Inverse Synthetic Aperture Radar (ISAR) target images are extracted using compressed sensing methods. The extracted images are edited and merged into measured Synthetic Aperture Radar (SAR) images. A noise free image of the target is extracted from the Radar Cross Section (RCS) measurement by using the Basis Pursuit Denoise (BPDN) method and then solving for a model consisting of point scatterers. The target signature point scatterers are then merged into a point scatterer representation of the SAR background scene. This method means that SAR images acquired in expensive airborne field trials can be used efficiently to evaluate different targets and camouflage measured separately in a ground based setup. The method is demonstrated with turntable measurements of a full scale target, with and without camouflage, signature extraction and blending into a SAR background. We find that the method provides an efficient way of evaluating measured target signatures in SAR backgrounds

Jersblad, Johan

Larsson, Christer

English

Lund University Publications

LUND UNIVERSITYPO Box 117221 00 Lund+46 46-222 00 00SAR-ISAR Blending Using Compressed Sensing MethodsLarsson, Christer; Jersblad, JohanPublished in:AMTA Proceedings2015Link to publicationCitation for published version (APA):Larsson, C., & Jersblad, J. (2015). SAR-ISAR Blending Using Compressed Sensing Methods. In AMTAProceedings (pp. 1-6). Antenna Measurement Techniques Association.Total number of authors:2General rightsUnless other specific re-use rights are stated the following general rights apply:Copyright and moral rights for the publications made accessible in the public portal are retained by the authorsand/or other copyright owners and it is a condition of accessing publications that users recognise and abide by thelegal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private studyor research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portalRead more about Creative commons licenses: https://creativecommons.org/licenses/Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will removeaccess to the work immediately and investigate your claim.SAR-ISAR Blending UsingCompressed Sensing MethodsChrister LarssonSaab DynamicsSE-581 88 Linköping, SwedenLund UniversityP.O. Box 118, SE-221 00 Lund, SwedenJohan JersbladSaab BarracudaSE-594 32 Gamleby, SwedenAbstract—Inverse Synthetic Aperture Radar (ISAR) targetimages are extracted using compressed sensing methods. Theextracted images are edited and merged into measured SyntheticAperture Radar (SAR) images. A noise free image of the targetis extracted from the Radar Cross Section (RCS) measurementby using the Basis Pursuit Denoise (BPDN) method and thensolving for a model consisting of point scatterers. The targetsignature point scatterers are then merged into a point scattererrepresentation of the SAR background scene. This method meansthat SAR images acquired in expensive airborne field trials canbe used efficiently to evaluate different targets and camouflagemeasured separately in a ground based setup. The method isdemonstrated with turntable measurements of a full scale target,with and without camouflage, signature extraction and blendinginto a SAR background. We find that the method provides anefficient way of evaluating measured target signatures in SARbackgrounds.I. INTRODUCTIONDifferent methods can be used in order to protect vehiclesand structures from being detected by air or satellite basedSynthetic Aperture Radar (SAR) systems. One method is touse camouflage e.g., in the forms of nets or mobile camouflagesystems. The efficiency of these camouflage systems then haveto be evaluated with the target placed in different backgrounds.The evaluation method that first comes to mind is to useairborne SAR field trials. Ideally, each target should then bemeasured for many orientations as well as illumination angleswhich would result in a large number of measurement cases.Data from one such comprehensive measurement campaign,Moving and Stationary Target Acquisition and Recognition(MSTAR), that was collected to provide data for research inautomatic target recognition is available for download [1].Figure 1 is an example of a SAR image of a T-72 tank takenfrom the MSTAR data set. Typical features for SAR imagesare visible in this image, such as the target itself, groundclutter and the target shadow. Note also that contributionsfrom system noise are visible in the otherwise dark blue targetshadow.The main disadvantages with using airborne SAR-systemsare that such measurements are complicated and expensive.Furthermore, in the application that we are addressing here,Fig. 1. X-band SAR front aspect image of a T-72 tank from the MSTARdata set [1].one would like to compare the effects of different camouflagesystems on the same target