## 1 Introduction  
Autonomous underwater vehicles (AUVs) have been widely used for various purposes, e.g., military, commercial, and marine scientific survey applications [1]. For different purposes and scenarios, the requirement for the localization and control accuracy of AUVs ranges from meters to hundreds of meters. Compared to manned underwater vehicles, AUVs have no human pilots on board and thus avoid being exposed to dull and dangerous underwater environments. Compared to the tethered unmanned underwater vehicles, i.e., remotely operated vehicles (ROVs) [2], AUVs are tether free and thus can reach wider ocean spaces without the technical issues brought by the tether cable. The perception-plan-action cycle [3] is achieved by AUVs without persistent human instructions in order to accomplish the programmed missions. However, in the underwater environment, the attenuation rate of the global positioning system (GPS) signals is high, which makes GPS not applicable. Moreover, the ubiquitous ocean currents pose great influences on the relatively slow-moving AUVs. These factors impede the realization of the autonomy of AUVs. In general, there are two types of localization methods underwater, i.e., distance independent methods and distance dependent methods [4]. Distance independent methods estimate the positions of underwater targets based on the topology of several beacon nodes. The most commonly used distance dependent methods include received signal strength indicator, time difference of arrival (TDOA), and time of arrival (TOA). They differ in the distance calculation processes. Multipath and shadow fading effects are usually concerned with the received signal strength indicator method. TOA methods use fewerfixed nodes than TDOA methods and are easier for implementation. The widely used base line localization methods are developed from TOA methods [5]. In TOA, the target position can be directly calculated based on the triangulation principle [6]. TOA has the advantages of low computational complexity and low cost. However, precise synchronization in-between subsurface nodes is often required which is hardly available in ocean applications considering the cost and power constraints. Besides, measurement noises play an important role in the localization accuracy. Improvement techniques such as the least square method and Kalman filter [7] have been proposed to overcome this issue. An extended Kalman filter (EKF) is designed to handle the measurement noises for a GPS intelligent buoy (GIB)-like system in underwater target tracking [8]. Experimental results show that the trajectory of the underwater target can be recovered well with the EKF based approach. Besides the localization issue, another difference between AUVs and ground or airborne robots is that the influence of the dense ocean current flow cannot be neglected [9]. Therefore, various regional ocean models that generate realistic ocean current fields have been developed and used for AUV simulations [10]. For AUVs that operate in coastal regions, considering the collision risks with ships and land, ref. [11] utilizes the probabilistic ocean current predictions in the path planning of AUVs to improve the safety and reliability with stochastic approaches. By modeling the ocean current as a vector valued function of space and time, the current flow field is described as an Eulerian map in [10]. The Eulerian map then provides inputs to a planning and control module to optimize metrics such as energy consumption and path lengths. Instead of using the predicted ocean current values, another approach is to treat ocean current as environmental disturbances directly. Soft sensors, i.e., \"observers\", are designed in [9; 12] to achieve integrated current estimation and AUV motion control performance. In [13], in order to compensate for the steady-state error caused by the presence of ocean current, an adaptive/integral proportional derivative controller is designed for AUVs.