Nonlinear estimation-based dipole source localization for artificial lateral line systems
Source abstract
Abstract
As a flow-sensing organ, the lateral line system plays an important role in various behaviors of fish. An engineering equivalent of a biological lateral line is of great interest to the navigation and control of underwater robots and vehicles. A vibrating sphere, also known as a dipole source, can emulate the rhythmic movement of fins and body appendages, and has been widely used as a stimulus in the study of biological lateral lines. Dipole source localization has also become a benchmark problem in the development of artificial lateral lines. In this paper we present two novel iterative schemes, referred to as Gauss-Newton (GN) and Newton-Raphson (NR) algorithms, for simultaneously localizing a dipole source and estimating its vibration amplitude and orientation, based on the analytical model for a dipole-generated flow field. The performance of the GN and NR methods is first confirmed with simulation results and the Cramer-Rao bound (CRB) analysis. Experiments are further conducted on an artificial lateral line prototype, consisting of six millimeter-scale ionic polymer-metal composite sensors with intra-sensor spacing optimized with CRB analysis. Consistent with simulation results, the experimental results show that both GN and NR schemes are able to simultaneously estimate the source location, vibration amplitude and orientation with comparable precision. Specifically, the maximum localization error is less than 5% of the body length (BL) when the source is within the distance of one BL. Experimental results have also shown that the proposed schemes are superior to the beamforming method, one of the most competitive approaches reported in literature, in terms of accuracy and computational efficiency.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Underwater Vehicles and Communication Systems · Underwater Acoustics Research · Marine animal studies overview
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (65)
Show source-provided references
1. Abdulsadda A T · Proc. 15th Int. Conf. on Advanced Robotics (Tallinn, Estonia) · 2011 · 447
2. Abdulsadda A T · Proc. IEEE Int. Conf. on Robotics and Automation (Shanghai, China) · 2011 · 271910.1109/ICRA.2011.5980545 ↗
3. 10.1080/19475411.2011.65023310.1080/19475411.2011.650233 ↗
4. 10.1117/12.88187710.1117/12.881877 ↗
5. Akanyeti O · Bioinspir. Biomim. · 2011 · 610.1088/1748-3182/6/3/036001 ↗
6. Bleckmann H · Reception of Hydrodynamic Stimuli in Aquatic and Semiaquatic Animals (Progress in Zoology) · 1994 · 1
7. 10.1007/s00359-007-0282-210.1007/s00359-007-0282-2 ↗
8. 10.1007/978-3-642-28302-4_1010.1007/978-3-642-28302-4_10 ↗
9. 10.1098/rspa.2010.009510.1098/rspa.2010.0095 ↗
10. 10.1109/TMECH.2012.219430010.1109/TMECH.2012.2194300 ↗
11. Chen Z · Smart Mater. Struct. · 2007 · 16 · 147710.1088/0964-1726/16/4/063 ↗
12. 10.1023/A:101249100749510.1023/A:1012491007495 ↗
