F2-B: Millimeter-Wave Standoff Detection of Concealed ExplosivesAbstract— The goal of this project is the development and evaluation of inexpensive, high resolution radar that can distinguish foreign objects hidden on individuals under clothing at a distance, while keeping the radar to a size that can still fit in or on a van. In collaboration with our partners at the Fraunhofer Institute in Germany, we have designed and tested a 2D system capable to detect metallic threats at standoff distances. In order to be able to detect dielectric structures, such as TNT, a 3D sys-tem must be used. A new 3D system based on an sparse array of transmitting/receiving antennas and a passive array of scatters have been studied by our group recently, showing that a resolution of about 7.5 millimeters at 40 meters range can be achieved with this novel configuration at 60 GHz.

I. PARTICIPANTS

Faculty/StaffName Title Institution Email Phone

Carey M. Rappaport Professor NU rappapor@ece.neu.edu 617.373.2043Jose A.Martinez-Lorenzo Research

ProfessorNU jmartine@ece.neu.edu 616.373.2847

Borja Gonzalez-Valdes Postdoc NU bgonzale@ece.neu.edu 617.373.8511Students

Name Degree Pursued

Institution Email Intended Year of Graduation

Gregory Allan BS NU allan.g@husky.neu.edu 2016Ben Berkowitz BS NU Berkowitz.be@husky.neu.edu 2015Galia Ghazi PhD NU ghazi.g@husky.neu.edu 2013Yolanda Rodriguez-Vaqueiro

MS NU rodriguezvaqueiro.y@husky.neu.edu

2013

Richard Obermeier BS NU richard.obermeier@gmail.com 2013Fernando Quivira BS NU f.quivira@gmail.com 2013

II. PROJECT OVERVIEW AND SIGNIFICANCEAs the problem of identifying suicide bombers wearing explosives concealed under clothing becomes in-creasingly important, it becomes essential to detect suspicious individuals at a distance. Systems which em-ploy multiple sensors to determine the presence of explosives on people are being developed. Their functions include observing and following individuals with intelligent video, identifying explosive residues or heat sig-natures on the outer surface of their clothing, and characterizing explosives using penetrating X-rays [1] [2], terahertz waves [3, 4, 5], neutron analysis [6, 7] or nuclear quadrupole resonance (NQR) [8, 9]. At present, radar is the only modality that can both penetrate and sense beneath clothing at a distance of 10 to 50 meters without causing physical harm. The objective of this project is the development and evaluation of an inexpensive, high-resolution radar that can distinguish foreign objects hidden on individuals at a distance, and that can still fit in or on a van. During the last year, the following activities were developed in the areas of standoff detection of potential

suicide bombers: 1) Numerical Analysis methods to simulate the performance of a 3D radar system; 2) Ad-vanced signal processing for threat detection based on regularized solutions like compressed sensing and Tickhonov regularization; 3) Data evaluation and performance evaluation. The latter activity was performed with data that was collected for the International Collaboration grant that this group had with the Depart-ment of Homeland Security. • This project would be the first inexpensive, high-resolution radar system with special application to re-

motely detecting and identifying potential suicide bombers.• This project explores new theories on the subject of signal processing algorithms for suicide bomber

detection radar systems. In this effort, effects like polarization, mutual coupling, and constitutive param-eters of the target - typically ignored in current signal processing algorithms - are taken into account.

• This project has the potential to be the first radar system that is capable of functioning at multiple ranges for both indoor and outdoor scenarios.

III. RESEARCH AND EDUCATION ACTIVITY

A. State-of-the-ArtandTechnicalApproach

Current first generation (1G) and second generation (2G) prototypes of mm-wave radar systems developed by our international research group and applied to the standoff detection problem produce two-dimensional (2D) images which map points across the chest of a target (integrated over the vertical extent of the radar beam) versus range (the distance between the radar and the target). Such images are based on a reconstruc-tion of the reflectivity function using Synthetic Aperture Radar (SAR) processing. Our group has demonstrat-ed that these images show clear signatures of concealed metallic structures at standoff distances of 10-20 meters [10-13]. Specifically, our work has shown that a person not wearing metallic pipes bombs produces an SAR image that contains relatively smooth variation of pixel intensity across the chest of the subject, while a human subject wearing metallic pipes produces an SAR image that contains abrupt variations across the chest, with these variations corresponding to the positions of the metallic pipes. The challenge arises when the metallic pipes are replaced by other realistic explosive configurations, e.g.[TNT, RDX, PETN, HMX, C4, SEMTEX, ANFO, TATP] rods with or without shrapnel, which have an electromagnetic signature that is much less intense than that of cylindrical metallic pipes. Our current 1G/2G radar systems are not as successful at detecting these dielectric/shrapnel explosive signatures. We believe the reason for this poor performance is that the non-instantaneous motion of the 2G receiving antenna used to create the linear synthetic aperture introduces random components both from receiving the signal and from target mo-tion, that blur the SAR image, making the threat detection of these structures unfeasible. This year, we have been designing and optimizing a third generation (3G) system, which is based on an array of static receivers that does not require motion and could record radar signals across the aperture practically simultaneously, thus eliminating the blurring effect. Additionally, the 1G/2G systems resolve well only in range and hori-zontally, as the synthetic aperture was created by horizontal receiver motion. To produce three-dimensional (3D) images (i.e. resolve the chest in both width and height), the static receiving antennas must be distrib-uted on a two-dimensional plane as compared to the dynamic 1G and 2G linear scanning configuration. If additional funding is secured and the and a 3G system prototype can be assembled, it would be possible to distinguish dielectric structures such as [nitrate-containing and peroxide-based explosives within glass or ceramic casing] due to the higher resolution of the target area. Additionally, such 3D images should provide information that can be more easily interpreted by an operator of the system, resulting in improved prob-ability of threat detection.

