| Paper ID | CI-2.6 |
| Paper Title |
SYNTHETIC APERTURE ACOUSTIC IMAGING WITH DEEP GENERATIVE MODEL BASED SOURCE DISTRIBUTION PRIOR |
| Authors |
Boqiang Fan, Rice University, United States; Samarjit Das, Bosch Research Pittsburgh, United States |
| Session | CI-2: Computational Imaging for Inverse Problems |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Computational Imaging: [CIS] Computational Imaging Systems |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Acoustic imaging has a wide range of real-world applications such as machine health monitoring. Conventionally, large microphone arrays are utilized to achieve useful spatial resolution in the imaging process. The advent of location-aware autonomous mobile robotic platforms opens up unique opportunity to apply synthetic aperture techniques to the acoustic imaging problem. By leveraging motion and location cues as well as some available prior information on the source distribution, a small moving microphone array has the potential to achieve imaging resolution far beyond the physical aperture limits. In this work, we propose to image large acoustic sources with a combination of synthetic aperture and their geometric structures modeled by a conditional generative adversarial network (cGAN). The acoustic imaging problem is formulated as a linear inverse problem and solved with the gradient-based method. Numerical simulations show that our synthetic aperture imaging framework can reconstruct the acoustic source distribution from microphone recordings and outperform static microphone arrays. |
