{"id":21,"date":"2015-10-15T09:14:08","date_gmt":"2015-10-15T13:14:08","guid":{"rendered":"https:\/\/sites.temple.edu\/aspl\/?page_id=21"},"modified":"2020-03-04T01:47:07","modified_gmt":"2020-03-04T06:47:07","slug":"research-areas","status":"publish","type":"page","link":"https:\/\/sites.temple.edu\/aspl\/research-areas\/","title":{"rendered":"Research Areas"},"content":{"rendered":"

Radar Signal Processing and Direction-of-Arrival Estimation
\n<\/strong><\/span><\/p>\n

We develop signal processing techniques and algorithms for radar imaging, target localization and tracking, motion classification, and clutter suppression, applied in passive radar, over-the-horizon radar, through-the-wall radar, and fall detection for assisted living. \u00a0 Our recent work features sparse reconstruction, Bayesian learning, and sparse array design.<\/p>\n

Selected publications:<\/strong><\/p>\n

S. Qin, Y. D. Zhang, and M. G. Amin, \u201cImproved two-dimensional DOA estimation using parallel coprime arrays<\/a>,\u201d Signal Processing, in press.<\/p>\n

G. Qin, M. G. Amin, and Y. D. Zhang, \u201cDOA estimation exploiting sparse array motions,\u201d IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 3013-3027, June 2019.<\/p>\n

Z. Zheng, W-Q. Wang, Y. Kong, and Y. D. Zhang, \u201cMISC Array: A new sparse array design achieving increased degrees of freedom and reduced mutual coupling effect<\/a>,\u201d IEEE Transactions on Signal Processing, vol. 67, no. 7, pp. 1728-1741, April 2019.<\/p>\n

M. Wang, Y. D. Zhang, and G. Cui, \u201cHuman motion recognition exploiting radar with stacked recurrent neural network<\/a>,\u201d Digital Signal Processing, vol. 87, pp. 125-131, April 2019.<\/p>\n

G. Qin, Y. D. Zhang, and M. G. Amin, \u201cDOA estimation exploiting moving dilated nested arrays,\u201d IEEE Signal Processing Letters, vol. 26, no. 3, pp. 490-494, March 2019.<\/p>\n

C. Zhou, Y. Gu, X. Fan, Z. Shi, G. Mao, and Y. D. Zhang, \u201cDirection-of-arrival estimation for coprime array via virtual array interpolation<\/a>,\u201d IEEE Transactions on Signal Processing, vol. 66, no. 22, pp. 5956-5971, Nov. 2018.<\/p>\n

C. Zhou, Y. Gu, Z. Shi, and Y. D. Zhang, \u201cOff-grid direction-of-arrival estimation using coprime array interpolation<\/a>,\u201d IEEE Signal Processing Letters, vol. 25, no. 11, pp. 1710-1714, Nov. 2018.<\/p>\n

M. Guo, Y. D. Zhang, and T. Chen, \u201cDOA estimation using compressed sparse array,\u201d IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 4133-4146, Aug. 2018.<\/p>\n

M. Guo, Y. D. Zhang, and T. Chen, \u201cPerformance analysis for uniform linear arrays exploiting two coprime frequencies,\u201d IEEE Signal Processing Letters, vol. 25, no. 6, pp. 838-842, June 2018.<\/p>\n

K. Liu and Y. D. Zhang, \u201cCoprime array-based DOA estimation in unknown nonuniform noise environment,\u201d Digital Signal Processing, vol. 79, pp. 66-74, April 2018.<\/p>\n

I. Djurovic and Y. D. Zhang, \u201cAccurate parameter estimation of over-the-horizon radar signals using RANSAC and MUSIC algorithms,\u201d Progress In Electromagnetics Research M, vol. 67, pp. 85-92, April 2018.<\/p>\n

Zhou, Y. Gu, Y. D. Zhang, Z. Shi, T. Jin, and X. Wu, \u201cCompressive sensing based coprime array direction-of-arrival estimation,\u201d IET Communications<\/em>, vol. 11, no. 11, pp. 1719-1724, Aug. 2017. (2019 IET Communications Premium Award<\/span>)<\/p>\n

