The lateral line is a critical mechanosensory organ that enables fish to perceive the surroundings accurately and rapidly. Massive efforts have been made to build an artificial lateral line system rivaling that of fish for underwater vehicles. Dipole source localization has become a standard problem for evaluating the sensing capabilities of the developed systems. In this paper we propose, for the first time, the multiple signal classification (MUSIC) method in order to achieve high-resolution dipole source localization based on spatial spectrum estimation. We also present the minimum variance distortionless response (MVDR) by making an improvement to the previous Capon's method. Experiments are conducted on a linear prototype of lateral line canal and the localization performance of these two methods are compared. The results show that the MUSIC method provides an overall localization resolution improvement of 10.4% and maintains a similar level of localization accuracy compared with the MVDR method. Further studies show that the MUSIC method has the potential of localizing two closer incoherent dipole sources with a minimum lateral separation of 20 mm, versus 70 mm for the MVDR method, at a dipole-array distance of half the array length. Both localization methods have strong robustness to the vibrational state of the dipole source. Our work provides a promising and robust way to meet the high-resolution and multi-source sensing requirements of underwater vehicles.
The unique mechanosensory lateral line system of fish inspires a novel and promising sensing scheme in dealing with the challenges that autonomous underwater vehicles (AUVs) face in underwater localization and navigation. In this paper, the quantitative and method-independent Cramer–Rao lower bound (CRLB) model is established to evaluate the practical performance of a lateral line sensor array (LLSA) for dipole source localization by considering the excitation pattern mismatches and the measurement noises. The effective localization area of the LLSA is studied under varying array lengths and sensor densities. Localizations for the LS (least squares) method and the MUSIC (multiple signal classification) method are compared in simulations. It is shown that the longer and denser the LLSA, the larger the effective localization area. Typically, the too separated sensor-to-sensor spacing deteriorates the near-body localization performance. The effective localization area obtained by employing the LS method matches the CRLB analysis, which also proves the validity of the analytical CRLB model. Besides, the MUSIC method presents smaller effective localization areas than the LS method. Physical experiments are also conducted and agree well with the CRLB analysis. Further study shows that the measurement noises have an equivalent effect on the excitation pattern mismatches, and can be effectively suppressed by choosing a large number of snapshots. In addition, the CRLB gives the perceptual distance that is consistent with the observation. At last, guidance on the design of a simplified LLSA with the least number of sensors and an optimal sensor-to-sensor spacing is provided for AUVs.
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