B. MajorContributions

During the last year, we have been working on a completely new concept that has the potential to revolu-

tionize the way in which we detect potential suicide bombers at standoff distances. In particular, we have been studying a 3D system configuration based on compressive sensing imaging and optimization; this new concept makes use of a Passive Array of Scatters (PAS), which ultimately provides resolutions of 7.5 mm at standoff distances of 40 m. The system configuration, the mathematical formulation for the imaging prob-lem, and some numerical examples for this novel configuration are summarized in the following subsections.

B.1-SystemConfiguration

B.1.1-Systemconceptofoperation

The proposed system configuration is shown schematically in Fig. 1. It is composed of an inexpensive, high-resolution radar system that can distinguish foreign objects hidden on individuals at a distance, and that can still fit in or on a van. Additionally, a PAS is placed between the radar and the person under test in order to be able to achieve a super-resolution radar system. The concept of using multiple PAS over an imposed trajec-tory (see Fig. 1(b)) – for person movement in places like airport terminals or bus stations – provides the sys-tem with the option of re-configurability so that it might be applicable to indoor scenarios at multiple ranges.

B.1.2Systemparameters

Figure 2 represents a top view of the configuration and parameters of the system. The blue dots, on the left, represent the positions of the transmitting and receiving antennas. The radar is located on a square aperture of width L1, and the total number of transmitting/receiving antennas is na. The orange dots, at the center of the image, represent the positions of the elements composing the PAS. The PAS is also located on a square aperture of width L2, and the total number of elements on the PAS is nd. The person under test is represented by the red silhouette on the right; and the reconstruction is performed by the imaging algorithm on a two dimensional plane, represented by a red line in Fig.2, located in front of the person under test withnp pixels. The distance between the radar and the person under test is Z0, and the distance between the PAS and the

person under test is Z2. The resolution of the radar system, which is equal to the pixel size of the reconstructed im-age, is indicated by the parameter ; and the total number of pixels in the image is np.

B.2-Mathematicalformulationfortheimagingproblem

B.2.1-Sensingmatrix

The sensing matrix, used by the imag-ing algorithm, is computed by using the phase term associated with an electro-magnetic wave traveling as follows: 1) from each one of the transmitting an-tennas to each one of the scatters in the PAS; 2) from each one of the scat-ters on the PAS to each pixel on recon-struction plane; 3) from each pixel on the reconstruction plane to each one of the scatters on the PAS; and 4) from

Figure 1: (a) General sketch of our van-based, high resolution radar system for standoff detection of potential suicide bombers. (b) Top view of the multiple-range concept of operation.

40

each one of the scatters on the PAS to each receiving antenna. This approximation is based on the following assumptions: 1) the amplitude attenuation associated with the electromagnetic wave propagation is considered to be constant, since it’s im-pact on the quality of the reconstructed image is negligible; 2) the mutual coupling among pixels in the reconstructed image is not taken into account; 3) the amplitude and phase of the induced currents on the reconstruction plane is proportional to the incident field produced by radar illumina-tion –the latter approximation is equiva-lent to traditional Physical Optics method. The system works on a multiple mono-static configuration, in which each element

of the array transmits and receives on different slots of time without interacting with the radiation of other elements in the array.

Under this configuration, the sensing matrix establishes a linear relationship between the un-known complex reflectivity vector and the measured complex field data . This relationship can be expressed in a matrix form as follows:

where represents the noise collected by each receiving antenna. The matrix can be rewritten as the product of two matrices: 1) Eb, which is a diagonal matrix accounting for the background incident field pro-duced by a single transmitting/receiving antenna and PAS on the reconstruction plane; and 2) P, which is a full matrix accounting for the propagation from each point on the reconstruction plane to each transmitting/receiving antenna after passing though the PAS. After applying some algebraic operations, the coefficients aijof the sensing matrix A can be expressed as follows:

where k is the free space wave number; ri is a vector indicating the position of the i-th transmitting/receiving antenna; rj is a vector indicating the position of the j-th pixel in the reconstruction plane; and rk’’ is a vector indicating the position of j-th scatter in the PAS.