Wang, Y. D. Zhang, and W. Wang, \u201cRobust DOA estimation in the presence of miscalibrated sensors,\u201d IEEE Signal Processing Letters<\/em>, vol. 24, no. 7, pp. 1073-1077, July 2017.<\/p>\n

Xi, S. Chen, Y. D. Zhang, and Z. Liu, \u201cGridless quadrature compressive sampling with interpolated array technique,\u201d Signal Processing<\/em>, vol. 133, pp. 1-12, April 2017.<\/p>\n

Qin, Y. D. Zhang, M. G. Amin, and F. Gini, \u201cFrequency diverse coprime arrays with coprime frequency offsets for multi-target localization,\u201d IEEE Journal of Selected Topics in Signal Processing<\/em>, Special Issue on Advances in Time\/Frequency Modulated Array Signal Processing, vol. 11, no. 2, pp. 321-335, March 2017.<\/p>\n

Y. D. Zhang, M. G. Amin, and B. Himed, \u201cStructure-aware sparse reconstruction and applications to passive multi-static radar,\u201d IEEE Aerospace and Electronic Systems Magazine<\/em>, vol. 32, no. 2, pp. 68-78, Feb. 2017.<\/p>\n

S. Qin, Y. D. Zhang, and M. G. Amin, \u201cDOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar,\u201d Digital Signal Processing<\/em>, special issue on Coprime Array and Sampling, vol. 61, pp. 26-34, Feb. 2017..<\/p>\n

S. Qin, Y. D. Zhang, M. G. Amin, and A. Zoubir, \u201cGeneralized coprime sampling of Toeplitz matrix for spectrum estimation,\u201d IEEE Transactions on Signal Processing<\/em>, vol. 65, no. 1, pp. 81-94, Jan. 2017.<\/p>\n

Q. Shen, W. Liu, W. Cui, S. Wu, Y. D. Zhang, and M. G. Amin, \u201cFocused compressive sensing for underdetermined wideband DOA estimation exploiting high-order difference co-arrays,\u201d IEEE Signal Processing Letters<\/em>, vol. 24, no. 1, pp. 86-90, Jan. 2017.<\/p>\n

Q. Shen, W. Cui, W. Liu, S. Wu, Y. D. Zhang, and M. G. Amin, \u201cUnderdetermined wideband DOA estimation of off-grid sources employing the difference co-array concept,\u201d Signal Processing<\/em>, vol. 130, pp. 299-304, Jan. 2017.<\/p>\n

S. Qin, Y. D. Zhang, M. G. Amin, and B. Himed, \u201cDOA estimation exploiting a uniform linear array with multiple co-prime frequencies,\u201d Signal Processing<\/em>, vol. 130, pp. 37-46, Jan. 2017.<\/p>\n

S. Subedi, Y. D. Zhang, M. G. Amin, and B. Himed, \u201cGroup sparsity based multi-target tracking in passive multi-static radar systems using Doppler-only measurements,\u201d IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3619-3634, July 2016.<\/p>\n

S. Subedi, Y. D. Zhang, M. G. Amin, and B. Himed, \u201cGroup sparsity based multi-target tracking in passive multi-static radar systems using Doppler-only measurements,\u201d IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3619-3634, July 2016.<\/p>\n

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, \u201cDual-function radar-communications: Information embedding using sidelobe control and waveform diversity,\u201d IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 2168-2181, April 2016.<\/p>\n

Q. Wu, Y. D. Zhang, M. G. Amin, and B. Himed, \u201cSpace-time adaptive processing and motion parameter estimation in multi-static passive radar exploiting Bayesian compressive sensing,\u201d IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 944 – 957, Feb. 2016.<\/p>\n

Y. Tang, Y. D. Zhang, M. G. Amin, and W. Sheng, \u201cDesign of wideband MIMO radar waveforms with low peak-to-average ratio,\u201d IET Radar, Sonar, and Navigation, vol. 10, no. 2, pp. 325-332, Feb. 2016.<\/p>\n

S. Subedi, Y. D. Zhang, M. G. Amin, and B. Himed, \u201cGroup sparsity based multi-target tracking in multi-static passive radar systems using Doppler-only measurements,\u201d IEEE International Radar Conference, Arlington, VA, May 2015. (2nd Place of Best Student Paper Award<\/span>)<\/p>\n

