TY - GEN
T1 - Compressive sensing of up-sampled model and atomic norm for super-resolution radar
AU - Yang, Dongshin
AU - Jitsumatsu, Yutaka
PY - 2017/8/10
Y1 - 2017/8/10
N2 - Compressive sensing (CS) for radar signal processing is known to be capable of various applications. This signal processing technique shows excellent performance for detecting objects. However, the grid problem of CS is an obstacle to more precise performance. In this paper, we introduce two methods to overcome this grid problem and evaluate the performance of the methods. The first method is an up-sampled model, which is a method of dividing the grids into smaller pieces. The second method is an atomic norm minimization, which is a detectable method for continuous parameters.
AB - Compressive sensing (CS) for radar signal processing is known to be capable of various applications. This signal processing technique shows excellent performance for detecting objects. However, the grid problem of CS is an obstacle to more precise performance. In this paper, we introduce two methods to overcome this grid problem and evaluate the performance of the methods. The first method is an up-sampled model, which is a method of dividing the grids into smaller pieces. The second method is an atomic norm minimization, which is a detectable method for continuous parameters.
UR - http://www.scopus.com/inward/record.url?scp=85028624773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028624773&partnerID=8YFLogxK
U2 - 10.23919/IRS.2017.8008110
DO - 10.23919/IRS.2017.8008110
M3 - Conference contribution
AN - SCOPUS:85028624773
T3 - Proceedings International Radar Symposium
BT - 2017 18th International Radar Symposium, IRS 2017
A2 - Rohling, Hermann
PB - IEEE Computer Society
T2 - 18th International Radar Symposium, IRS 2017
Y2 - 28 June 2017 through 30 June 2017
ER -