B.2.2-Imagingalgorithmusingcompressivesensingapproach

The proposed radar system is designed in accordance with the compressive sensing theory [14-20]. In order to apply such principles for standoff detection of explosive related-threats, certain mathematical conditions must be satisfied by the sensing matrix A and the reconstructed reflectivity image x. These conditions can be summarized as follows [17]: 1) the sensing matrix must satisfy the Restricted-Isometry-Property condition, which is related to the independency of the columns of the matrix; and 2) the unknown reflectivity vector must accept a sparse representation as a solution, which related to the number of non-zero entries on the solution vector. The parameters of the systems can be modified until these two conditions are satisfied; the

Fig 2– Top view of the radar configuration. The blue circles on the left represent an thinned array of transmitter/receiver antennas; the orange dots on the center represent the passive array of scatters, which randomly redirect the energy of the radar towards the target; the person under test (target) is represented by the red silhouette on the right, and the two dimensional plane over which the reconstruction is implemented is represented by the red line in front of the person under test.

optimized parameters include the following: aperture length of the radar, aperture length of the PAS, resolu-tion in the reconstruction plane, number of antennas on the radar aperture, number of scatters in the PAS, working frequency, separation between the radar and the PAS, separation between the PAS and the target. This optimization is done manually, but it is expected that in further research contributions such optimiza-tion process should be automatized. If the two aforementioned conditions are satisfied, then the reconstruction of the unknown vector can be performed with a small number of measurements (transmitting/receiving antennas) by solving the following convex problem [20]:

where represent the norm-one of the vector x. In the particular case where x is not sparse, the problem can still be solved if one can find a discretized functional W, in which a sparse representation xp of the un-known vector xcan be found through the following relationship: xp=Wx. Therefore, the “Compressive Sens-ing” problem can be now solved by the following problem:

A Total Variation (TV) functional W is used in this particular work [20]. The TV functional W computes and adds the two directional gradients of the image x for each pixel; thus achieving a sparse representation xp of the original image x.

B.3-NumericalExamples

B.3.1-Radarconfiguration

The imaging principles described in the previous section are evaluated on two different scenarios (see Table I.): configuration #1, in which the distance between the radar and person under test is ten meters; and con-figuration #2, in which the distance between the radar and person under test is forty meters. Table 1 also summarizes all the parameters used for the numerical simulations. It is important to realize that in order to increase the range by a factor of four, from ten to forty meters, the length of the radar aperture must also be increased by a factor of four, and the number of antennas in such aperture must also be increased by a 60% factor, from five to eight hundred. The size and the number of scatters of the PAS is the same for both configu-rations, leading to the same system resolution of 7.5 millimeters. For the simulations in this work, a uniform white noise of 25 dB of signal to noise ratio is considered; and the working frequency of the system is 60 GHz.

B.3.2-Targetspecification

In order to test the feasibility of the system, a projection into a two dimensional plane of the three dimensional geometry, -- a person with attached explosives-- is used as ground truth for the imaging algorithm. This two dimensional simplification of the three dimensional problem allows for a fast reconstruc-tion using only one frequency for the radar configuration, and its extension to the three dimensional problem can be easily implemented in the future (see Fig. 3 on following page). Fig. 4 (a) shows the two dimensional projection of a person under test; and Fig. 4 (b) shows the same person with two

Table 1. Parameters for the numerical examples.

different types of explosive stimulants: two vertical metallic pipes of high reflectivity, and one square made of TNT of low dielectric reflectivity. The colorbar in the image indicate the absolute value of the reflectivity divided by the average reflectivity on the whole image.

B.3.3-Reconstructionresults

Fig. 5 (a) and (b) shows the reconstructed image when traditional Fourier-based SAR techniques [21] are used for the case of a person without and with explosive stimulants located at ten meters from the radar system (Configuration #1). This algorithm did not use the PAS; and, therefore, the resolution of the system is limited to that of the radar aperture. The quality of the reconstruction is quite deficient, and it is very difficult to discern the threat from the no-treat cases. Only an amplitude-based algorithm could be used to distinguish between the cases. The threat case, containing metallic pipes, shows some pixels with higher intensity level than those of the no-threat case. When the PAS is introduced and the norm-one minimization is used for the imaging algorithm, the quality of the reconstructed images, for both threat and no-threat cases, is substantially improved when compared to those produced by traditional SAR imaging algorithms [22-23] – as it can be seen on Fig. 6 (a)-(b). Fig. 6(c) shows the reconstructed image for configuration #2, in which the radar and the target are separated 40 meters, when the PAS and the norm-one minimization on the imaging algorithm are used. Standoff detec-tion at 40 meters requires that the length of the square radar aperture be increased from 0.4 meters to 1.6 meters, and the number of transmitting/receiving antennas is also increased from 500 to 800. This upgraded version of the system is capable of producing a resolution of 7.5 millimeters at 40 meters range.