C. Liu, F. Xi, S. Chen, Y. D. Zhang, and Z. Liu, “Pulse-Doppler signal processing with quadrature compressive sampling,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 2, pp. 1217-1230, April 2015.<\/p>\n

S. Qin, Y. D. Zhang, and M. G. Amin, “Generalized coprime array configurations for direction-of-arrival estimation,” IEEE Transactions on Signal Processing, vol. 63, no. 6, pp. 1377-1390, March 2015. (2018 IEEE Signal Processing Society Young Author Best Paper Award<\/span>)<\/p>\n

Q. Wu, Y. D. Zhang, W. Tao, and M. G. Amin, \u201cRadar-based fall detection based on Doppler time-frequency signatures for assisted living,\u201d IET Radar, Sonar & Navigation, special issue on Application of Radar to Remote Patient Monitoring and Eldercare, vol. 9, no. 2, pp. 164-172, Feb. 2015.<\/p>\n

S. Qin, Y. D. Zhang, and M. G. Amin, \u201cDOA estimation of mixed coherent and uncorrelated signals exploiting a nested MIMO system,\u201d IEEE Benjamin Franklin Symposium on Microwave and Antenna Sub-systems, Philadelphia, PA, Sept. 2014. (First Prize of Student Paper Competition<\/span>)<\/p>\n

Y. D. Zhang, J. J. Zhang, M. G. Amin, and B. Himed, \u201cInstantaneous altitude estimation of maneuvering targets in over-the-horizon radar exploiting multipath Doppler signatures,\u201d EURASIP Journal on Advances in Signal Processing, special issue on Emerging Radar Techniques, vol. 2013, no. 2013:100, May 2013 .<\/p>\n

Click here for Full list of papers on\u00a0Radar Signal Processing<\/a><\/p>\n

\n

Wireless Communications and Networking<\/strong><\/span><\/p>\n

We develop novel signal processing methods and algorithms for increased communication capacity and quality related to wireless communications, cooperative networking, energy harvesting system that are operated in challenging environments with rich multipath and jammers.<\/p>\n

Selected publications:<\/strong><\/p>\n

Y. Gu and Y. D. Zhang, \u201cCompressive sampling optimization for user signal parameter estimation in massive MIMO systems<\/a>,\u201d Digital Signal Processing, vol. 94, pp. 105-113, Oct. 2019.<\/p>\n

Y. Gu and Y. D. Zhang, \u201cInformation-theoretic pilot design for downlink channel estimation in FDD massive MIMO systems<\/a>,\u201d IEEE Transactions on Signal Processing, vol. 67, no. 9, pp. 2334 \u20132346, May 2019.<\/p>\n

Y. Gu, N. A. Goodman, and Y. D. Zhang, \u201cAdaptive beamforming via sparsity-based reconstruction of covariance matrix<\/a>,\u201d in A. De Maio, Y. C. Eldar, and A. Haimovich (eds.), Compressed Sensing in Radar Signal Processing, Cambridge University Press, 2019.<\/p>\n

A. Ahmed, Y. D. Zhang, and Y. Gu, \u201cDual-function radar-communications using QAM-based sidelobe modulation,\u201d Digital Signal Processing, vol. 82, pp.166-174, Nov. 2018.<\/p>\n

A. Hassanien, M. G. Amin,\u00a0Y. D. Zhang, and F. Ahmad, \u201cDual-function radar-communications: Information embedding using sidelobe control and waveform diversity<\/a>,\u201d\u00a0IEEE Transactions on Signal Processing,\u00a0vol. 64, no. 8, pp. 2168-2181, April 2016.<\/p>\n

Y. D. Zhang, M. G. Amin, and B. Wang, \u201cMitigation of sparsely sampled nonstationary jammers for multi-antenna GNSS receivers,\u201d IEEE International Conference on Acoustics, Speech, and Signal Processing, Shanghai, China, March 2016.<\/p>\n

M. G. Amin and Y. D. Zhang, \u201cNonstationary jammer excision for GPS receivers using sparse reconstruction techniques<\/a>,\u201d ION GNSS+, Tampa, FL, Sept. 2014. (Best Presentation Award<\/span>)<\/p>\n

