TY - GEN
T1 - Online blind deconvolution for sequential through-the-wall-radar-imaging
AU - Mansour, Hassan
AU - Kamilov, Ulugbek
AU - Liu, Dehong
AU - Orlik, Philip
AU - Boufounos, Petros
AU - Parsons, Kieran
AU - Vetro, Anthony
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/15
Y1 - 2016/11/15
N2 - We propose an online blind deconvolution approach to sequential through-the-wall-radar-imaging (TWI) where the received signal is contaminated by front wall ringing artifacts. The sequential measurements correspond to individual transmitter-receiver pairs where the front wall ringing induces a multipath kernel that corrupts the received target reflections. The convolution kernels may vary across sequential measurements but are assumed to be shared among targets viewed by a single measurement. Our approach extends recent convex programming formulations for blind deconvolution to the sequential measurement scenario by formulating it as a low-rank tensor recovery problem. We develop a stochastic gradient descent algorithm that is capable of recovering the sparse scene and separating out the delay convolution kernels. We demonstrate the recovery capabilities of our approach on a synthetic scene as well as with real TWI radar measurements.
AB - We propose an online blind deconvolution approach to sequential through-the-wall-radar-imaging (TWI) where the received signal is contaminated by front wall ringing artifacts. The sequential measurements correspond to individual transmitter-receiver pairs where the front wall ringing induces a multipath kernel that corrupts the received target reflections. The convolution kernels may vary across sequential measurements but are assumed to be shared among targets viewed by a single measurement. Our approach extends recent convex programming formulations for blind deconvolution to the sequential measurement scenario by formulating it as a low-rank tensor recovery problem. We develop a stochastic gradient descent algorithm that is capable of recovering the sparse scene and separating out the delay convolution kernels. We demonstrate the recovery capabilities of our approach on a synthetic scene as well as with real TWI radar measurements.
UR - https://www.scopus.com/pages/publications/85002637466
U2 - 10.1109/CoSeRa.2016.7745700
DO - 10.1109/CoSeRa.2016.7745700
M3 - Conference contribution
AN - SCOPUS:85002637466
T3 - 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016
SP - 61
EP - 65
BT - 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016
Y2 - 19 September 2016 through 23 September 2016
ER -