B.3-Keypointsaboutthenewstandoffdetectionsystem

This report has described a new millimeter wave imaging system, which is able to produce super resolution images at standoff distances. Unlike traditional imaging systems in which the radar system directly illumi-nates the target under test, this system illuminates a passive array of scatters that redirects the energy of the radar towards the person under test. The PAS can be seen as a magnification lens that is located in front of the target, producing a super-resolution image. The imaging algorithm used for this system is based on com-pressive sensing theory. This imaging algorithm is different than traditional SAR algorithms because instead

Figure 3: Three dimensional views of a human person carrying explosive simulants (left and center images), and a projection of the geometry into a two dimensional plane used as a ground truth for the reconstruction algorithm (right image).

Figure 4: Projection of the person under test used as ground truth by the imaging algorithm: (a) no-threat case, (b) threat case composed of two metallic pipes with high reflectivity and TNT square of low dielectric reflectivity.

Figure 5: Reconstruction using traditional Fourier-based SAR algorithms for configuration #1: (a) no-threat case, and (b) threat case.

Figure 6: Reconstruction using compressive sensing and the passive array of scatters: (a) no-threat case in configuration #1, (b) threat case in configuration #1, (c) threat case in configuration #2.

of just performing a Fourier transform of the measured data, it solves a norm-one minimization problem. Another important feature of this system is that it can be configured to work at multiple ranges if a specified trajectory is imposed on the person under test, making this system well suited for deployment for indoor spaces such as airport terminals or bus stations. The performance of the system in terms of quality of the reconstructed image was tested for two target range configurations 10 and 40m. In both cases, the system produced a resolution of 7.5 mm. The same PAS was used for both configurations, but it was necessary to increase the size of the radar aperture for the farther case to achieve the required 7.5 mm resolution.

C. MajorContributions

1) System design and performance evaluation: This year, we have designed a new radar configuration based on a sparse array of transmitting/receiving antennas and a passive array of scatters. This new configuration has been proved to be successful for detecting potential security threats at standoff distances. Additionally, we have demonstrated how this millimeter wave radar can be used to achieve a resolution of 7.5 mm at ranges of 40 meters. 2) Numerical Analysis for the detection of suicide bombers: We have implemented a three dimensional point scattering method that can be used to compute the scattered fields produced by a potential suicide bomber at standoff distances. 3) Signal processing for SAR systems: We have implemented a norm-one minimization imaging algorithm, which substantially improve the quality of the image when compared with traditional Fourier-based algo-rithms. Additionally, we have accelerated our traditional imaging algorithms by means of a new Inverse Fast-Multipole-Method (I-FMM). Finally, a new inversion based on Singular Value Decomposition and Tickhonov regularization has also been employed in order to de-convolve the radar Point Spread Function from the reconstructed image.

IV. FUTURE PLANS

A. ResearchPlan

• Develop a three dimensional asymptotic forward method, like Physical Optics, able to generate synthetic data for the standoff detection problem.

• Incorporate a three dimensional full wave forward method for the analysis of the passive array of scat-ters, so that the mutual interactions among dipoles are also included on the generation of synthetic data.

• Develop a three dimensional reconstruction based on Compressive Sensing and the passive array of scat-ters. This can be done in three different ways: 1) using multiple frequencies with the current norm-one minimization algorithm, 2) using multiple layers for the passive array of scatters, and 3) combining both multiple layers on the passive array of scatters and multiple frequencies.

• Develop a three dimensional full wave forward method, like Steepest Descent Fast Multipole Method (SD-FMM) or Finite Differences in the Frequency Domain (FDFD), able to generate synthetic data for the standoff detection problem.

• Create datasets using the radar system to validate the algorithms described above.

B. EducationalPlan

Graduate student Galia Ghazi will continue to play an important role in our research project. She will assist in developing new simulations and inversion algorithms for detecting suicide bombers using millimeter wave

radar systems. Undergraduate students Richard Obermeier, Fernando Quivira, Ben Berkowitz and Gregory Allan are active participants in the research group. They conduct numerical modeling experiments to identify target scat-tering and discrimination features. Populating the research group with undergraduates brings homeland security technologies to undergraduate engineering students, and establishes a pipeline to train and provide a rich pool of talented new graduate student researchers.

