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SCIENCE CHINA Information Sciences, Volume 64 , Issue 11 : 212303(2021) https://doi.org/10.1007/s11432-019-2797-4

Extended scintillation phase gradient autofocus in future spaceborne P-band SAR mission

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  • ReceivedOct 8, 2019
  • AcceptedFeb 14, 2020
  • PublishedOct 26, 2021

Abstract


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 41271459).


References

[1] Rino C L. The Theory of Scintillation With Applications in Remote Sensing. New York: Wiley, 2011. Google Scholar

[2] Meyer F, Bamler R, Jakowski N. The Potential of Low-Frequency SAR Systems for Mapping Ionospheric TEC Distributions. IEEE Geosci Remote Sens Lett, 2006, 3560-564 CrossRef ADS Google Scholar

[3] Xu Z W, Wu J, Wu Z S. A survey of ionospheric effects on space-based radar. Waves Random Media, 2004, 14S189-S273 CrossRef ADS Google Scholar

[4] Belcher D P. Theoretical limits on SAR imposed by the ionosphere. IET Radar Sonar Navigation, 2008, 2435-448 CrossRef Google Scholar

[5] quegan S, Lamont J. Ionospheric and tropospheric effects on synthetic aperture radar performance. Int J Remote Sens, 1986, 7525-539 CrossRef ADS Google Scholar

[6] Rino C L. A power law phase screen model for ionospheric scintillation: 1. Weak scatter. Radio Sci, 1979, 141135-1145 CrossRef ADS Google Scholar

[7] Rino C L. A power law phase screen model for ionospheric scintillation: 2. Strong scatter. Radio Sci, 1979, 141147-1155 CrossRef ADS Google Scholar

[8] Kung Chie Yeh , Chao-Han Liu . Radio wave scintillations in the ionosphere. Proc IEEE, 1982, 70324-360 CrossRef Google Scholar

[9] Arcioni M, Bensi P, Davidson M W J, et al. ESA'S BIOMASS mission candidate system and payload overview. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012. 5530--5533. Google Scholar

[10] Ho Tong Minh D, Tebaldini S, Rocca F. Capabilities of BIOMASS Tomography for Investigating Tropical Forests. IEEE Trans Geosci Remote Sens, 2015, 53965-975 CrossRef ADS Google Scholar

[11] Wang C, Chen L, Liu L. A New Analytical Model to Study the Ionospheric Effects on VHF/UHF Wideband SAR Imaging. IEEE Trans Geosci Remote Sens, 2017, 554545-4557 CrossRef ADS Google Scholar

[12] Ishimaru A, Kuga Y, Liu J. Ionospheric effects on synthetic aperture radar at 100 MHz to 2 GHz. Radio Sci, 1999, 34257-268 CrossRef ADS Google Scholar

[13] Zheng-Wen Xu , Jian Wu , Zhen-Sen Wu . Potential Effects of the Ionosphere on Space-Based SAR Imaging. IEEE Trans Antennas Propagat, 2008, 561968-1975 CrossRef ADS Google Scholar

[14] Wang C, Zhang M, Xu Z W. Effects of Anisotropic Ionospheric Irregularities on Space-Borne SAR Imaging. IEEE Trans Antennas Propagat, 2014, 624664-4673 CrossRef ADS Google Scholar

[15] Li Y H, Hu C, Dong X C. Impacts of ionospheric scintillation on geosynchronous SAR focusing: preliminary experiments and analysis. Sci China Inf Sci, 2015, 581-3 CrossRef Google Scholar

[16] Hu C, Li Y, Dong X. Performance Analysis of L-Band Geosynchronous SAR Imaging in the Presence of Ionospheric Scintillation. IEEE Trans Geosci Remote Sens, 2017, 55159-172 CrossRef ADS Google Scholar

[17] Ji Y, Zhang Q, Zhang Y. L-band geosynchronous SAR imaging degradations imposed by ionospheric irregularities. Sci China Inf Sci, 2017, 60060308 CrossRef Google Scholar

[18] Meyer F J, Chotoo K, Chotoo S D. The Influence of Equatorial Scintillation on L-Band SAR Image Quality and Phase. IEEE Trans Geosci Remote Sens, 2016, 54869-880 CrossRef ADS Google Scholar

[19] Ji Y, Zhang Y, Zhang Q. Comments on “The Influence of Equatorial Scintillation on L-Band SAR Image Quality and Phase”. IEEE Trans Geosci Remote Sens, 2019, 577300-7301 CrossRef ADS Google Scholar

