Performance evaluation and analysis for dipole source localization with lateral line sensor arrays

Abstract

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.

An underwater moving dipole tracking method of artificial lateral line based on intelligent optimization and recursive filter

“…They proved that the detection precision of MUSIC is higher, and the detection range increased when the sensor density and BL of ALLS increased [22]. Liu et al also compared the dipole location accuracy of MUSIC and minimum variance distortionless response (MVDR) methods based on an ALLS.…”

Fish Lateral Line Inspired Flow Sensors and Flow-aided Control: A Review

“…For further study, MUSIC method showed a potential to locate two close dipole sources [76] . In 2019, Ji et al established a quantitive and method-independent Cramer-Rao Lower Bound (CRLB) model to evaluate the localization performance of the above two methods, least square and MUSIC, which provided guidance on the optimal design of the ALL with the least sensors and the most appropriate spacing [77] .…”

“…Table 3 as follows can be a summary of this section. Flow induced by oscillating sources Tang et al 2019 [62] 8 pressure sensors Linearizing the kinematic and dynamic conditions of the free surface wave equation Yang et al 2007 [30] An array of AHC sensors Extracting velocity amplitudes at the sphere vibration frequency Yang et al 2010 [32] 15 biomimetic neuromasts Beamforming algorithm Pandya et al 2006 [44] An array of 16 hot-wire anemometers Template training approach and the modeling approach Asadnia et al 2013 [33] An array of 2 by 5 pressure sensors Measuring the maximum peak-to-peak out put of the sensors Abdulsadda et al 2011 [35] 5 IPMC sensors Neural network Abdulsadda et al 2013 [65] , Chen et al 2012 [69,70] 6 IPMC sensors Gauss Newton and Newton Raphson algorithms Ahrari et al 2016 [66] Multiple flow velocity sensors Bi-level optimization methodology Zheng et al 2018 [63] 9 underwater pressure sensors forming a cross Generalized regression neural network Lin et al 2018 [64] 9 pressure sensors in a straight line Least square method Ji et al 2018 [76] 9 pressure sensors in a straight line MUSIC, MVDR Ji et al 2019 [77] 9 pressure sensors in a straight line CRLB model…”

Artificial lateral line based relative state estimation between an upstream oscillating fin and a downstream robotic fish

“…Recently, varieties of artificial lateral line systems (ALLSs) have been developed for their great potentials in improving the performance of underwater robots and surface vehicles, including remotely operated vehicle (ROV) [2], autonomous surface vehicle (ASV) [3,4], bio-inspired aquatic system [5], etc. The lateral line inspired researches cover from localization of dipole source [6][7][8][9][10], identification of flow characteristics [11][12][13], and applications on underwater robots [14][15][16][17][18][19][20][21][22][23][24]. Among the above-mentioned researches, investigating the applications of ALLS on underwater robots has been capturing an increasing attention.…”