# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Sensor Scheduling Strategies  
Due to the limited energy source of sensors, effective sensor scheduling strategies are important for robust tracking by avoiding node failure incurred by energy exhaustion. An adaptive scheduling strategy is presented to increase the energy efficiency of each sensor node in [61]. However, the larger energy is consumed in the algorithm compared that with the sensor selection procedure. A Distributed smart sensor scheduling is proposed to switch the working mode of sensors automatically in [63]. The authors in [66] proposed an adaptive algorithm by changing the sampling interval when the tracking accuracy is satisfactory, which is focused on tradeoff between communication rate from the temporal domain. A proper criterion about the value of measurements residual for sensors figuring out whether transmitting their measurements is presented in [96]. Base on the criterion, artificial measurements are also created for those unsent measurements to guarantee the tracking accuracy. Furthermore, the best group of task sensor is determined by the generalized Breiman, Friedman, Olshen, and Stone (GBFOS) algorithms. The adaptive algorithm is focused on tradeoff between communication rates from the spatial domain.  
To reduce the energy, only part of sensors need to participate into tracking. In general, in 3D underwater environment, four sensors can determine the position of one target. Therefore, some researches are focused on designing the sensor scheduling strategies which can select the most informative sensors from the candidate ones located within the sensing region of the target. In [84], only 60% sensors located closer to the target can receive the waking message. The measuring accuracy of underwater acoustic sensors is dependent on the measuring distance. It is intuitive to select the nearest sensors as the task sensors. However, the algorithm merely based on distance neglect the influence of node topology on the tracking performance. The authors in [97] derive the multiple-model posterior Cramer-Rao lower bound (PCRLB) in the presence of multiplicative noise. Base on the estimated PCRLB, an effective sensor selection scheme is developed for determining the subset sensors to attend tracking task. The algorithm shows great performance on tracking 3D maneuvering target and can be extended to underwater application. Most of the scheduling strategies mentioned before are based on global information. The global sensor scheduling schemes assume that each sensor knows the position of global sensors, which is impractical for underwater acoustic tracking for the limited energy and memory source. The sensor scheduling strategy based on local node selection is proposed in [98]. The communication radius of sensors is assumed adjustable. The fusion center calculates the longest distance between itself and the sensor with probability of detecting the target at next time. The fusion center adjusts the communication radius according to the longest distance and transmits the waking message containing containing the prediction state of the target. These sensors receiving waking messages can further decide whether participating into tracking by calculating its distance to the predicted position.