Citation: Zhao, Y.; Zhao, H.; Liu, Y.;
Deng, X. Marine Adaptive Sampling
Scheme Design for Mobile Platforms
under Different Scenarios. J. Mar. Sci.
Eng. 2022, 10, 664. https://doi.org/
10.3390/jmse10050664
Academic Editors: Jacopo Aguzzi
and Giacomo Picardi
Received: 9 April 2022
Accepted: 10 May 2022
Published: 13 May 2022
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Journal of
Marine Science
and Engineering
Article
Marine Adaptive Sampling Scheme Design for Mobile
Platforms under Different Scenarios
Yuxin Zhao
1,2
, Hengde Zhao
1,2
, Yanlong Liu
1,2
and Xiong Deng
1,2,
*
1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;
zhaoyuxin@hrbeu.edu.cn (Y.Z.); 250467588@hrbeu.edu.cn (H.Z.); yanlong_liu@hrbeu.edu.cn (Y.L.)
2
Engineering Research Center of Navigation Instrument, Ministry of Education, Harbin 150001, China
* Correspondence: XiongDeng407@hrbeu.edu.cn
Abstract:
Marine adaptive sampling is a technique that makes full use of limited observation re-
sources by selecting the optimal positions. Recently, the design of an adaptive sampling scheme
based on a mobile platform has become a research hotspot. However, adaptive sampling system
involves multiple subsystems, and the attributes as well as tasks are always different, which may lead
to different sampling scenarios. A great deal of research has been conducted for specific situations,
especially with fixed starting and ending points. However, systematic design and simulation experi-
ments under various circumstances are still lacking. How to design the adaptive observation system,
so as to cope with the observation task under different scenarios, is still a problem worth studying.
Aiming to solve this problem, we designed a systematic scheme design process. The process includes
setting up and verifying the background field, adopting the hierarchical optimization framework to
adapt to different circumstances, and variable adjustments for twin frames. The needs covered in
this paper include not having a fixed starting point and ending point, only having a fixed starting
point, having a fixed starting point and ending point, increasing sampling coverage, and simple
obstacle avoidance. Finally, the relevant conclusions are applied to the multi-platform simultaneous
observation scenario. It provides a systematic flow pattern for designing adaptive sampling scheme
of mobile platforms.
Keywords: adaptive sampling; intelligent optimization; data assimilation; ocean forecasting
1. Introduction
Adaptive sampling is a sampling strategy based on prior data, which reduce the
blindness of observation. Mobile platforms such as autonomous underwater vehicle (AUV)
and gliders provide new options for sampling tasks in complex ocean [
1
,
2
]. Logically, how
to design adaptive sampling scheme based on mobile platform is a question worthy of
investigation. The solution of this problem has high development potential and application
value in exploring ocean environment and obtaining ocean state. Indeed, it has been applied
to thermocline tracking [
3
] and marine environment analysis [
4
]. With the improvement of
modern coupling model forecasting capabilities [
5
,
6
], more accurate prior information will
be provided, and the technology will have a broader development prospect [
7
]. The overall
process is divided into two parts: forecast and observation. Forecast provides guidance for
the design of observation scheme, and observation information improves the quality of
forecast as return by providing more accuracy initial conditions for next forecast.
In an adaptive observation problem based on a mobile platform, Ref. [
8
] proposed
a deployment method through a genetic algorithm. It studies co-optimization of hetero-
geneous platform and has been validated in a twin experiment framework [
7
]. Due to
the limitation in the spatial and temporal resolution of the model and the observation
properties of the platform, it only becomes a rough track of the mobile platform. The work
provides a reference for follow-up studies. Ref. [
9
] used the Gaussian process regression
method instead of an ocean model to complete the sampling of multi-platforms systems in
J. Mar. Sci. Eng. 2022, 10, 664. https://doi.org/10.3390/jmse10050664 https://www.mdpi.com/journal/jmse