V. RELEVANCE AND TRANSITION

• Standoff detection of threats is a major challenge for DHS.• An improved technology platform and associated algorithms will enhance the current state of the art.• A higher three dimensional resolution coupled with new feature detection will integrate into automatic

threat detection systems.• A well-conceived experiment is essential to validate models, inversion principles, and the concept of op-

eration.• The imaging algorithms that have been developed for this grant have been presented to L3-communica-

tions, which is one of the major manufactures of millimeter wave imaging systems in the country, in order to transitioning them into their systems.

VI. LEVERAGING OF RESOURCES

The product we are developing has a deep impact on the scientific community and is also of interest to several federal offices. New proposals related to the topic of this research will be submitted to other funding sources such as the International Collaboration grant opportunity from the Department of Homeland Security.

VII. DOCUMENTATION

A. Publicationsandconferencepresentations

1. Fernandes, J., Obermeier, R., Martinez-Lorenzo, J.A., and Rappaport, C., “FMCW SAR imaging of body worn explosives from FDFD modeled scattered field data” Progress in Electromagnetics Research Symposium, Cambridge, MA, July 2010, pp. 350. (Acknowledgement prohibited by publication)

2. Fernandes, J., Rappaport, C., Martinez-Lorenzo, J. A., Hagelen, M., “Experimental results for standoff detec-tion of concealed body-worn explosives using millimeter-wave radar and limited view ISAR processing,” IEEE Homeland Security Technology Conference, Waltham, MA, May 11, 2009,

3. Rappaport, C., Fernandes, J., Martinez-Lorenzo, J. A., Hagelen, M., “Experimental results for standoff detec-tion of concealed body-worn explosives using millimeter-wave radar and limited view ISAR processing,” Gordon Research Conference, Les Diablerets, Switzerland, June 16, 2009, one page.

4. Martinez-Lorenzo, J. A., and Rappaport, C., “Underground Focusing Spotlight Synthetic Aperture Radar for Tunnel Detection Applications,” IEEE International Symposium on Antennas and Propagation, Charleston, SC, June, 2009, four pages on CD.

5. Martinez-Lorenzo, J., Rappaport, C., Quivira, F., “Physical Limitations on Detecting Tunnels using Under-ground Focusing Spotlight Synthetic Aperture Radar,” IEEE International Geoscience and Remote Sensing Symposium, July 2009, Cape Town South Africa, pp. I-160 – 163.

6. Martinez-Lorenzo, J., Rappaport, C., and Quivira, F., “Physical Limitations on Detecting Tunnels using Un-

derground Focusing Spotlight Synthetic Aperture Radar,” accepted for publication in 2010 IEEE Transac-tions on Geoscience and Remote Sensing.

7. Quivira, F., Martinez-Lorenzo, J.A., and Rappaport, C., “Impact of Soil Roughness in Under- ground Focus-ing SAR Images,” Progress in Electromagnetics Research Symposium, Cambridge, MA, July 2010, pp.30. (Acknowledgement prohibited by publication)

8. H. Gomez-Sousa, J. A. Martinez-Lorenzo, O. Rubinos-Lopez, M. Grana-Varela, B. Gonzalez-Valdes, M. Arias-Acuna. “Strategies for improving the use of the memory hierarchy in an implementation of the Modified Equivalent Current Approximation (MECA) method,” Applied Computational Electromagnetics Society Journal, 25(10):841–852, January 2010.

9. J. G. Meana, J. A. Martinez-Lorenzo, Fernando Las-Heras and C. M. Rappaport. “Wave Scattering by Dielec-tric and Lossy Materials Using the Modified Equivalent Current Approximation (MECA),” IEEE Transac-tions on Antennas and Propagation, 58(11):3757–3761, November 2010.

10. J. A. Martinez-Lorenzo, C. M. Rappaport and F. Quivira. “Physical Limitations on detecting tunnels using underground focusing spotlight synthetic aperture radar,” IEEE Transactions on Geoscience and Remote Sensing, 59(1):65–70, January 2011.

11. J. G. Meana, J. A. Martinez-Lorenzo, Fernando Las-Heras, “DIRECT: deterministic radio-electric coverage tool,” IEEE Antennas and Propagation Magazine, 53(2):135–145, April 2011, Work featured on the cover page.

12. J. A. Martinez-Lorenzo, B. G. Valdes, C. M. Rappaport and A. G. Pino. “Reconstructing distortions on reflec-tor antennas with the iterative-field-matrix method using near-field observation data,” IEEE Transactions on Antennas and Propagation, 59(6):2434–2437, June 2011.

13. Y. Alvarez, J. A. Martinez-Lorenzo, F. Las-Heras and C. M. Rappaport. “An inverse fast multipole method for imaging applications,” IEEE Antennas and Wireless Propagation Letters, 10:1259–1262, 2011.

14. B. G. Valdes, J. A. Martinez-Lorenzo, C. M. Rappaport and A. G. Pino. “A non-iterative approach to subreflec-tor shaping for reflector antenna distortion compensation,” IEEE Transactions on Antennas and Propaga-tion, accepted for publication.

