## 1 Introduction  
Ocean phenomena such as front, wind-driven red tides, and mixing upwelling are rapidly dynamic processes with high spatial and temporal characteristic, which are difficult to observe using regular static mooring observation systems. In order to provide coverage sampling in a regional area and ocean feature tracking in a mobile form, the use of mobile sensor platforms is preferred. Then, it becomes necessary to study the cooperation and optimization between the control and trajectory planning of mobile sensor networks and data assimilation in an ocean dynamic model. Researchers have carried out a number of ocean observation and environment monitoring programs. Data obtained from these programs has been applied to validate a variety of advanced instruments and ocean sampling strategies. These programs have also provided lots of data for research on ocean physics, biology, chemistry and others. Three large scale sea trials in 2000, 2003 and 2006 had been carried out [1,2] in the Autonomous Ocean Sampling Networks (AOSN) program. A variety of advanced, applicable instruments and regional observing technologies are proposed in ocean observation systems. A group of gliders were utilized to establish a virtual moored array to analyze meso-scale variability and phytoplankton in the Philippine Sea east of Luzon Strait for about 10 days of continuous observation [3]. The European Gliding Observatories (EGO) initiative [4,5] is a gathering of several European countries and oceanographic researchers, interested in ocean observation with underwater gliders. Surface vehicles and underwater vehicles together are exploited to establish a multi-layer model simulation and data assimilation system to collect temperature, salinity and ocean current data for oceanographic feature exploration [6]. The development of _in situ_ observing instruments and mobile platforms favors the improvement of ocean observing systemc [7]. Underwater observing sensor platforms mainly include Array for Real-time Geostrophic Oceanography (ARGO) profile floats, fixed underwater observation networks, shipboard oceanographic survey systems, and marine vehicles including Autonomous Underwater Vehicles (AUVs), Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Gliders (AUGs). Researchers prefer to select marine vehicles as sensor platforms carrying platforms for their superior maneuverability and low cost. Marine vehicles are intelligent, mobile, controllable sensor platforms and make possible rapid and adaptive ocean dynamics process observation, especially for near-real-time (NRT) observations of ocean micro-scale phenomena [8,9]. The development of computers and data assimilation techniques provides convenience for prediction and estimation of ocean dynamic process. Ocean models such as Modular Ocean Model (MOM), Princeton Ocean Model (POM), and Regional Ocean Modeling System (ROMS) [10] have developed to a mature status, and become an essential tool to simulate ocean environments. Ocean models are utilized to establish the communication between observations and ocean physical processes. Data assimilation is a technique that melts observation data, and ocean dynamic process together to make efficient, accurate and realistic estimations. Data assimilation methods can be grouped into two categories: sequential assimilation and non-sequential assimilation [11]. The former include optimal interpolation (OI), three-dimensional variation (3 DVAR) [12], Kalman Filter, Extended Kalman Filter (EKF), and Ensemble Kalman filter (EnKF) [13]. The latter includes four-dimensional variation (4 DVAR) [14] and Kalman Filter Smoother. According to the observation mission and optimal criteria, the NRT path planning of marine vehicles is closely related to the vehicles' maneuverability, data estimation and prediction, and cooperative control methods.