# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Mode-Based Method
#### 5.2.1 Passive Tracking
Compared with the system in [56] with a towed array, the system is much easier to design and realize in [75]. The BOT problem existing in most passive tracking scenarios poses a great challenge in multi-target tracking and trace across processing [76]. To address the multi-trace crossing problem, the authors in [77] innovatively propose the single double side constant false alarm rate (SD-CFAR) to detect possible targets from the bearing-time measurements. Then the multi-track gate method is adopted to choose proper measurements belonging to the trace of each target at the current time. Then the auto-regression (AR) model is exploited to solve the problem of trace interruption due to the missing of tracking points incurred by detection error or trace crossing. If one measurement is available for a trace, this trace can be updated by the measurement using KF. Otherwise, the AR model is carried out to predict the missing points, and the number of missing points determines either the trace to be updated or terminated. Similarly, the authors in [78] propose the probabilistic multi-hypothesis tracker (PMHT) for BOT problem to realize multi-sensor-multi-target tracking. The algorithm solves the data association and target estimation using the expectation-maximization (EM) algorithm and KF respectively. This algorithm performs very well in the clutter environment; however, the performance of it is dependent on the accuracy of the initial state estimation about the target. Recently, measurements in passive tracking are not limited to bearing measurements anymore, and it can contain the time-delay measurement obtained by hearing the emitted signals arriving at different sensors as [75] shows. However, the relationship between the target state and time-delay measurements is much complicated than the relationship between bearing measurements. A network consisting of underwater gliders is proposed in [37], where an array of two sensors embedded on each glider provides the passive measurements of time-delay for tracking purpose. To solve the previously mentioned problem, an adaptive compressive sensing and processing (ADCSP) method is employed to ensure the accuracy of tracking while reducing the measurement sampling rate and producing a low volume of measurement statistics transmitted to the fusion center. The introduction of the compressive sensing and processing method relaxes the requirement for the length of the sensor array and hardware hosted on the glider, which is the most significant contribution of this paper. Unknown environmental factors such as water depth, salinity, and currents pose great challenges for underwater target tracking [79]. Furthermore, plenty of undetermined noise in marine environments also causes the time-delay passive measurements being severely corrupted. As a result, a simple model is unable to address the underwater tracking problem. In [80], the tracking model of [PERSON] is extended to modeling for the bottom and surface reflection of sound in ocean environments as Figure 9 shows. Then the time-delay between the bottoms reflected signal and the  
Figure 8: Geometry to illustrate the effect of bearing error. direct one as well as the time-delay between the surface reflected signal and the direct one can be used to calculate the distance of the target to the observer. To improve tracking performance, the tracking model is developed by considering the sound speed variation resulted from the effect of water depth. The extra range information about the target is obtained by using multiple detecting platforms in [37; 75] or utilizing the reflection in [80], which makes the tracking much easier than that in [56; 73; 77; 78] only having the DOA measurements. Compared with other passive tracking algorithms, the method proposed in [77; 78] can be used for tracking multi-target. Reference [79] is one of the few tracking papers which attach importance to and propose a corresponding resolution for the effect of unknown environmental factors on underwater target tracking.