for many different angles. Using anairborne SAR-system for all combinations of configurations,camouflage systems and angles would be very time consum-ing. Reproducibility is another aspect that has to be taken intoaccount. It is hard to measure repeatedly in a consistent wayat the same angles.A more cost effective solution is to use ground basedISAR measurements of the desired targets with or withoutcamouflage systems and then blend these ISAR images intothe SAR scene. This means that the SAR-data can be reusedfor many different target measurements. The merging of thedata should then be performed as a part of the imaging processto get realistic composite images that are free from editingartifacts. An example of a method where that is done isdescribed in [2] where the merging of the ISAR and SARdata is done as a part of doing the down range processingstep.Compressed sensing methods are used in this study. Thecompressed sensing field, sometimes called compressive sens-ing, was pioneered about 10 years ago by [3], [4]. It isbasically a method to solve an underdetermined system ofequations iteratively by assuming a sparse solution. Thereare many potential application areas for compressed sensingincluding radar imaging [5].This study presents a method where the inverse scatteringproblem is solved for a model consisting of isotropic pointscatters using a compressed sensing method. The point scattersare then blended with an edited point scatterer representationof the background.II. THEORYThis study presents an approach where the RCS turntablemeasurement data from the target is used to solve an inverseproblem consisting of isotropic point scatterers in the imagedomain. The system of equations describing the model can bewrittenAz = b, (1)where A is the forward operator, z = z(x, y) contains theisotropic point scatterers, and b = b(f, ϕ) is the measuredRCS. This is shown graphically in (2).xyzf'bAImage RCS dataFig. 2. The sparse matrix containing the isotropic point scatters, z, theforward operator A and the resulting RCS stored in matrix b.The model as defined by the forward operator A is con-structed so that the point scatters are fixed to the turntable,isotropic and have constant amplitude for all frequencies. Thesolution to the system of equations is naturally sparse, i.e., fewscatterers are required to describe the measured RCS meaningthat most elements in z(x, y) are zero. The compressedsensing method Basis Pursuit Denoise (BPDN) implementedin SPGL1 is used to extract the point scatterer locations andamplitude [6], [7]. The BPDN problem is formulated asMinimize ||z||1 subject to ||Az − b||2 ≤ σ, (2)where the indices 1 and 2 denote the `1 and `2 norms,respectively. σ is an estimate of the noise. Using BPDNdescribed by (2) will promote sparsity in the solution forz [6], [7]. The point scatterers represented by the matrix zthen results in noise free RCS if operated on by the forwardoperator A. This part of the method is similar to previouswork in [8] and [9]. A near field formulation for A is used inour implementation.Equation (1) can also be solved directly using back projec-tion which is a standard method for ISAR imaging [10], [11].z(x, y) is then given byz(x, y) =M∑m=1N∑n=1Kmb(fn,m)e(−i2kn(rm(x,y)−rm0))fn (3)for M antenna positions and N frequencies. rm(x, y) is thedistance from the antenna to (x, y) and rm0 is the distancefrom the antenna to the origin. Km is a normalization constantgiven byKm =(1MN)(rm(x, y)rm0fc)2(4)where fc is the center frequency.Both BPDN and back projection are used in this study.BPDN is used to generate the point scatterer representationof the target and back projection is used to generate the SARand ISAR images.III. METHODThe RCS measurement data from the target is used to solvean inverse problem consisting of isotropic point scatterersas described previously. This part of the method is similarto previous work in [8] and [9]. The SAR background isthen generated synthetically from point scatterers extractedfrom a background SAR measurement in this study. Thebackground is edited to approximate the effect of a shadow andattenuation through camouflage. The target and backgroundpoint scatterers are merged and the forward operator A toobtain RCS data that is used to make a blended SAR imagethrough back projection.The method can be summarized in the following steps:1) Perform a target measurement to aquire the complexRCS data, b(fn,m).2) Use the BPDN method to determine the point scattererrepresentation z(x, y) for the target.3) Edit z(x, y) to only contain the target scatterers.4) Generate a point scatterer background.5) Overlay the target shadow on the background pointscatterers. Remove the point scatters that are in theshadow.6) If there is camouflage then overlay the camouflageshadow on the background point