# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method
#### 5.1.3 UWSNs  
The development of microelectromechanical devices and wireless technologies leads to the emergence of inexpensive small sensors equipped with sensing, processing and communication capabilities [59]. UWSNs integrate numerous sensors deployed randomly or strategically in the monitoring area to complete underwater target tracking tasks. Compared with TASA, the collaborative nature of UWSNs brings several advantages such as self-organization, rapid development and inherent intelligent processing capability [60]. However, there are also many constraints for UWSNs. To ensure the tracking accuracy, it is desired to get as many measurements as possible from the sensors. If let all sensors work in detecting mode, for the limited energy and the fact that it is impractical to recharge the battery on each sensor, the lifetime of the USWN will be much shorter. Therefore, most of the underwater tracking algorithms based on UWSNs are focused on the trade-off between tracking accuracy and energy consumption. In [61], a tracking algorithm based on distributed UWSNs is proposed to increase the energy efficiency of each sensor node. This algorithm includes a wake-up/sleep (Wus) and valid measurement selecting (VMS) scheme. The estimated position of the target provided by KF is used to wake up sensors which could detect the target. The scheme of VMS determines the valid sensors which can detect the target among activated sensors and passes their measurements to the processing node to update the state of the target. Based on the previous work, the authors further extend the tracking algorithm to track maneuvering target based on UWSNs by combining the interacting multiple models (IMM) with energy-efficient schemes Wus and VMS [62]. A distributed smart sensor scheduling method is proposed to save energy at the level of sensors in [63]. Each node is modeled as a probabilistic finite state automaton (PFSA). According to the local information, the sensor updates the probability of PFSA to switch the working mode between sleeping, listening, low sensing and high-power sensing to minimize the energy consumption. As the energy model shown in [64], compared with the energy of the sensor node consumed during the process of obtaining and processing measurements, the communication with other sensors or the sink node dominates the whole energy consumption. Therefore, there is a vast literature focused on reducing the amount of communication to save energy. In [65], by cutting down the useless information communication between local sensors and the fusion center, the energy consumption has been dramatically decreased. The algorithm requires that the distributed sensor in UWSNs determines whether sending its measurement to the fusion center according to the value of measurement residual which indicates the new measurement containing how much information to update the state of the target. However, the threshold of measurement residual has a great effect on the tracking performance and this paper does not provide methods about how to determine the threshold. Among these energy-efficient tracking algorithms, the common characteristic is that not all measurements obtained in UWSNs are adopted or not all sensors work in active mode, which will surely reduce the accuracy of tracking. In [65], the authors design a scheme of selecting the obtained measurements to send, thereby saving energy by cutting down the amount of communication. To ensure the tracking accuracy, corresponding artificial measurements are generated in the fusion center to compensate for the unsent measurement. In [66], the authors propose an adaptive sensor scheduling strategy for underwater target tracking based on UWSNs, which is named accuracy-stressed and energy-concerned (AnE). The strategy is designed for saving energy based on guaranteeing tracking accuracy. According to the trilateration, four sensor nodes can determine the position of the target.