13. 10.1007/978-0-387-22628-6_710.1007/978-0-387-22628-6_7 ↗
14. 10.1007/s00359005005710.1007/s003590050057 ↗
15. 10.1007/s00359005005810.1007/s003590050058 ↗
16. 10.1007/BF0019397410.1007/BF00193974 ↗
17. 10.1007/978-1-4612-3714-3_2210.1007/978-1-4612-3714-3_22 ↗
18. 10.1002/047120061110.1002/0471200611 ↗
19. 10.1242/jeb.0214010.1242/jeb.02140 ↗
20. 10.1016/j.sna.2010.02.01610.1016/j.sna.2010.02.016 ↗
21. Dijkstra M · J. Micromech. Microeng. · 2005 · 15 · S13210.1088/0960-1317/15/7/019 ↗
22. 10.1038/3504070610.1038/35040706 ↗
23. Fan Z · J. Micromech. Microeng. · 2002 · 12 · 65510.1088/0960-1317/12/5/322 ↗
24. 10.4031/MTSJ.45.4.2010.4031/MTSJ.45.4.20 ↗
25. 10.1007/s00422-005-0005-010.1007/s00422-005-0005-0 ↗
26. 10.1109/TMECH.2010.209066510.1109/TMECH.2010.2090665 ↗
27. 10.1242/jeb.00007510.1242/jeb.000075 ↗
28. 10.1007/s00359-007-0275-110.1007/s00359-007-0275-1 ↗
29. 10.1007/BF0033693210.1007/BF00336932 ↗
30. 10.1007/BF0019772510.1007/BF00197725 ↗
31. 10.1007/BF0019772610.1007/BF00197726 ↗
32. Hassan E-S · Biol. Cybern. · 1993 · 69 · 52510.1007/BF01185424 ↗
33. 10.1007/BF0000276310.1007/BF00002763 ↗
34. Jacobsen E · IEEE Signal Process. Mag. · 2003 · 20 · 74
35. 10.1007/978-1-4612-3714-3_410.1007/978-1-4612-3714-3_4 ↗
36. Kay S M · Fundamentals of Statistical Signal Processing: Estimation Theory · 1993
37. Kim K J · Smart Mater. Struct. · 2003 · 12 · 6510.1088/0964-1726/12/1/308 ↗
38. 10.3762/bjnano.2.3210.3762/bjnano.2.32 ↗
39. Kroese A B A · J. Neurophysiol. · 1992 · 68 · 221210.1152/jn.1992.68.6.2212 ↗
40. Lamb H · Hydrodynamics · 1932
41. 10.1242/jeb.0112510.1242/jeb.01125 ↗
42. Liu C · Bioinspir. Biomim. · 2007 · 2 · S16210.1088/1748-3182/2/4/S05 ↗
43. 10.1039/b808839j10.1039/b808839j ↗
44. 10.1007/s00359-008-0350-210.1007/s00359-008-0350-2 ↗
45. 10.1038/4013510.1038/40135 ↗
46. Oppenheim A V · Discrete-Time Signal Processing · 2010
47. Ozaki Y · Proc. 13th IEEE Int. Conf. on Micro Electro Mechanical Systems (Miyazaki, Japan) · 2000 · 531
48. 10.1155/ASP/2006/7659310.1155/ASP/2006/76593 ↗
49. 10.1126/science.98205610.1126/science.982056 ↗
50. 10.1242/jeb.0112910.1242/jeb.01129 ↗
51. 10.1016/j.mee.2011.02.09110.1016/j.mee.2011.02.091 ↗
52. Ren Z · Bioinspir. Biomim. · 2012 · 7
53. Sadeghi M M · Proc. IEEE Int. Electron Devices Meeting · 2011 · 29.4.1–29.4.4
54. 10.1117/12.88057110.1117/12.880571 ↗
55. Shahinpoor M · Smart Mater. Struct. · 2001 · 10 · 81910.1088/0964-1726/10/4/327 ↗
56. 10.1109/JOE.2010.206081010.1109/JOE.2010.2060810 ↗
57. 10.1103/PhysRevLett.102.05810410.1103/PhysRevLett.102.058104 ↗
58. 10.1007/s00422-010-0369-710.1007/s00422-010-0369-7 ↗
59. Venturelli R · Bioinspir. Biomim. · 2012 · 710.1088/1748-3182/7/3/036004 ↗
60. 10.1007/BF0061117510.1007/BF00611175 ↗
61. 10.3390/s710238910.3390/s7102389 ↗
62. 10.1242/jeb.02045310.1242/jeb.020453 ↗
63. 10.1073/pnas.060927410310.1073/pnas.0609274103 ↗
64. 10.1063/1.361047010.1063/1.3610470 ↗
65. Yang Y · Bioinspir. Biomim. · 2010 · 510.1088/1748-3182/5/1/016001 ↗