B. Chalise, Y. D. Zhang, and M. G. Amin, \u201cLocal CSI based full diversity achieving relay selection for amplify-and-forward cooperative systems<\/a>,\u201d IEEE Transactions on Signal Processing, vol. 61, no. 21, pp. 5165-5180, Nov. 2013.<\/p>\n

B. Chalise, W.-K. Ma, Y. D. Zhang, H. Suraweera, and M. G. Amin, \u201cOptimum performance boundaries of OSTBC based AF-MIMO relay system with energy harvesting receiver<\/a>,\u201d IEEE Transactions on Signal Processing, vol. 61, no. 17, pp. 4199-4213, Sept. 2013.<\/p>\n

Y. D. Zhang and M. G. Amin, \u201cAnti-jamming GPS receiver with reduced phase distortions<\/a>,\u201d IEEE Signal Processing Letters, vol. 19, no. 10, pp. 635-638, Oct. 2012.<\/p>\n

B. K. Chalise, Y. D. Zhang, and M. G. Amin, \u201cPrecoder design for OSTBC based AF MIMO relay system with channel uncertainty<\/a>,\u201d IEEE Signal Processing Letters, vol. 19, no. 8, pp. 515-518, Aug. 2012.<\/p>\n

Click here for\u00a0Full list of papers on Wireless Communications and Networking<\/a><\/p>\n

\n

Spectrum Sharing and Coexistence of Wireless Systems<\/strong><\/span><\/p>\n

We have developed a number of novel techniques to provide\u00a0significant improvements in the efficiency of radio spectrum utilization and protection of passive sensing services. \u00a0These techniques support\u00a0spectrum sharing and coexistence of wireless systems for wireless communications, broadcast, radar, and radio astronomy.<\/p>\n

Selected publications:<\/strong><\/p>\n

A. Ahmed, S. Zhang, and Y. D. Zhang, \u201cOptimized sensor selection for joint radar-communication systems<\/a>,\u201d IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, May 2020.<\/p>\n

A. Ahmed, Y. D. Zhang, A. Hassanien, and B. Himed, \u201cOFDM-based joint radar-communication system: Optimal sub-carrier allocation and power distribution by exploiting mutual information<\/a>,\u201d Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2019.<\/p>\n

A. Ahmed, Y. D. Zhang, and B. Himed, \u201cDistributed dual-function radar-communication MIMO system with optimized resource allocation<\/a>,\u201d IEEE Radar Conference, Boston, MA, April 2019.<\/p>\n

A. Ahmed, Y. D. Zhang, and Y. Gu, \u201cDual-function radar-communications using QAM-based sidelobe modulation<\/a>,\u201d Digital Signal Processing, vol. 82, pp.166-174, Nov. 2018.<\/p>\n

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, \u201cPhase-modulation based dual-function radar-communications<\/a>,\u201d IET Radar, Sonar, and Navigation,\u00a0vol. 10, no. 8, pp. 1411-1421, Oct. 2016.<\/p>\n

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, \u201cSignaling strategies for dual-function radar-communications: An overview,\u201d IEEE Aerospace and Electronic Systems Magazine, vol. 31, no. 10, pp. 36-45, Oct. 2016. (2018 IEEE Aerospace and Electronic Systems Society Harry Rowe Mimno Award<\/span>)<\/p>\n

A. Hassanien, M. G. Amin, Y. D. Zhang, and B. Himed, \u201cA dual-function MIMO radar-communications system using PSK modulation<\/a>,\u201d European Signal Processing Conference, Budapest, Hungary, Sept. 2016.<\/p>\n

A. Hassanien, M. G. Amin, Y. D. Zhang, F. Ahmad, and B. Himed, \u201cNon-coherent PSK-based dual-function radar-communication systems<\/a>,\u201d IEEE Radar Conference, Philadelphia, PA, May 2016.<\/p>\n

A. Hassanien, M. G. Amin,\u00a0Y. D. Zhang, and F. Ahmad, \u201cDual-function radar-communications: Information embedding using sidelobe control and waveform diversity<\/a>,\u201d\u00a0IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 2168-2181, April 2016.<\/p>\n

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, \u201cEfficient sidelobe ASK based Dual-Function radar-communications<\/a>,\u201d SPIE Radar Sensor Technology Conference, Baltimore, MD, April 2016.<\/p>\n

