TY - GEN
T1 - The Effects of Noise, Sparsity and Phase on Pseudo-Random Time-Space Modulation SAR Performance
AU - Liu, Ying
AU - Yu, Ze
AU - Chen, Wenjiao
AU - Yu, Jindong
AU - Geng, Jiwen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - SAR based on compressed sensing (CS) greatly reduces the amount of data. The pseudo-random space-time modulation technology could alleviate the constraint on the type of the observed scene. This paper provides a survey on the effects of noise, sparsity, and phase on the modulation technology performance. Selection of a suitable algorithm is necessary to achieve this goal. l1-norm algorithm performs the best of the three algorithms, including greedy algorithm, and Bayesian algorithm. The experiment results show that the performance of the pseudo-random space-time modulation SAR is improved. With the increase of the noise and the decrease of the sparsity, the performance improvement with modulation is more and more limited. With the decrease of phase density, the performance obtained by modulation decreases continuously. When the amplitude variation range exceeds [0,π], the improvements are similar.
AB - SAR based on compressed sensing (CS) greatly reduces the amount of data. The pseudo-random space-time modulation technology could alleviate the constraint on the type of the observed scene. This paper provides a survey on the effects of noise, sparsity, and phase on the modulation technology performance. Selection of a suitable algorithm is necessary to achieve this goal. l1-norm algorithm performs the best of the three algorithms, including greedy algorithm, and Bayesian algorithm. The experiment results show that the performance of the pseudo-random space-time modulation SAR is improved. With the increase of the noise and the decrease of the sparsity, the performance improvement with modulation is more and more limited. With the decrease of phase density, the performance obtained by modulation decreases continuously. When the amplitude variation range exceeds [0,π], the improvements are similar.
KW - noise
KW - phase
KW - pseudo-random modulation
KW - reconstruction algorithm
KW - sparsity
UR - https://www.scopus.com/pages/publications/85101995638
U2 - 10.1109/IGARSS39084.2020.9323928
DO - 10.1109/IGARSS39084.2020.9323928
M3 - 会议稿件
AN - SCOPUS:85101995638
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1169
EP - 1172
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -