Reverberation suppression technology based on low-rank sparse matrix factorization and a localization window filtering
In the context of strong reverberation,traditional methods such as pre-whitening,time-frequency analysis and subspace analysis are not effective in detecting moving targets.To address this issue,this paper uses the newly intro-duced low-rank sparse matrix decomposition theory in recent years to improve the detection ability of moving targets in the context of robbery.A robust PCA processing algorithm combining multiple frames of data is adopted.By combining the acoustic characteristic of reverberation data,the acoustic detection problem is transformed into an image decomposition problem,and the performance comparison of the PCA algorithm is given by comparing the processing results.At the same time,this article proposes a localization window filtering method that combines the feature differences of target motion con-tinuity and sparse clutter randomness,further filtering out sparse clutter,purifying active sonar display images,and improv-ing the performance of active sonar moving target detection.The simulation and experimental data processing results show that under the signal-to-noise ratio of-5 dB at the array end,the algorithm can still accurately locate the target,filter out sparse clutter,and achieve better results in the time-frequency domain,significantly improving the active sonar moving tar-get detection ability.
万方数据
维普