15. J. A. Martinez-Lorenzo, F. Quivira and C. M. Rappaport. “SAR imaging of suicide bombers wearing con-cealed explosive threats,” Progress In Electromagnetics Research, 125:255–272, 2012.

16. Y. Alvarez, B. Valdes, J. A. Martinez-Lorenzo, F. Las-Heras and C. M. Rappaport. “Human body profile recon-struction for detection of concealed objects,” IEEE Transactions on Antennas and Propagation, ac-cepted for publication.

17. Y. Alvarez, J. A. Martinez-Lorenzo, F. Las-Heras and C. M. Rappaport. “An inverse Fast Multipole Method for geometry reconstruction using scattered field information,” IEEE Transactions on Antennas and Propa-gation, 60(7):3351-3360, July 2012, doi:10.1109/TAP.2012.2196950

18. H. Gomez-Sosa, J. A. Martinez-Lorenzo and O. Rubinos. “Three-dimensional wedge diffraction correction deduced by the stationary phase method on the modified equivalent current approximation,” Progress In Electromagnetics Research - M, 23:207–227, 2012.

19. A. Farid, J. A. Martinez-Lorenzo, A. Alshawabkeh and C. M. Rappaport. “Experimental validation of a nu-merical forward model for tunnel detection using cross-borehole radar,” ASCE, Journal of Geotechnical and Geoenvironmental Engineering, accepted for publication, doi:10.1061/(ASCE)GT.1943-5606.0000716

20. Y. Alvarez, B. Valdes, J. A. Martinez-Lorenzo, F. Las-Heras and C. M. Rappaport. An improved SAR based technique for accurate profile reconstruction. IEEE Transactions on Antennas and Propagation, accepted for publication.

21. F. Quivira, J. A. Martinez-Lorenzo and C. M. Rappaport. “Impact of the wave number estimation in under-

ground focused SAR imaging,” Progress In Electromagnetics Research Letters, 32:29-38, 2012.22. L. Tirado, B. Valdes, J. A. Martinez-Lorenzo, C. M. Rappaport, H. Gomez-Sosa and O. Rubinos. “GPU imple-

mentation of the Modified Equivalent Current Approximation (MECA) method,” Applied Computational Electromagnetics Society Journal, accepted for publication.

23. J. A. Martinez-Lorenzo, Y. Rodriguez-Vaqueiro, C. M. Rappaport, A. G. Pino and O. Rubinos. “A compressed sensing approach for detection of explosive threats at standoff distances using a Passive Array of Scatters,” CD Proc., HST 2012, IEEE International Conference on Technologies for Homeland Security, Waltham, MA, Nov. 2012.

24. B. Gonzalez-Valdes, J. A. Martinez-Lorenzo, C. M. Rappaport, Y. Alvarez and F. Las-Heras. Three- dimen-sional Millimeter-Wave Portal for Human Body Imaging. CD Proc., HST 2012, IEEE International Confer-ence on Technologies for Homeland Security, Waltham, MA, Nov. 2012.

25. G. Ghazi, J. A. Martinez-Lorenzo and C. M. Rappaport. “A new super-resolution algorithm for millimeter wave imaging on security applications,” CD Proc., HST 2012, IEEE International Conference on Technolo-gies for Homeland Security, Waltham, MA, Nov. 2012.

26. Y. Alvarez, F. Las-Heras, J. A. Martinez-Lorenzo and C. M. Rappaport. “On the fast multipole method appli-cation for inverse problems,” CD Proc., EuCAP 2012, VI European Conference on Antennas and Propaga-tion, to be held in Prague, Czech Republic, March 2012.

27. B. G. Valdes, F. Quivira, J. A. Martinez-Lorenzo and C. Rappaport. “Tunnel detection using underground-focusing spotlight SAR and rough surface estimation”, CD Proc., AP-S 2012, IEEE AP-S International Sym-posium, Chicago, IL, Jul. 2012.

28. B. G. Valdes, J. A. Martinez-Lorenzo, C. Rappaport, Y. Alvarez and F. Las-Heras. “3D Whole-Body-Imaging for detecting explosive-related threats,” CD Proc., AP-S 2012 | IEEE AP-S International Symposium, Chi-cago, IL, Jul. 2012.

29. B. G. Valdes, J. A. Martinez-Lorenzo, C. Rappaport and A. G. Pino. “Fast multi-frequency algorithm for radar based profile reconstruction,” CD Proc., AP-S 2012 | IEEE AP-S International Symposium, Chicago, IL, Jul. 2012.

30. C. M. Rappaport, Y. Rodriguez-Vaqueiro, J. A. Martinez-Lorenzo, J. Beaty and W. Naqvi. “Phenomenological scattering analysis of an RF area secure perimeter,” CD Proc., HST 2011, IEEE International Conference on Technologies for Homeland Security, Waltham, MA, Nov. 2011.

