In Situ Underwater Localization of Magnetic Sensors Using Natural Computing Algorithms

Abstract

In the shallow water regime, several positioning methods for locating underwater magnetometers have been investigated. These studies are based on either computer simulations or downscaled laboratory experiments. The magnetic fields created at the sensors’ locations define an inverse problem in which the sensors’ precise coordinates are the unknown variables. This work addresses the issue through (1) a full-scale experimental setup that provides a thorough scientific perspective as well as real-world system validation and (2) a passive ferromagnetic source with (3) an unknown magnetic vector. The latter increases the numeric solution’s complexity. Eight magnetometers are arranged according to a 2.5 × 2.5 m grid. Six meters above, a ferromagnetic object moves according to a well-defined path and velocity. The magnetic field recorded by the network is then analyzed by two natural computing algorithms: the genetic algorithm (GA) and particle swarm optimizer (PSO). Single- and multi-objective versions are run and compared. All the methods performed very well and were able to determine the location of the sensors within a relative error of 1 to 3%. The absolute error lies between 20 and 35 cm for the close and far sensors, respectively. The multi-objective versions performed better.

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

“…The algorithm considers versions of PSO and genetic algorithms, which are single and multi‐objective. Compared to the performance of the present technique, the approach computes the location of magnetic sensors with a 25 cm uncertainty around their precise location 28 . The authors have presented a target localization strategy based on the Time Difference of Arrival (TDOA) in an underwater environment that is not uniform.…”

“…Compared to the performance of the present technique, the approach computes the location of magnetic sensors with a 25 cm uncertainty around their precise location. 28 The authors have presented a target localization strategy based on the Time Difference of Arrival (TDOA) in an underwater environment that is not uniform. Although TDOA schemes allow targets to be asynchronous with anchors, TOA (Time of Arrival)-based methods require them to be synchronous.…”

“…The IUSSOT used in the proposed algorithm overcomes the various issues of existing literature 1 and provides better performance with respect to simulation metrics such as average localization error, average ranging error, RMSE, and NLE. Existing research [18][19][20]24,[26][27][28] is quite expensive since it determines the location of target nodes using a range-based localization technique and a vast amount of anchor nodes that are GPS enabled. The proposed approach uses the range-free method, and the nodes are initialized employing a centroid-based localization algorithm.…”

“…7.2 | Comparison of target node with respect to average ranging error analysis of LAS-IUSSOT and I-LASP 1 in sparse and dense region in 3D-UASN Average ranging error, as per Equations ( 27) and (28), is directly proportional to the difference between the target node (j) and reinforced node (v) or beacon node (u). That is the difference of real and calculated value in the given range R CR .…”

A Compensation Method for the Geomagnetic Measurement Error of an Underwater Ship-Borne Magnetometer Based on Constrained Total Least Squares

“…Therefore, the interference magnetic field in underwater carrier geomagnetic measurement mainly comes from the carrier magnetic field (solid magnetic field, induced magnetic field), the random magnetic field and the instrument error of the magnetometer itself, with the carrier magnetic field accounting for a relatively large proportion [ 7 , 8 , 9 ]. Interference magnetic fields will seriously affect the accuracy of geomagnetic field measurements, thereby affecting the accuracy of geomagnetic matching navigation [ 10 , 11 , 12 , 13 ]. Therefore, improving the accuracy of geomagnetic surveys through error compensation has become one of the most urgent and difficult problems to solve in geomagnetic matching navigation.…”