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1- Faculty of Engineering, Ferdowsi University of Mashhad
Abstract:   (1498 Views)
Synthetic aperture sonar (SAS) achieves high-resolution imaging by coherently integrating echoes along the platform trajectory, making it inherently sensitive to six-degree-of-freedom (6-DOF) motion. Sub-wavelength perturbations distort phase, broaden the main lobe, and raise sidelobes. This paper introduces a geometry-centric array optimization framework that: (i) operationalizes a Phase Sensitivity Index (PSI) with translational and rotational components to quantify array-dependent motion sensitivity; and (ii) calibrates PSLR/ISLR to image signatures (main-lobe width and sidelobe proximity) against a back-projection (BP) reference, ensuring physics-consistent visual interpretation. Three receiver arrays—2D planar, 3D cubic, and 3D hemispherical (36 elements each)—are evaluated under six motions (surge, sway, heave, roll, pitch, yaw) and three imaging states (ideal, motion-degraded, corrected), yielding 54 scenarios. Motion parameters reflect realistic autonomous underwater vehicle conditions: translational RMS 2.0–3.2 mm, rotational RMS 0.25°–0.35°, platform speed 2.5 m/s, and dominant 0.2–2 Hz content. The hemispherical array attains the lowest normalized PSI and consistently superior sidelobe metrics, outperforming the planar array by approximately 6.5 dB in PSLR and 5.6 dB in ISLR on updated corrected global means, and the cubic array by approximately 2.9 dB in both PSLR and ISLR. Yaw and Heave cause the greatest degradation; Roll is least harmful. Motion correction improves PSLR by ≥4 dB and ISLR by ≥3 dB relative to degraded images, while the residual gap to ideal remains ≤4 dB, by design. The proposed framework enables quantitative array selection and supports robust SAS imaging in embedded AUVs without external navigation sensors.
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Article Highlights
A geometry-centric framework is proposed to optimize hydrophone array configurations for robust SAS imaging under 6-DoF motion.
A normalized Phase Sensitivity Index (PSI) is introduced, with translational and rotational components, to quantify array-dependent motion sensitivity.
PSI is calibrated against BP-based PSLR and ISLR, linking phase-error sensitivity directly to interpretable image-quality degradation.
Across 54 scenarios, the 3D hemispherical array consistently outperforms cubic and planar arrays, achieving the best corrected sidelobe performance.
First-order sensitivity analysis shows that mutual coupling, tolerances, and multipath mainly shift absolute metrics but do not change the hemispherical array’s overall superiority.

 
Type of Study: Research Paper | Subject: Environmental Study
Received: 2025/08/12 | Accepted: 2026/04/18

References
1. Zeng, S., Fan, W. and Du, X., (2022), Three-Dimensional Imaging of Circular-Array Synthetic Aperture Sonar for Unmanned Surface Vehicle, Sensors, Vol.22(10), p.3797. DOI: 10.3390/s22103797. [DOI:10.3390/s22103797] [PMID] []
2. Gill, J. and Rama Rao, V.V.S., (2014), Motion Compensation of Airborne Synthetic Aperture Radar, IFAC Proceedings Volumes (Proc. 19th IFAC World Congress). DOI: 10.3182/20140313-3-IN-3024.00065. [DOI:10.3182/20140313-3-IN-3024.00065]
3. Zhang, J., Cheng, G., Tang, J., Wu, H. and Tian, Z., (2023), A Subaperture Motion Compensation Algorithm for Wide-Beam, Multiple-Receiver SAS Systems, Journal of Marine Science and Engineering, Vol.11(8), p.1627. DOI: 10.3390/jmse11081627. [DOI:10.3390/jmse11081627]
4. Zhang, X., Huang, P., Sun, H., Ying, W. and Yang, P., (2022), Wide-Bandwidth Signal-Based Multireceiver SAS Imagery Using Extended Chirp Scaling Algorithm, IET Radar, Sonar & Navigation, Vol.16(3), p.531-541. DOI: 10.1049/rsn2.12200. [DOI:10.1049/rsn2.12200]
5. Zhang, X., Yang, P. and Zhou, M., (2023), Multireceiver SAS Imagery with Generalized PCA, IEEE Geoscience and Remote Sensing Letters, Vol.20, p.1-5 (Art. 1502205). DOI: 10.1109/LGRS.2023.3286180. [DOI:10.1109/LGRS.2023.3286180]
6. Baron, V., Finez, A., Bouley, S., Fayet, F., Mars, J.I. and Nicolas, B., (2021), Hydrophone Array Optimization, Conception, and Validation for Localization of Acoustic Sources in Deep-Sea Mining, IEEE Journal of Oceanic Engineering, Vol.46(2), p.555-563. DOI: 10.1109/JOE.2020.3004018. [DOI:10.1109/JOE.2020.3004018]
7. Król, J. and Błażejewski, A., (2020), Fibonacci Array-Based Focused Acoustic Camera for Broad-Band Beamforming, Journal of Sound and Vibration, Vol.478, p.115351. DOI: 10.1016/j.jsv.2020.115351. [DOI:10.1016/j.jsv.2020.115351]
8. Zhang, L., Liu, H., Liu, Y., et al., (2019), Direction-of-Arrival Estimation with Structured Array Designs, IET Microwaves, Antennas & Propagation. DOI: 10.1049/iet-map.2019.0518. [DOI:10.1049/iet-map.2019.0518]
9. de Bree, H.E., Druyvesteyn, W.F. and co-authors, (2008), Moving Microphone Arrays to Reduce Spatial Aliasing in the Beamforming Technique: Theoretical Background and Numerical Investigation, Journal of the Acoustical Society of America, Vol.124(6), p.3648-3658. DOI: 10.1121/1.2998778. [DOI:10.1121/1.2998778] [PMID]
10. Zhong, H., Zhou, Z., Zhang, P., et al., (2022), An Efficient Multireceiver SAS Imaging Algorithm for Large Data in Heterogeneous Environment, Research Square, Preprint. DOI: 10.21203/rs.3.rs-1624407/v. [DOI:10.21203/rs.3.rs-1624407/v1]

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