B. Otherconferencepresentations(noproceedings)

1. Yuri Lopez , J. A. Martinez-Lorenzo, B. Valdes, C. M. Rappaport. “3D Whole Body Imaging for detecting explosive-related threats,” The Gordon-CenSSIS Research and Industrial Collaboration Conference, Bos-ton, MA, USA, Oct 13-14, 2011.

2. Spiros Mantzavinos, K. Williams, G. Ghazi, J. Rooney, J. A. Martinez-Lorenzo, B. Valdes, R. Moore, Y. Lopez and C. M. Rappaport. “Building a high performance, flexible multi-modality millimeter-wave imaging radar person-screening testbed,” The Gordon-CenSSIS Research and Industrial Collaboration Conference, awarded as honorable mention student poster, Boston, MA, USA, Oct 13-14, 2011.

3. Yolanda Rodriguez-Vaqueiro, J. A. Martinez-Lorenzo, B. Valdes, A. Morgenthaler, J. Beaty and C. M. Rap-paport. “Phenomenological scattering analysis of an RF area secure perimeter,” The Gordon-CenSSIS Re-search and Industrial Collaboration Conference, awarded as honorable mention student poster, Boston, MA, USA, Oct 13-14, 2011.

4. Kathryn Williams, J. A. Martinez-Lorenzo, B. Valdes, Y. Alvarez and C. M. Rappaport. “Feasibility of com-putational methods for realistic simulation and image reconstruction for millimeter-wave whole body

imaging,” The Gordon-CenSSIS Research and Industrial Collaboration Conference, awarded as honorable mention student poster, Boston, MA, USA, Oct 13-14, 2011.

5. Galia Ghazi, S. Mantzavinos, J. A. Martinez-Lorenzo and C. M. Rappaport. “FMCW signal processing for detecting explosives-related threats using near field and standoff MMWR,” The Gordon-CenSSIS Research and Industrial Collaboration Conference, Boston, MA, USA, Oct 13-14, 2011.

6. Ben Berkowitz, J. A. Martinez-Lorenzo, B. Valdes and C. M. Rappaport. “Optimization of an array of an-tenna elements for millimeter wave imaging,” The Gordon-CenSSIS Research and Industrial Collaboration Conference, awarded as honorable mention student poster, Boston, MA, USA, Oct 13-14, 2011.

7. Luis Tirado, J. A. Martinez-Lorenzo, B. Valdes and C. M. Rappaport. “Near-field fast forward models and signal processing for Whole-Body-Imaging,” The Gordon-CenSSIS Research and Industrial Collaboration Conference, Boston, MA, USA, Oct 13-14, 2011.

8. G. Ghazi, J. A. Martinez-Lorenzo, K. Williams, S. Kilcoyne, G. Ghazi, T. Hayes, M. Buttimer, D. Dow, C. Rap-paport, D. Busoic and R. Moore, “Building a High Performance, Flexible Mutli-Modality Millimeter-Wave Imaging Radar Person-Screening TestBed,” ALERT site visit, Boston, MA, USA, March 24-25, 2011.

9. S. Mantzavinos, J. A. Martinez-Lorenzo, F. Quivira and C. M. Rappaport. “Millimeter-Wave Stand-off Radar Detection System with Motion Compensation and 3D Imaging Capabilities,” ALERT site visit, Boston, MA, USA, March 24-25, 2011.

10. J. A. Martinez-Lorenzo, L. Tirado, F. Quivira, G. Ghazi, K. Williams and C. Rappaport, Near Field Signal Pro-cessing for the Whole Body Imaging Inversion Problem, ALERT site visit, Boston, MA, USA, March 24-25, 2011.

11. K. Williams, J. A. Martinez-Lorenzo and C. M. Rappaport. “Computational Modeling of Close-in Millimeter Wave Radar for the Detection of Concealed Threats,” ALERT site visit, Boston, MA, USA, March 24-25, 2011.

C. Patents

J. A. Martinez-Lorenzo and C. M. Rappaport. “Signal Processing Algorithm for Explosive Detection and Identi-fication using Electromagnetic Radiation,” To be submitted to the Office of Technology Innovation and Com-mercialization office at Northeastern University.