A. Hassanien, M. G. Amin, and Y. D. Zhang, \u201cComputationally efficient beampattern synthesis for dual-function radar-communications<\/a>,\u201d SPIE Radar Sensor Technology Conference, Baltimore, MD, April 2016.<\/p>\n

\n

Machine Learning
\n<\/strong><\/span><\/p>\n

We have studied various machine learning and deep learning techniques with applications to radar target classification, elderly fall detection, and road crack detection.<\/p>\n

Selected publications:<\/strong><\/p>\n

S. Zhang, A. Ahmed, and Y. D. Zhang, \u201cSparsity-based time-frequency analysis for automatic radar waveform recognition<\/a>,\u201d IEEE International Radar Conference, Rockville, MD, April-May 2020.<\/p>\n

M. Wang, Y. D. Zhang, and G. Cui, \u201cHuman motion recognition exploiting radar with stacked recurrent neural network<\/a>,\u201d Digital Signal Processing, vol. 87, pp. 125-131, April 2019.<\/p>\n

M. Wu, X. Dai, Y. D. Zhang, B. Davidson, J. Zhang, and M. G. Amin, \u201cFall detection based on sequential modeling of radar signal time-frequency features,\u201d IEEE International Conference on Healthcare Informatics, Philadelphia, PA, Sept. 2013.<\/p>\n

Q. Wu, Y. D. Zhang, W. Tao, and M. G. Amin, \u201cRadar-based fall detection based on Doppler time-frequency signatures for assisted living,\u201d IET Radar, Sonar & Navigation, vol. 9, no. 2, pp. 164-172, Feb. 2015.<\/p>\n

L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, \u201cRoad crack detection with deep convolution neural network,\u201d Proceedings of IEEE International Conference on Image Processing, Phoenix, AZ, Sept. 2016.<\/p>\n

\n

Compressive Sensing and Sparse Signal Reconstruction<\/strong><\/span><\/p>\n

We have developed a number of novel compressive sensing and sparse reconstruction algorithms that consider the group sparsity and signal structures that find significant importance in many radar, sonar, structure health monitoring applications, and time-frequency analysis.<\/p>\n

Selected publications:<\/strong><\/p>\n

Y. Gu and Y. D. Zhang, \u201cCompressive sampling optimization for user signal parameter estimation in massive MIMO systems,\u201d Digital Signal Processing, vol. 94, pp. 105-113, Oct. 2019.<\/p>\n

M. Guo, Y. D. Zhang, and T. Chen, \u201cDOA estimation using compressed sparse array,\u201d IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 4133-4146, Aug. 2018.<\/p>\n

S. Liu, Y. D. Zhang, T. Shan, and R. Tao, “Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations,” IEEE Transactions on Signal Processing, vol. 66, no. 8, pp. 2153-2166, April 2018.<\/p>\n

S. Subedi, Y. D. Zhang, M. G. Amin, and B. Himed, \u201cGroup sparsity based multi-target tracking in passive multi-static radar systems using Doppler-only measurements,\u201d IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3619-3634, July 2016.<\/p>\n

M. G. Amin, X. Wang, Y. D. Zhang, F. Ahmad, and E. Aboutanios, \u201cSparse array and sampling for interference mitigation and DOA estimation in GNSS,\u201d Proceedings of IEEE, special issue on Vulnerabilities, Threats, and Authentication in Satellite-based Navigation Systems, vol. 104, no. 6, pp. 1302-1317, June 2016.<\/p>\n

Q. Wu, Y. D. Zhang, M. G. Amin, and B. Himed, “Structured Bayesian compressive sensing exploiting spatial location dependence,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Brisbane, Australia, April 2015.<\/p>\n

C. Liu, F. Xi, S. Chen, Y. D. Zhang, and Z. Liu, “Pulse-Doppler signal processing with quadrature compressive sampling,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 2, pp. 1217-1230, April 2015.<\/p>\n

Q. Wu, Y. D. Zhang, F. Ahmad, and M. G. Amin, “Compressive sensing based high-resolution polarimetric through-the-wall radar imaging exploiting target characteristics,” IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 1043-1047, May 2015.<\/p>\n

G. Li, P. K. Varshney, and Y. D. Zhang, \u201cMultistatic radar imaging via decentralized and collaborative subspace pursuit,\u201d International Conference on Digital Signal Processing, Hong Kong, China. Aug. 2014.<\/p>\n