[20] Ji Y, Zhang Y, Dong Z. Impacts of Ionospheric Irregularities on L-Band Geosynchronous Synthetic Aperture Radar. IEEE Trans Geosci Remote Sens, 2020, 583941-3954 CrossRef ADS Google Scholar

[21] Ji Y, Dong Z, Zhang Y. Geosynchronous SAR raw data simulator in presence of ionospheric scintillation using reverse backprojection. 58512-514 CrossRef ADS Google Scholar

[22] Jakowski N, Mayer C, Hoque M M. Total electron content models and their use in ionosphere monitoring. Radio Sci, 2011, 46RS0D18 CrossRef ADS Google Scholar

[23] Jehle M, Frey O, Small D. Measurement of Ionospheric TEC in Spaceborne SAR Data. IEEE Trans Geosci Remote Sens, 2010, 482460-2468 CrossRef ADS Google Scholar

[24] Meyer F J, Nicoll J B. Prediction, Detection, and Correction of Faraday Rotation in Full-Polarimetric L-Band SAR Data. IEEE Trans Geosci Remote Sens, 2008, 463076-3086 CrossRef ADS Google Scholar

[25] Ji Y, Zhang Y, Zhang Q. Retrieval of Ionospheric Faraday Rotation Angle in Low-Frequency Polarimetric SAR Data. IEEE Access, 2019, 73181-3193 CrossRef Google Scholar

[26] Gomba G, Parizzi A, De Zan F. Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split-Spectrum Method. IEEE Trans Geosci Remote Sens, 2016, 541446-1461 CrossRef ADS Google Scholar

[27] Rogers N C, Quegan S, Jun Su Kim S. Impacts of Ionospheric Scintillation on the BIOMASS P-Band Satellite SAR. IEEE Trans Geosci Remote Sens, 2014, 521856-1868 CrossRef ADS Google Scholar

[28] Jun Su Kim , Papathanassiou K P, Scheiber R. Correcting Distortion of Polarimetric SAR Data Induced by Ionospheric Scintillation. IEEE Trans Geosci Remote Sens, 2015, 536319-6335 CrossRef ADS Google Scholar

[29] Wang R, Hu C, Li Y. Joint Amplitude-Phase Compensation for Ionospheric Scintillation in GEO SAR Imaging. IEEE Trans Geosci Remote Sens, 2017, 553454-3465 CrossRef ADS Google Scholar

[30] Yu L, Zhang Y, Zhang Q. Minimum-Entropy Autofocusing Based on Re-PSO for Ionospheric Scintillation Mitigation in P-Band SAR Imaging. IEEE Access, 2019, 784580-84590 CrossRef Google Scholar

[31] Quegan S, Green J J, Chen J. Simulation of ionospheric disturbances and impact assessment on biomass product quality. 2009, ESA/ESTEC, Noordwijk, The Netherlands, Contract 21760/08/NL/CT. Google Scholar

[32] Quegan S, Green J, Schneider R Z. Quantifying and correcting ionospheric effects on P-band SAR images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2008. 541--544. Google Scholar

[33] Zhuo Li , Shaun Quegan , Jie Chen . Performance Analysis of Phase Gradient Autofocus for Compensating Ionospheric Phase Scintillation in BIOMASS P-Band SAR Data. IEEE Geosci Remote Sens Lett, 2015, 121367-1371 CrossRef ADS Google Scholar

[34] Knepp D L. Multiple phase-screen calculation of the temporal behavior of stochastic waves. Proc IEEE, 1983, 71722-737 CrossRef Google Scholar

[35] Knepp D L. Multiple phase screen calculation of two-way spherical wave propagation in the ionosphere. Radio Sci, 2016, 51259-270 CrossRef ADS Google Scholar

[36] Carrano C S, Groves K M, Caton R G. Simulating the impacts of ionospheric scintillation on L band SAR image formation. Radio Sci, 2012, 47RS0L20 CrossRef ADS Google Scholar

[37] Cumming I G, Wong F H. Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation. Boston: Artech House, 2005. Google Scholar

[38] Belcher D P, Mannix C R, Cannon P S. Measurement of the Ionospheric Scintillation Parameter $C_{k}L$ From SAR Images of Clutter. IEEE Trans Geosci Remote Sens, 2017, 555937-5943 CrossRef ADS Google Scholar

[39] Mannix C R, Belcher D P, Cannon P S. Measurement of Ionospheric Scintillation Parameters From SAR Images Using Corner Reflectors. IEEE Trans Geosci Remote Sens, 2017, 556695-6702 CrossRef ADS Google Scholar

[40] Cannon P S, Belcher D P. Amplitude scintillation effects on SAR. IET Radar Sonar Navigation, 2014, 8658-666 CrossRef Google Scholar

[41] Wahl D E, Eichel P H, Ghiglia D C. Phase gradient autofocus: A robust tool for high resolution SAR phase correction. IEEE Trans Aerosp Electron Syst, 1994, 30827-835 CrossRef ADS Google Scholar

[42] Hian Lim Chan , Tat Soon Yeo . Noniterative quality phase-gradient autofocus (QPGA) algorithm for spotlight SAR imagery. IEEE Trans Geosci Remote Sens, 1998, 361531-1539 CrossRef ADS Google Scholar

[43] Wei Ye , Tat Soon Yeo , Zheng Bao . Weighted least-squares estimation of phase errors for SAR/ISAR autofocus. IEEE Trans Geosci Remote Sens, 1999, 372487-2494 CrossRef ADS Google Scholar

[44] de Macedo K A C, Scheiber R, Moreira A. An Autofocus Approach for Residual Motion Errors With Application to Airborne Repeat-Pass SAR Interferometry. IEEE Trans Geosci Remote Sens, 2008, 463151-3162 CrossRef ADS Google Scholar

[45] Zhang L, Qiao Z, Xing M. A Robust Motion Compensation Approach for UAV SAR Imagery. IEEE Trans Geosci Remote Sens, 2012, 503202-3218 CrossRef ADS Google Scholar

[46] Zhang L, Sheng J, Xing M. Wavenumber-Domain Autofocusing for Highly Squinted UAV SAR Imagery. IEEE Sens J, 2012, 121574-1588 CrossRef ADS Google Scholar

[47] Li J, Chen J, Wang P. A Coarse-to-Fine Autofocus Approach for Very High-Resolution Airborne Stripmap SAR Imagery. IEEE Trans Geosci Remote Sens, 2018, 563814-3829 CrossRef ADS Google Scholar

[48] Shao S, Zhang L, Liu H. Spatial-variant contrast maximization autofocus algorithm for ISAR imaging of maneuvering targets. Sci China Inf Sci, 2019, 62040303 CrossRef Google Scholar

  • Figure 1

    (Color online) A point-scatterer simulation of the scintillation impacts on spaceborne P-band SAR imaging. (a) The phase fluctuation of the two-way ITF. (b) The amplitude fluctuation of the two-way ITF. (c) The scintillation-affected SAR echo. protect łinebreak (d) The scintillation-affected azimuth IRF compared with the ideal azimuth IRF. (f) The azimuth IRF only affected by the amplitude scintillation. (e) The azimuth IRF only affected by the phase scintillation.

  • Figure 2

    (Color online) Geometry of the spaceborne P-band SAR acquisition in existence of the ionospheric scintillation.

  • Figure 3

    (Color online) The spatial-variant SPE. (a) The wrapped phase fluctuation (deg) of the two-way ITF for the whole protect łinebreak 10 km $\!\!\times\!\!$ 10 km scene. (b) The unwrapped SPEs of the eleven targets in the center range bin with a neighbouring azimuth interval of 1 km. (c) The unwrapped SPEs of eleven targets in the center azimuth bin with a neighbouring range interval of 1 km.

  • Figure 4

    (Color online) The SPE correlation versus the spatial separation of the target at the ground. (a) The 2D distribution and (b) the profiles along- and across-track.

  • Figure 5

    (Color online) An example of the PGA application to the SPE compensation. (a) The SPE estimation without the constant and linear parts. (b) The corrected azimuth IRF compared with the ideal azimuth IRF. After the compensation, the Ares is 5.06 m, the PSLR is $-$12.14 dB, the ISLR is $-$7.10 dB, and the PGL is $-$0.25 dB

  • Figure 6

    (Color online) The azimuth-imaging performance before (black lines) and after (red lines) the scintillation compensation by the classical PGA. Statistical indicators are generated by the Monte-Carlo simulations with 200 repetitions, which are depicted by mean values (solid circles) and standard deviations (fluctuating range bars). (a) Ares, (b) PSLR, (c) ISLR, (d) PGL.

  • Figure 7

    (Color online) The flow chart of ESPGA.

  • Figure 8

    (Color online) The BPGA diagram of the whole (left) and block-masked (right) images exhibited with the selected range bins.

  • Figure 9

    (Color online) The diagram of the overall estimation.