VIII. REFERENCES

[1] J. Yinon, “Field detection and monitoring of explosives,” Trends in analytical chemistry, vol. 21, no. 4, pp. 415–423, 2002.[2] J. Yinon, Forensic and Environmental Detection of Explosives. Chichester: John Wiley and Sons, 1999.[3] M. Leahy-Hoppa, M. Fitch, X. Zheng, L. Hayden, and R. Osiander, “Wideband terahertz spectroscopy of explosives,” Chemical Physics Letters, vol. 424, no. 8, pp. 227–230, 2007.[4] D. J. Cook, B. K. Decker, and M. G. Allen, “Quantitative thz spectroscopy of explosive materials,” in Optical Terahertz Sicience and Technology, Orlando, Florida, 14-16 March, 2005.[5] H. Liu, Y. Chen, G. J. Bastiaans, and X. Zhang, “Detection and identification of explosive rdx by thz diffuse reflection spectroscopy,” Optics Express, vol. 14, pp. 415–423, 1 2006.[6] P. Shea, T. Gozani, and H. Bozorgmanesh, “A tnt explosives-detection system in airline baggage,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associ-ated Equipment, vol. 299, no. 20, pp. 444–448, December 1990.[7] C. L. Fink, B. J. Micklich, T. J. Yule, P. Humm, L. Sagalovsky, and M. M. Martin, “Nuclear instruments and

methods in physics research section b: Beam interactions with materials and atoms,” Evaluation of neutron techniques for illicit substance detection, vol. 99, no. 1-4, pp. 748–752, May 1995.[8] H. Itozaki and G. Ota, “International journal on smart senssing and intelligent system,” Nuclear quadru-pole resonanace for explosive detection, vol. 1, no. 3, pp. 705–715, september 2008.[9] J. B. Miller and G. A. Barral, “Explosives detection with nuclear quadrupole resonance,” American Scien-tist, vol. 93, pp. 50–57, January-February 2005.[10] Martinez-Lorenzo J. A., Rappaport C. M., Sullivan R., and Angell A., “Standoff concealed explosives de-tection using millimeter-wave radar to sense surface shape anomalies,” in CD Proc., AP-S 2008, IEEE AP-S International Symposium, San Diego, CA, USA, 6 2008.[11] Valdes B. G., Martinez-Lorenzo J. A., Rappaport C. M., and Pino A. G., “Design and near field based op-timization on an array of reflectors as radar antenna to sense surface shape anomalies,” in Proc., AP-S 2009 IEEE AP-S International Symposium, Charleston, SC, USA, 6 2009.[12] Fernandes J., Martinez-Lorenzo J. A., Hagelen M., and Rappaport C., “Experimental results for standoff detection of concealed body-worn explosives using millimeter-wave radar and limited view isar processing,” in Homeland Security Technology Conference, Woburn, MA, HST 2009. IEEE International, 5 2009, pp.1157–1160.[13] J. A. Martinez-Lorenzo, F. Quivira and C. M. Rappaport. SAR imaging of suicide bombers wearing con-cealed explosive threats. Progress In Electromagnetics Research, 125:255–272, 2012.[14] E. Candès, J. Romberg, and T. Tao, “Robust Uncertainly Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information” , IEEE Transactions on Information Theory, 52, 2, February 2006, pp. 489-502.[15] E. Candès, J. Romberg, and T. Tao, “Signal Recovery from Incomplete and Inaccurate Measurements” , Communications on Pure and Applied Mathematics, 59, 2006, pp. 1207-1223.[16] D. L. Donoho, “Compressed Sensing”, IEEE Transactions on Information Theory, 52, 4, April 2006, pp. 1289-1306.[17] R.G. Baraniuk, “Compressive Sensing”, IEEE Signal Processing Magazine, 24(4), pp.118-121, July 2007.[18] A. C. Fannjiang, T. Strohmer, and P. Yan. “Compressed Remote Sensing of Sparse Objects”, SIAM J. Imag-ing Sciences, Vol. 3, No. 3, 2010, pp. 595-618.[19] M. D. Migliore, D. Pinchera, “Compressed Sensing in Electromagnetics: Theory, Applications and Per-spectives”, Proc. of the EuCAP, Rome (Italy), 2011.[20] S. Becker, J. Bobin, E. J. Candès. “NESTA: A Fast accurate first-order method for sparse recovery” Siam J. on Imaging Sciencies, Vol. 4, pp. 1-39.http://www-stat.stanford.edu/~candes/nesta/[21] D. M. Sheen, D. L. McMakin, T. E. Hall, “Three-Dimensional Millimeter-Wave Imaging for Concealed Weapon Detection,” IEEE Transactions on Microwave Theory and Techniques, Vol. 49, No. 9, pp. 1581-1592, September 2001.[22] Y. Alvarez, J. A. Martínez, F. Las-Heras, C. M. Rappaport, “An inverse Fast Multipole Method for geometry reconstruction using scattered field information”. IEEE Transactions on Antennas and Propagation, Vol. 60, No. 7, pp. 3351-3360, July 2012.[23] Y. Alvarez, J. A. Martinez-Lorenzo, F. Las-Heras and C. M. Rappaport. “An inverse fast multipole method for imaging applications,” IEEE Antennas and Wireless Propagation Letters, 10:1259–1262, 2011.