# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method
#### 5.1.3 UWSNs
To improve tracking accuracy, the best sensor group consisting of four sensors is picked out at each time step from the candidate sensors based on the criterion of predicted tracking accuracy generated by combining IMM with EKF. Then one member of the best sensor group which can minimize the communication burden is selected as the fusion center to reduce energy consumption. Additionally, the sampling interval is increased when the tracking accuracy is satisfactory, which can further increase the energy efficiency. Compared with other energy-efficient tracking algorithms, this paper keeps the tracking accuracy first, nevertheless, the selection of best sensor group will incur excessive computation cost when the number of the candidate sensors is large. Since UWSNs consist of numerous sensors, the topology of these sensors will also have a great effect on the tracking performance [67]. Inadequate sensor configuration will yield significant tracking errors no matter what tracking algorithm is utilized. Therefore, there are some papers that consider the configuration of sensor deployment to improve the tracking performance. The authors in [68] investigate the problem of optimal sensor deployment for underwater target tracking using range-only measurements. Based on the conclusion obtained from [69] that the sensor configuration derived from the Fisher information matrix (FIM) of a 3-D scenario can estimate the position of the target accurately by an unbiased estimator, the authors in [68] compute the optimal geometric sensor formation of UWSNs with arbitrary number of sensors by maximizing the FIM to obtain as much range-related information as possible. Recently, the complex network theory is employed to solve the topology control problem of UWSNs [70]. Based on the concept that UWSNs can be abstracted and analyzed as a scale-free complex network, the authors in [71] provide a mathematical model embodying the most typical characteristics of UWSNs such as large propagation delay, energy consumption, and signal irregularity. Firstly, a mode named edges constructed model (ECM) is used to generate the initial topology. Then a topology control strategy based on the complex network theory (TCSCN) is proposed to evolve the initial topology to a double clustering structure which can effectively ensure achieving (1, \(\upxi\))-Coverage and (1, \(\upxi\))-Connectivity while optimizing the propagation delay and energy consumption. Previous topology control strategies are focused on UWSNs with stationary nodes, while for mobile networks, the problem is converted into optimizing the trajectories of sensors. The authors in [72] investigate how to optimize the trajectory of sensors in the mobile wireless network and an effective approach named optimal control (OC) is presented. An integral objective function representing the track coverage of the sensor work is obtained using geometric transversals. On this basis, the problem of determining the trajectory that maximizes track coverage and minimizesenergy consumption can be transferred into a mathematical problem which can be solved by OC. By implementing this approach along with two traditional methods on three networks with different sizes, the authors demonstrate that OC can significantly improve the performance of the sensor work compared with average coverage and path planning method. The success of [72] lies in the fact that the authors obtain suitable objective functions and system models, which makes it possible to apply OC to mobile sensor networks. However, the result is obtained from simulations, which lacks credibility. Moreover, the tracking performance of two algorithms proposed in [71; 72] for the optimization of the structure of UWSNs has not been presented. The authors in [61; 62; 63; 65; 66] focus on the energy consumption of the tracking based on UWSNs. The consumption is reduced by turning off unnecessary sensors in former three papers, while by cutting needless communication between sensors in the latter two papers.