B. Jokanovic, M. G. Amin, Y. D. Zhang, and F. Ahmad, \u201cTime-frequency kernel design for sparse joint-variable signal representations,\u201d European Signal Processing Conference, Lisbon, Portugal, September 2014.<\/p>\n

Q. Wu, Y. D. Zhang, M. G. Amin, A. Golato, F. Ahmad, and S. Santhanam, “Structural health monitoring exploiting MIMO ultrasonic sensing and group sparse Bayesian learning,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2014.<\/p>\n

Click here for Full list of papers on\u00a0Compressive Sensing and Sparse Reconstruction<\/a><\/p>\n

\n

Time-frequency Analysis<\/strong><\/span><\/p>\n

We have developed time-frequency representations for the analysis and exploitation of\u00a0time-varying nonstationary signals in single- and multi-sensor platforms. \u00a0We have used spatial time-frequency distributions for high-resolution direction-of-arrival (DOA) estimation, source separation, interference suppression. Sparse reconstruction methods have been developed to handle nonstationary signals and jammers with missing samples.<\/p>\n

Selected publications:<\/strong><\/p>\n

S. Zhang and Y. D. Zhang, “Robust time-frequency analysis of multiple FM signals with burst missing samples<\/a>,” IEEE Signal Processing Letters, vol. 26, no. 8, pp. 1172-1176, Aug. 2019.<\/p>\n

V. S. Amin, Y. D. Zhang, and B. Himed, \u201cSparsity-based time-frequency representation of FM signals with burst missing samples,\u201d Signal Processing, vol. 155, pp. 25-43, Feb. 2019.<\/p>\n

S. Liu, Y. D. Zhang, T. Shan, and R. Tao, “Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations,” IEEE Transactions on Signal Processing, vol. 66, no. 8, pp. 2153-2166, April 2018.<\/p>\n

M. G. Amin, D. Borio, Y. D. Zhang, and L. Galleani, \u201cTime-frequency analysis for GNSS: From interference mitigation to system monitoring,\u201d IEEE Signal Processing Magazine<\/em>, vol. 34, no. 5, pp. 85-95, Sept. 2017.<\/p>\n

Y. D. Zhang, \u201cResilient quadratic time-frequency distribution for FM signals with gapped missing data,\u201d IEEE Radar Conference, Seattle, WA, May 2017.<\/p>\n

M. G. Amin, B. Jakonovic, Y. D. Zhang, and F. Ahmad, \u201cA sparsity-perspective to quadratic time-frequency distributions,\u201d Digital Signal Processing,\u00a0vol. 46, pp. 175-190, Nov. 2015.<\/p>\n

Y. D. Zhang, L. Guo, Q. Wu, and M. G. Amin, \u201cMulti-sensor kernel design for time-frequency analysis of sparsely sampled non-stationary signals,\u201d IEEE International Radar Conference, Arlington, VA, May 2015.<\/p>\n

L. Stankovic, S. Stankovic, I. Orovic, and Y. D. Zhang, \u201cTime-frequency analysis of micro-Doppler signals based on compressive sensing,\u201d in M. Amin (ed.), Compressive Sensing for Urban Radars, CRC Press, 2014.<\/p>\n

S. Liu, T. Shan, R. Tao, Y. D. Zhang, G. Zhang, F. Zhang, and Y. Wang, \u201cSparse discrete fractional Fourier transform and its applications,\u201d IEEE Transactions on Signal Processing, vol. 62, no. 24, pp. 6582-6595, Dec. 2014.<\/p>\n

A. Belouchrani, M. G. Amin, N. Thirion-Moreau, and Y. D. Zhang, \u201cSource separation and localization using time-frequency distributions,\u201d IEEE Signal Processing Magazine, special issue on Time-Frequency Analysis and Applications, vol. 30, no. 6, pp. 97-107, Nov. 2013.<\/p>\n

Y. Zhang, W. Mu, and M. G. Amin, “Subspace analysis of spatial time-frequency distribution matrices,” IEEE Transactions on Signal Processing, vol. 49, no. 4, April 2001.<\/p>\n

