Correlation BP $\tilde{s}=1$ Output SCR Correlation $\tilde{s}=10$ Output SCR Correlation $\tilde{s}=50$
20 17.3087 51.3593 52.0820 51.9498
10 7.3087 31.3593 32.0820 31.9498
0 -2.6913 11.3593 12.0820 11.9498
-10 -12.6913 -8.6407 -7.9180 -8.0502
-20 -22.6913 -28.6407 -27.9180 -28.0502
Table 8.  Input vs. ouput SCR in dB, pol SAR standard BP, two point targets in dipole clutter
Input SCR Output SCR HH Output SCR HV Output SCR VV
20 18.0264 18.8373 18.9482
10 8.0264 8.8373 8.9482
0 -1.9736 -1.1627 -1.0518
-10 -11.9736 -11.1627 -11.0518
-20 -21.9736 -21.1627 -21.0518
Table Options

Download as excel

Input SCR Output SCR HH Output SCR HV Output SCR VV
20 18.0264 18.8373 18.9482
10 8.0264 8.8373 8.9482
0 -1.9736 -1.1627 -1.0518
-10 -11.9736 -11.1627 -11.0518
-20 -21.9736 -21.1627 -21.0518
Table 9.  Input vs. ouput SCR in dB, pol SAR correlation BP, two point targets in dipole clutter, $\tilde{s} = 1$
Input SCR Output SCR HH-HH Output SCR HV-HV Output SCR VV-VV
20 51.1293 54.0229 53.9465
10 31.1293 34.0229 33.9465
0 11.1293 14.0229 13.9465
-10 -8.8707 -5.9771 -6.0535
-20 -28.8707 -25.9771 -26.0535
Table Options

Download as excel

Input SCR Output SCR HH-HH Output SCR HV-HV Output SCR VV-VV
20 51.1293 54.0229 53.9465
10 31.1293 34.0229 33.9465
0 11.1293 14.0229 13.9465
-10 -8.8707 -5.9771 -6.0535
-20 -28.8707 -25.9771 -26.0535
Table 10.  Input vs. ouput SCR in dB, pol SAR correlation BP, two point targets in dipole clutter, $\tilde{s} = 10$
Input SCR Output SCR HH-HH Output SCR HV-HV Output SCR VV-VV
20 51.3130 54.0887 53.9973
10 31.3130 34.0887 33.9973
0 11.3130 14.0887 13.9973
-10 -8.6870 -5.9113 -6.0027
-20 -28.6870 -25.9113 -26.0027
Table Options

Download as excel

Input SCR Output SCR HH-HH Output SCR HV-HV Output SCR VV-VV
20 51.3130 54.0887 53.9973
10 31.3130 34.0887 33.9973
0 11.3130 14.0887 13.9973
-10 -8.6870 -5.9113 -6.0027
-20 -28.6870 -25.9113 -26.0027
Table 11.  Input vs. ouput SCR in dB, pol SAR correlation BP, two point targets in dipole clutter, $\tilde{s} = 50$
Input SCR Output SCR HH-HH Output SCR HV-HV Output SCR VV-VV
20 51.4206 53.9605 53.7934
10 31.4206 33.9605 33.7934
0 11.4206 13.9605 13.7934
-10 -8.5794 -6.0395 -6.2066
-20 -28.5794 -26.0395 -26.2066
Table Options

Download as excel

Input SCR Output SCR HH-HH Output SCR HV-HV Output SCR VV-VV
20 51.4206 53.9605 53.7934
10 31.4206 33.9605 33.7934
0 11.4206 13.9605 13.7934
-10 -8.5794 -6.0395 -6.2066
-20 -28.5794 -26.0395 -26.2066
[1]

Venkateswaran P. Krishnan, Eric Todd Quinto. Microlocal aspects of common offset synthetic aperture radar imaging. Inverse Problems & Imaging, 2011, 5 (3) : 659-674. doi: 10.3934/ipi.2011.5.659
[2]

Mikhail Gilman, Semyon Tsynkov. Statistical characterization of scattering delay in synthetic aperture radar imaging. Inverse Problems & Imaging, 2020, 14 (3) : 511-533. doi: 10.3934/ipi.2020024
[3]

Raluca Felea, Romina Gaburro, Allan Greenleaf, Clifford Nolan. Microlocal analysis of Doppler synthetic aperture radar. Inverse Problems & Imaging, 2019, 13 (6) : 1283-1307. doi: 10.3934/ipi.2019056
[4]

