TY - GEN
T1 - Multi-ping Reverberation Suppression Combined with Spatial Continuity of Target Motion
AU - Nie, Ruixin
AU - Liu, Xionghou
AU - Sun, Chao
AU - Zhou, Yifan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/14
Y1 - 2021/7/14
N2 - Reverberation suppression is necessarily required for detecting the underwater low-speed small targets (e.g., frogmen and UUVs). In recent years, the promising reverberation suppression method is to use the correlation characteristics between multi-ping echoes to implement the low-rank sparse matrix decomposition. However, almost no prior knowledge about the moving continuity characteristic of the target has been considered in existing methods, which leads to performance degradation in complicated underwater scenarios. To solve the problem, we introduce the detecting contiguous outliers in the low-rank representation (DECOLOR) to improve the calculation accuracy of the low-rank and sparse matrix decomposition. In DECOLOR, a priori knowledge that the slow small target is moving contiguously with relatively a small size (i.e., a highlight point moving continuously across a group of sonar images), is adopted. And the locations of target highlight point in the group of sonar images are modeled by the first-order Markov Random Fields (MRFs). By doing so, the low-rank and sparse matrix decomposition using the DECOLOR shows a higher accuracy for matrix decomposition. Thus, the proposed method shows a better reverberation suppression ability than existing methods. The proposed methods are tested and evaluated through a series of simulation experiments.
AB - Reverberation suppression is necessarily required for detecting the underwater low-speed small targets (e.g., frogmen and UUVs). In recent years, the promising reverberation suppression method is to use the correlation characteristics between multi-ping echoes to implement the low-rank sparse matrix decomposition. However, almost no prior knowledge about the moving continuity characteristic of the target has been considered in existing methods, which leads to performance degradation in complicated underwater scenarios. To solve the problem, we introduce the detecting contiguous outliers in the low-rank representation (DECOLOR) to improve the calculation accuracy of the low-rank and sparse matrix decomposition. In DECOLOR, a priori knowledge that the slow small target is moving contiguously with relatively a small size (i.e., a highlight point moving continuously across a group of sonar images), is adopted. And the locations of target highlight point in the group of sonar images are modeled by the first-order Markov Random Fields (MRFs). By doing so, the low-rank and sparse matrix decomposition using the DECOLOR shows a higher accuracy for matrix decomposition. Thus, the proposed method shows a better reverberation suppression ability than existing methods. The proposed methods are tested and evaluated through a series of simulation experiments.
KW - detecting contiguous outliers in the low-rank representation (DECOLOR)
KW - integral sidelobe ratio (ISLR)
KW - low-rank and sparse decomposition (LRSD)
KW - reverberation suppression
KW - small target detection
KW - sonar image
UR - http://www.scopus.com/inward/record.url?scp=85115424959&partnerID=8YFLogxK
U2 - 10.1109/COA50123.2021.9520050
DO - 10.1109/COA50123.2021.9520050
M3 - 会议稿件
AN - SCOPUS:85115424959
T3 - 2021 OES China Ocean Acoustics, COA 2021
SP - 689
EP - 693
BT - 2021 OES China Ocean Acoustics, COA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 OES China Ocean Acoustics, COA 2021
Y2 - 14 July 2021 through 17 July 2021
ER -