  • Figure 10

    (Color online) Exhibitions of the simulated spaceborne P-band SAR image derived from an airborne P-band SAR real scene. The azimuth direction is denoted by the red arrow. The simulated scene size is 20 km $\times$ 20 km with the samplings of 4000 $\times$ 4000 in azimuth and ground range. (a) The ideal SAR image in absence of the scintillation effects and (b) the scintillation-affected SAR image.

  • Figure 11

    (Color online) The selection of strong scatterers (red circles) by using a uniform threshold for the whole image (left) and by using the proposed BPGA strategy (right).

  • Figure 12

    (Color online) (a) The splicing for two SPE estimates and (b) the correlated function reaching the maximum at the staggered distance.

  • Figure 13

    (Color online) Integral SPE lines after BPGA and azimuth splicing. (a) The first to fifth, (b) the sixth to tenth, protect łinebreak (c) the eleventh to fifteenth and (d) the sixteenth to twentieth SPE lines, versus the blue, green, red, cyan and purple, respectively.

  • Figure 14

    (Color online) Integral SPE distribution covering the holistic scene. (a) The wrapped SPE injected into the ideal image exhibited with the eliminated constant and linear components of every SPE along-track line; (b) the SPE estimation by ESPGA; (c) the differential of the simulated and estimated SPE.

  • Figure 15

    (Color online) Probability density diagram for simulated and residual SPEs.

  • Figure 16

    (Color online) The spaceborne P-band SAR image after the SPE compensation by using the proposed ESPGA.

  • Figure 17

    (Color online) The correlation coefficient before (left) and after the mitigation (right).

  •   

    Algorithm 1 BPGA estimation

    Divide the SAR image into $M\!\times\!~N$ blocks;

    for each estimation block

    Get a block-masked image with the original image size of $N_a~\!\times~\!N_r$;

    Find the amplitude maximums $A$ and record their azimuth indexes $\boldsymbol~J$ for each range bin;

    Choose those range bins with strong maximums thatsatisfy the amplitude threshold in (18);

    Classify the above sifted maximums by clustering their azimuth indexes. If two maximum indexes are satisfied with the interval threshold, they are a group. Count the number of each group;

    Choose range bins of a group with the largest number to implement the classical PGA, and save the result of the SPE estimation for each block and average azimuth index of this group;

    end for

  • Table 1  

    Table 1Parameters of a spaceborne P-band SAR system

    $~{}~{}~{}~{}~{}$Parameter$~{}~{}~{}~{}~{}$ $~{}~{}~{}$Specification $~{}~{}~{}$$~{}~{}~{}$Unit$~{}~{}~{}$
    Radar altitude 700 km
    Carrier frequency 500 MHz
    Incident angle 30 $^{\circ}$
    Squint angle 90 $^{\circ}$
    System bandwidth 56 MHz
    Doppler bandwidth 1223 Hz
  •   

    Algorithm 2 Overall estimation

    for each block range bin

    for each BPGA result

    Get or renew the two neighboring SPE estimates waiting for splicing;

    Estimate the staggered distance of the two SPEs based on a maximization process in (19);

    Eliminate the linear component discrepancy of the overlapped parts of the two SPEs;

    Average the overlapped two segments to obtain a public one, and generate an integral SPE line by an end-to-end splicing according to (20);

    Eliminate the linear component of the SPE line;

    end for

    end for

    Interpolate in range for an integral 2D SPE distribution.

  • Table 2  

    Table 2Scintillation parameters

    $~{}~{}~{}~{}~{}$Parameter$~{}~{}~{}~{}~{}$ $~{}~{}~{}$Specification$~{}~{}~{}$ Unit
    PS altitude $z_0$ 350 km
    Outer scale $L_o$ 10 km
    Spectrum index $p$ 3
    Turbulence strength $C_kL$ $10^{33}$ electrons$^2/({\rm~m}^2{\rm~rad}^3)$
    Anisotropic scale ratio $a\!:b$ 1
    Scintillation index $S_4$ 0.155
    Scintillation phase variance $~{\left\langle~{\Delta~{\phi~^2}}~\right\rangle~}$ 1.017 rad$^2$
  •   

    Algorithm 3 SPE compensation

    Generate a zero matrix $\boldsymbol~I$ with the image size;

    for each range line

    Generate a subimage masked by each range line;

    Operate the azimuth FFT;

    Search the index scope corresponding to this range line in the SPE distribution;

    Multiply the subimage by the conjugated exponential indexed SPE the in range-Doppler domain;

    Operate the azimuth IFFT;

    Accumulate the compensated subimage into $\boldsymbol~I$;

    end for

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