scatterers. Attenuatethe point scatters that are in the shadow.7) Combine the edited point scatterer background with thetarget point scatters. Use the forward operator, A, togenerate the corresponding RCS.8) Add noise to the RCS to simulate the performance ofthe SAR system.9) Use the RCS to generate a blended SAR-ISAR imagewith back projection.The effects of multiple scattering from target-ground interac-tions are not taken into account in the method presented here.IV. RESULTS AND DISCUSSIONMeasured X-band data from a full scale target are used todemonstrate the method. Measured data from a STANDCAM(Standard Decoy for Camouflage Materials) is used for thispurpose, see Fig. 3. This non-classified target is developed byWTD 52, Oberjettenberg, Germany and is made from metal-lized glass-fiber reinforced plastic. The right side is wheeledand the left side is tracked in order to simplify comparisonsbetween wheeled and tracked alternatives of a vehicle.The generic camouflage net is based on a warp-knittedpolyester coated with an electrically conductive coating. Thesurface resistance of the material is typically 200 Ω/. Theinteraction between the material and the radar wave is a combi-nation of transmission, reflection and absorption. The internalstructure, i.e., distance between the yarns and roughness, ofthe coated fabric is much smaller than the wavelength of theincoming waves. More information about the camouflage andanalysis of its effectiveness can be found in [12].abFig. 3. The STANDCAM test target, without (a) and with camouflage (b).The measurements were performed at a center frequency of10 GHz. Data with a bandwidth of 0.5 GHz with VV polariza-tion and 2.8◦ angular width was used for the processing for theimages presented in this paper. This gives a resolution of about0.3 m in both down and cross range. A Hanning window wasused for all data processed with back projection. The distancebetween the measurement radar and the target was r0 = 163 m.The measurements were performed at the Swedish DefenceResearch Agency (FOI) outdoor measurement range.Fig. 4 shows an ISAR image of the target without cam-ouflage and Fig. 5 shows an ISAR image of the target withcamouflage. Both images are processed using back projection.The images show that the camouflage attenuates the signature.Fig. 4. Back projection ISAR image of the target without camouflage. Thefilled circle sector shows the part of the angle interval that is used for theimage.Fig. 5. Back projection ISAR image of the target with camouflage. The filledcircle sector shows the part of the angle interval that is used for the image.The BPDN method is then used to solve the inverse problemdefined by (1). This results in a sparse solution with scatterersand their amplitudes. This is shown in the left frames ofFigs. 6 and 7 for the uncamouflaged and camouflaged targets,respectively. The right frames show the corresponding backprojection ISAR images processed from the RCS generated bythe forward operator, A, operating on the matrix containingthe point scatterer amplitudes.Fig. 6. Isotropic point reflector representation (left) and the correspondingback projection ISAR image (right) for the target.Fig. 7. Isotropic point reflector representation (left) and the correspondingback projection ISAR image (right) for the target with camouflage.A synthetic point scatterer representation of the backgroundis made as described previously. The clutter shown in thefigures corresponds to a mean backscattering cross sectionper unit area or backscattering coefficient, σ0, of -20 dB. Thiscorresponds to a very low clutter level. [13] The noise levelper unit are is set to -32 dB.A target shadow is generated using a 3D CAD model ofthe target. Here an elevation angle of 10◦ is assumed forthe SAR system. An outline overlay of the shadow on thepoint scatterers is shown in the left frame of Fig 8. The pointscatterers inside the shadow region have been removed tosimulate the effect of the shadow. The right frame shows thecorresponding back projection ISAR images processed fromthe RCS generated by the forward operator, A, operating onthe matrix containing the point scatterer amplitudes.Fig. 8. Isotropic point reflector representation (left) and the correspondingback projection ISAR image (right) for the shadow of the target in the clutterbackground.The shadow from the camouflage is generated in the sameway also using a CAD model. An outline overlay of theshadow on the point scatterers is shown in the left frameof Fig 9. The background scatterers in the target shadoware removed as before while the scatterers in the shadow ofthe camouflage are attenuated corresponding to the nominalattenuation of the camouflage net. The corresponding backprojection ISAR image shown in the right frame is thenprocessed in the same way