Moving Target Detection Using Dynamic Mode Decomposition.

Area of Science:

• Signal Processing
• Underwater Acoustics
• Target Detection

Background:

• Detecting moving targets in reverberant environments presents significant challenges.
• Existing methods often rely on low-rank and sparse theory for matrix decomposition of multiframe data.
• These traditional approaches can be computationally intensive, limiting real-time applications.

Purpose of the Study:

• To introduce a novel matrix decomposition method for enhanced moving target detection.
• To leverage dynamic mode decomposition (DMD) for efficient low-rank and sparse decomposition.
• To improve computational speed for real-time processing in reverberant acoustic environments.

Main Methods:

• Arranging multiframe data containing target echo and reverberation into a matrix.
• Applying dynamic mode decomposition (DMD) for matrix decomposition.
• Categorizing DMD eigenmodes to achieve low-rank and sparse decomposition, isolating the target from the sparse component.

Main Results:

• The proposed DMD-based method achieves a significant improvement in computation speed (4-90 times faster) compared to previous low-rank and sparse methods.
• A slight reduction in detection gain was observed, which is acceptable for many real-time applications.
• The method was successfully validated using three sets of underwater acoustic data.

Conclusions:

• Dynamic mode decomposition offers an efficient alternative for low-rank and sparse matrix decomposition in target detection.
• The improved computational speed enables real-time processing, allowing more time for other detection stages.
• This approach shows promise for robust moving target detection in challenging underwater acoustic environments.