Click here for\u00a0Full list of papers on Time-Frequency Analysis<\/a><\/p>\n

\n

Elderly Assisted Living<\/strong><\/span><\/p>\n

Assisted living is an emerging area that addresses the challenges of self-dependence living within homes or residences for the elderly population. Among these challenges, elderly falls are a major public health concern as they often result in disability and the main cause of accidental death in the U.S. senior citizens. Because immediate assistance provided after a fall can significantly reduce complications of fall risks, it is critical to detect elderly falls in a timely and accurate manner so that immediate response and proper care can be rendered. Among different available methods for this purpose, radar is an excellent sensing modality because it offers non-intrusive, clutter suppressed and noise tolerant sensing capabilities for moving human objects. Radar systems avoid direct contact (unlike wearable devices or accelerometers) and, in contrast to optical systems, can operate in all types of environments, can penetrate walls and fabrics, preserve privacy, and are insensitive to lighting conditions. We perform experimental studies and analyses for motion classification and fall detection using different radar systems.<\/p>\n

Selected publications:<\/strong><\/p>\n

M. G. Amin, Y. D. Zhang, F. Ahmad, and K. C. Ho, \u201cRadar signal processing for elderly fall detection,\u201d IEEE Signal Processing Magazine, special issue on Signal Processing for Assisted Living, vol. 33, no. 2, pp. 71-80, March 2016.<\/p>\n

Q. Wu, Y. D. Zhang, W. Tao, and M. G. Amin, \u201cRadar-based fall detection based on Doppler time-frequency signatures for assisted living,\u201d IET Radar, Sonar & Navigation, special issue on Application of Radar to Remote Patient Monitoring and Eldercare, vol. 9, no. 2, pp. 164-172, Feb. 2015.<\/p>\n

B. Jokanovic, M. G. Amin, Y. D. Zhang, and F. Ahmad, \u201cMulti-window time-frequency signature reconstruction from undersampled continuous wave radar measurements for fall detection,\u201d IET Radar, Sonar & Navigation, special issue on Application of Radar to Remote Patient Monitoring and Eldercare, vol. 9, no. 2, pp. 173-183, Feb. 2015.<\/p>\n

L. Ramirez Rivera, E. Ulmer, Y. D. Zhang, W. Tao, and M. G. Amin, \u201cRadar-based fall detection exploiting time-frequency features,\u201d IEEE China Summit and International Conference on Signal and Information Processing, Xi\u2019an, China, July 2014.<\/p>\n

M. Wu, X. Dai, Y. D. Zhang, B. Davidson, J. Zhang, and M. G. Amin, \u201cFall detection based on sequential modeling of radar signal time-frequency features,\u201d IEEE International Conference on Healthcare Informatics, Philadelphia, PA, Sept. 2013.<\/p>\n

\n

Radio Frequency Identification (RFID)<\/strong><\/span><\/p>\n

Radio-frequency identification (RFID) uses wireless signals to transfer data for the purposes of automatic identification and and tracking tags attached to objects. We develop technologies for RFID reader and tag localization, propagation characterization, and\u00a0collision avoidance algorithms.<\/p>\n

Selected publications:<\/strong><\/p>\n

S. Subedi, E. Pauls, and\u00a0Y. D. Zhang, \u201cAccurate localization and tracking of a passive RFID reader based on RSSI measurements,\u201d IEEE Journal of Radio Frequency Identification, vol. 1, no. 2, pp. 144-154, June 2017.<\/p>\n

E. Pauls and Y. D. Zhang, “Experimental studies of high-accuracy RFID localization with channel impairments,” SPIE Mobile Multimedia\/Image Processing, Security, and Applications, Baltimore, MD, April 2015.<\/p>\n

S. Subedi, Y. D. Zhang, and M. G. Amin, \u201cPrecise RFID localization in impaired environment through sparse signal recovery,\u201d SPIE Wireless Sensing, Localization, and Processing Conference, Baltimore, MD, April-May 2013.<\/p>\n

Y. Zhang, X. Li, and M. G. Amin, \u201cPrinciples and techniques of RFID positioning,\u201d in M. Bolic, D. Simplot-Ryl, and I. Stojmenovic (eds.), RFID Systems, Research Trends and Challenges, John Wiley, 2010.<\/p>\n

