## 1 Introduction
In view of artificial potentials and virtual bodies, [PERSON] _et al._[15] proposed a formation control method to detect and track ocean features, such as ocean fronts, upwelling, gradient fields, and eddies. [PERSON] _et al._[16] introduced a cooperative KF method to track ocean features in a noisy and changing scalar field, which connects the formation control, data estimation, and the formation optimization together. [PERSON] _et al._[17] and [PERSON] _et al._[18] consider the feature boundary as a hidden Markov model (HMM) with separated sample data information collected from multi vehicle platforms, and develop a Page's cumulative sum algorithm for each vehicle's boundary track, and estimate the boundary with the data collected from vehicles, the prior knowledge and boundary model. [PERSON] _et al._[19] designed a mutual observation method: gliders' trajectory is predicted with the output information from an ocean model, and gliders provide NRT measurement information that is sent back to the ocean model. For the sample of physical and physical biogeochemical dynamics, [PERSON] [20] combines the ocean model with sample data via data assimilation, and develops a novel adaptive modeling approach with simplified maximum likelihood principles. [PERSON] _et al._[21] proposed Objective Analysis (OA) as the evaluation criteria, and then constrained the mobile vehicle-sensor nodes on the parameterized trajectory to execute samples. The desired observation data can be obtained with the optimization of trajectory parameters and number of vehicle sensor nodes. [PERSON] _et al._[8] described networks with drifting profiling floats and gliders for adaptive ocean sampling, and proposed the genetic algorithm to optimize the gliders' trajectory. Considering the situation where ocean current is comparable to vehicles' velocity, [PERSON] _et al._[22] proposed a method to compute an optimal transit route to rapidly reach a specified waypoint based on the ray equations for non dispersive wave propagation. [PERSON] _et al._[23] applied a mixed integer linear programming (MILP) method to find the vehicles' path, that where the sample process can maximize the uncertainty of scalar field estimates. [PERSON] _et al._[24] proposed a genetic algorithm to assimilate the uncertain regional measurements into the dynamical ocean model. In this study, a framework of ocean phenomena observing systems with underwater mobile sensor networks is given. The main idea is to integrate the control and plan of the sensor platforms with data assimilation to obtain more sample data. A brief overview of observation system configurations is given in Section 2. Section 3 presents the decomposition and assignment of observation missions. With respect to different kinds of observation missions, the advantage and characteristics of cooperative observations with different marine vehicles are analyzed. Autonomous trajectory planning and optimization of underwater vehicles, mainly for ocean feature tracking and coverage sample of ocean areas, is described in Section 4. Section 5 details the ocean data assimilation and ocean model simulation. Descriptions of semi-physical simulation platforms of marine vehicles are given in Section 6. Finally, Section 7 contains the corresponding simulation results, including the isothermal line tracking in the South China Sea (SCS) and underwater glider dynamic experiments.