# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Physical Features
#### 3.1.1 Thermoclines  
A vertical gradient method [8; 9] was proposed to use an AUV to identify a thermocline and gave a demonstration in a dam reservoir in the Douro River, Northern Portugal. They applied a depth adaptation algorithm implemented on a small-size MARES AUV, so that the vehicle could stay in the vicinity of the thermocline. They simplified the thermocline profile by dividing it into three layers: a layer they named _thermocline_ (a thin layer in the water column having the greatest linear gradient), a deep-water layer (the least dynamic and the coldest zone lying below the thermocline layer in which the temperature decreased slowly with depth increase) and a mixed layer (a relatively dynamic zone above the thermocline layer due to various influencing factors from the atmosphere). This layered ocean model of the temperature structure was originally proposed by [PERSON] [10]. Utilising a state machine, the algorithm achieved detection and tracking of a thermocline by comparing the vertical temperature gradient in each depth bin with a set of thresholds in real time. [PERSON] et al. proposed [11] an average gradient method. Their method started with an initial deep dive to acquire a complete temperature profile from the surface to the operational depth limit of the AUV. The result from this initial yo-yo determined the size of the depth bins, also referred to as the vertical depth levels. The depth bin size in their algorithm is highly dependent on the water depth and the thermocline range. The guideline of the scale analysis used is shown in Table 4. The next step was to average the data over the depth bins and then calculate the rates of temperature change over the depth change (vertical derivatives). Any vertical derivatives greater than the average value were assessed to be within the thermocline. [PERSON] et al. [12] focused on the maximum value of vertical temperature gradient to detect a thermocline and its corresponding depth to guide an AUV to successively track the thermocline. In each ascent and descent leg, the peak gradient was saved and used to establish the subsequent target diving depth. They also allowed an additional extension depth, so as to be able to capture the genuine peak spots. Having this extension depth helped to deal with the large vertical depth variation that is often found in an undulated thermocline and helps prevent the AUV from getting trapped in a local maximum. They successfully applied their maximum gradient method on a Tethys AUV mission in Monterey Bay. [PERSON] and [PERSON] [13] presented a dichotomous triggering method, in benchtop simulations and during a short deployment of a glider in the Atlantic Ocean. This method is straightforward in the sense that depth bins are only specified by two variables, temperature and depth (a priori). There are no initial deep dives, headings or waypoints requirements. The transition between behaviours was triggered by events of interest with a fixed threshold parameter of 3 \({}^{\circ}\)C/4 m. The average temperature of depth bins at each point was evaluated by dichotomous criteria, as to whether it was above or below the threshold. This event-driven performance may include changing a glider's target depth or returning to a previous state or even a more complicated transition mechanism, if desired. Later, [PERSON] and [PERSON] [5] introduced a feature-based gradient method, a new hybrid adaptive sampling algorithm combining the average gradient method [11] and the maximum gradient method [12]. The two gradient methods were combined to complement each other. Based on the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Water Depth** & **Thermocline Depth** & **Depth Bin Size** \\ \hline Shallow water/coastal system & 100 m & 10 m & \(\sim\)1 m \\ Open ocean & 1000 m & 100 m & \(\sim\)10 m \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Brief scale analysis of thermocline depth range and depth bin size.