Y. Zhang, K. Yemelyanov, X. Li, and M. G. Amin, \u201cEffect of metallic objects and liquid supplies on RFID links,\u201d IEEE AP-S International Symposium on Antennas and Propagation and USNC\/URSI National Radio Science Meeting, Charleston, SC, June 2009.<\/p>\n

X. Li, Y. Zhang, and M. G. Amin, “Multifrequency-based range estimation of RFID tags,” IEEE International Conference on RFID, Orlando, FL, April 2009.<\/p>\n

Y. Zhang, M. G. Amin, and S. Kaushik, \u201cLocalization and tracking of passive RFID tags based on direction estimation,\u201d International Journal of Antennas and Propagation, vol. 2007, Article ID 17426, doi:10.1155\/2007\/17426, 9 pages, Dec. 2007.<\/p>\n

Click here for\u00a0Full list of papers on Radio Frequency Identification (RFID)<\/a><\/p>\n

\n

Ultrasound Signal Processing for Nondesctructive Testing\u00a0and Structure Health Monitoring<\/strong><\/span><\/p>\n

Mechanical and aerospace structures can develop debilitating defects, such as cracks, during their service lifetime. Nondestructive testing and structural health monitoring represent a collection of strategies and techniques for the timely detection of flaws.\u00a0Our research focus lies in the development multi-input multi-output (MIMO) based processing and sparse reconstruction algorithms for high-resolution flaw imaging that enables effective detection and classifications.<\/p>\n

Selected publications:<\/strong><\/p>\n

Q. Wu, Y. D. Zhang, M. G. Amin, A. Golato, F. Ahmad, and S. Santhanam, “Structural health monitoring exploiting MIMO ultrasonic sensing and group sparse Bayesian learning,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2014.<\/p>\n

R. Demirli, M. G. Amin, X. Shen, and Y. D. Zhang, \u201cUltrasonic flaw detection and imaging through reverberant layer via subspace analysis and projection,\u201d Advances in Acoustics and Vibration, special issue on Advances in Acoustic Sensing, Imaging and Signal Processing, vol. 2012, doi:10.1155\/2012\/957379, June 2012.<\/p>\n

Y. D. Zhang, X. Shen, R. Demirli and M. G. Amin, \u201cUltrasonic flaw imaging via multipath exploitation,\u201d Advances in Acoustics and Vibration, special issue on Advances in Acoustic Sensing, Imaging and Signal Processing, vol. 2012, doi:10.1155\/2012\/874081, April 2012.<\/p>\n

R. Demerli, X. Rivenq, Y. D. Zhang, C. Ioana, and M. G. Amin, \u201cMIMO imaging for ultrasonic nondestructive testing,\u201d SPIE Conference on Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security V, San Diego, CA, March 2011.<\/p>\n

Click here for\u00a0Full list of papers on Ultrasound Signal Processing<\/a><\/p>\n

\n

Image Processing & Watermarking<\/strong><\/span><\/p>\n

We perform image segmentation, digital watermarking, and image-based time-frequency analysis for nonstationary signal classification.<\/p>\n

Selected publications:<\/strong><\/p>\n

Q. Wu, Y. D. Zhang, W. Tao, and M. G. Amin, \u201cRadar-based fall detection based on Doppler time-frequency signatures for assisted living,\u201d IET Radar, Sonar & Navigation, special issue on Application of Radar to Remote Patient Monitoring and Eldercare, vol. 9, no. 2, pp. 164-172, Feb. 2015.<\/p>\n

Y. Zhang, B. Mobasseri, B. M. Dogahe, and M. G. Amin, \u201cImage-adaptive watermarking using 2-D chirps,\u201d Signal Image and Video Processing, vol. 4, no. 1, pp. 105-121, March 2010.<\/p>\n

W. Tao, H. Jin, Y. Zhang, L. Liu, and D. Wang, \u201cImage thresholding using graph cuts,\u201d IEEE Transactions on Systems, Man and Cybernetics, Part A, vol. 38, no. 5, pp. 1181-1195, Sept. 2008.<\/p>\n

W. Tao, H. Jin, and Y. Zhang, “Color image segmentation based on mean shift and graph cuts,” IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 37, no. 5, pp. 1382-1389, Oct. 2007.<\/p>\n

Click here for\u00a0Full list of papers on Image Processing and Watrmarking<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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