as described previously.The point scatterers from the target measurement and theedited background are then merged together into new z matri-ces for both the target without camouflage and for the targetwith camouflage. Back projection images are then processedas described previously to generate RCS data. The results areshown in the left frames of Figs. 10 and 11 for the targetwithout and with camouflage, respectively.This procedure where ISAR images are blended into a SARbackground results in seamless SAR images that can be usedto evaluate e.g., the efficiency of different camouflage withdifferent detection algorithms.Fig. 9. Isotropic point reflector representation (left) and the correspondingback projection ISAR image (right) for the shadow of the camouflaged targetin the clutter background.Noise is also added to the RCS to simulate the performanceof a real SAR system. The right frames of Figs. 10 and 11show the back projection images with added noise.Fig. 10. Back projection ISAR image of the target in a clutter background,without (left) and with (right) noise.V. SUMMARY AND CONCLUSIONSWe have shown how the compressed sensing method BPDNcan be used to solve the inverse scattering problem for a modelfor a target with or without camouflage consisting of isotropicpoint scatterers. The target point scatterers are then mergedFig. 11. Back projection ISAR image of the camouflaged target in a clutterbackground, without (left) and with (right) noise.into a SAR background that has been edited to show shadowsfrom the target and the camouflage system.The paper describes a method so that SAR images acquiredin expensive airborne field trials can be used efficiently toevaluate different targets and camouflage measured separatelyin a ground based setup. The method is demonstrated withturntable measurements of a full scale target, with and withoutcamouflage, signature extraction and blending into a SARbackground. We find that the method provides an efficient wayof evaluating measured target signatures in SAR backgrounds.ACKNOWLEDGEMENTThe financial support by the Swedish Defence MaterielAdministration is gratefully acknowledged.REFERENCES[1] T. D. Ross, S. W. Worrell, V. J. Velten, J. C. Mossing, and M. L.Bryant, “Standard SAR ATR evaluation experiments using the MSTARpublic release data set,” in Aerospace/Defense Sensing and Controls.International Society for Optics and Photonics, 1998, pp. 566–573.[2] S. Bokaderov, M. Laubach, H. Schimpf, and P. Wellig, “EOSAR - Atool for the blending of real objects into SAR scenes,” in 2011 GermanMicrowave Conference (GeMIC). IEEE, 2011, pp. 1–4.[3] E. J. Candès, J. K. Romberg, and T. Tao, “Stable signal recovery fromincomplete and inaccurate measurements,” Communications on pure andapplied mathematics, vol. 59, no. 8, pp. 1207–1223, 2006.[4] D. L. Donoho, “Compressed sensing,” IEEE Transactions on Informa-tion Theory, vol. 52, no. 4, pp. 1289–1306, 2006.[5] L. C. Potter, E. Ertin, J. T. Parker, and M. Cetin, “Sparsity andcompressed sensing in radar imaging,” Proc. IEEE, vol. 98, no. 6, pp.1006–1020, 2010.[6] E. van den Berg and M. P. Friedlander, “SPGL1: A solver for large-scalesparse reconstruction,” June 2007, http://www.cs.ubc.ca/labs/scl/spgl1.[7] ——, “Probing the pareto frontier for basis pursuit solutions,” SIAMJournal on Scientific Computing, vol. 31, no. 2, pp. 890–912, 2008.[8] V. M. Patel, G. R. Easley, D. M. Healy Jr, and R. Chellappa, “Com-pressed sensing for synthetic aperture radar imaging,” in 2009 16th IEEEInternational Conference on Image Processing (ICIP). IEEE, 2009, pp.2141–2144.[9] B. Fischer, I. LaHaie, and M. Hawks, “On the use of basis pursuit and aforward operator dictionary to separate specific background types fromtarget RCS data,” in Proc. Antenna Measurement Techniques Association(AMTA), Tucson, AZ, USA, 2014, pp. 85–90.[10] T. Vaupel and T. F. Eibert, “Comparison and application of near-fieldISAR imaging techniques for far-field radar cross section determination,”IEEE Trans. Antennas Propagat., vol. 54, no. 1, pp. 144–151, Jan. 2006.[11] C. Larsson, “Nearfield RCS measurements of full scale targets usingISAR,” in Proc. Antenna Measurement Techniques Association (AMTA),Tucson, AZ, USA, 2014, pp. 1–6.[12] J. Jersblad and C. Larsson, “Camouflage effectiveness of static nets inSAR images,” in Target and Background Signatures, ser. Proc. SPIE,K. U. Stein and R. H. M. A. Schleijpen, Eds., vol. 9653, In press, 2015.[13] F. T. Ulaby and M. C. Dobson, Handbook of Radar Scattering Statisticsfor Terrain. Norwood, MA, USA: Artech House, 1989.

SAR-ISAR Blending Using Compressed Sensing Methods

https://lup.lub.lu.se/search/files/5509289/8160871.pdf

SAR-ISAR Blending Using Compressed Sensing Methods

Abstract

Similar works

Full text

Available Versions

Lund University Publications