Kaitlyn Muller. The relationship between backprojection and best linear unbiased estimation in synthetic-aperture radar imaging. Inverse Problems & Imaging, 2016, 10 (2) : 549-561. doi: 10.3934/ipi.2016011
[5]

T. Varslo, C E Yarman, M. Cheney, B Yazıcı. A variational approach to waveform design for synthetic-aperture imaging. Inverse Problems & Imaging, 2007, 1 (3) : 577-592. doi: 10.3934/ipi.2007.1.577
[6]

Lassi Roininen, Markku S. Lehtinen, Petteri Piiroinen, Ilkka I. Virtanen. Perfect radar pulse compression via unimodular fourier multipliers. Inverse Problems & Imaging, 2014, 8 (3) : 831-844. doi: 10.3934/ipi.2014.8.831
[7]

Markku Lehtinen, Baylie Damtie, Petteri Piiroinen, Mikko Orispää. Perfect and almost perfect pulse compression codes for range spread radar targets. Inverse Problems & Imaging, 2009, 3 (3) : 465-486. doi: 10.3934/ipi.2009.3.465
[8]

Gang Bao, Jun Lai. Radar cross section reduction of a cavity in the ground plane: TE polarization. Discrete & Continuous Dynamical Systems - S, 2015, 8 (3) : 419-434. doi: 10.3934/dcdss.2015.8.419
[9]

Qiang Yin, Gongfa Li, Jianguo Zhu. Research on the method of step feature extraction for EOD robot based on 2D laser radar. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1415-1421. doi: 10.3934/dcdss.2015.8.1415
[10]

Jean-François Crouzet. 3D coded aperture imaging, ill-posedness and link with incomplete data radon transform. Inverse Problems & Imaging, 2011, 5 (2) : 341-353. doi: 10.3934/ipi.2011.5.341
[11]

Mathias Fink, Josselin Garnier. Ambient noise correlation-based imaging with moving sensors. Inverse Problems & Imaging, 2017, 11 (3) : 477-500. doi: 10.3934/ipi.2017022
[12]

Tom Reichert. Pandemic mitigation: Bringing it home. Mathematical Biosciences & Engineering, 2011, 8 (1) : 65-76. doi: 10.3934/mbe.2011.8.65
[13]

Peter Kuchment, Leonid Kunyansky. Synthetic focusing in ultrasound modulated tomography. Inverse Problems & Imaging, 2010, 4 (4) : 665-673. doi: 10.3934/ipi.2010.4.665
[14]

Lassi Roininen, Markku S. Lehtinen, Sari Lasanen, Mikko Orispää, Markku Markkanen. Correlation priors. Inverse Problems & Imaging, 2011, 5 (1) : 167-184. doi: 10.3934/ipi.2011.5.167
[15]

Mina Youssef, Caterina Scoglio. Mitigation of epidemics in contact networks through optimal contact adaptation. Mathematical Biosciences & Engineering, 2013, 10 (4) : 1227-1251. doi: 10.3934/mbe.2013.10.1227
[16]

Cicely K. Macnamara, Mark A. J. Chaplain. Spatio-temporal models of synthetic genetic oscillators. Mathematical Biosciences & Engineering, 2017, 14 (1) : 249-262. doi: 10.3934/mbe.2017016
[17]

Masaru Ikehata, Esa Niemi, Samuli Siltanen. Inverse obstacle scattering with limited-aperture data. Inverse Problems & Imaging, 2012, 6 (1) : 77-94. doi: 10.3934/ipi.2012.6.77
[18]

Ming Su, Arne Winterhof. Hamming correlation of higher order. Advances in Mathematics of Communications, 2018, 12 (3) : 505-513. doi: 10.3934/amc.2018029
[19]

Vladimír Špitalský. Local correlation entropy. Discrete & Continuous Dynamical Systems - A, 2018, 38 (11) : 5711-5733. doi: 10.3934/dcds.2018249
[20]

Daniela Calvetti, Erkki Somersalo. Microlocal sequential regularization in imaging. Inverse Problems & Imaging, 2007, 1 (1) : 1-11. doi: 10.3934/ipi.2007.1.1

2018 Impact Factor: 1.469

Tools

Metrics

  • PDF downloads (128)
  • HTML views (247)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences