# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method
#### 5.1.1 Acoustic Imaging Sensor  
With the aid of visual analysis and the development of underwater imaging technologies, it is possible to realize underwater target tracking based on video or image sequences which are obtained by acoustic imaging sensors. Imaging sensors are not attractive compared with other acoustic sensors used in the underwater environment for their limited sensing range and poor visibility. Nevertheless, tracking based on visual data can also be an alternative method, especially in close-range tracking. Many approaches have been taken to overcome the limitations of tracking based on imaging sensors. In [49], the authors propose a multiple fish tracking system replacing traditional trawl surveys to ensure the safety of the captured fish after sampling. Before tracking, an automatic fish segmentation algorithm including modified [PERSON]'s thresholding method and histogram back projection procedure is exploited to overcome the problem of low contrast incurred by the fast attenuation and non-uniformity of LED illumination. Then built upon feature-based object matching method, extended Viterbi data association (VDA) is used for multi-target tracking to overcome the challenge of poor motion continuity incurred by a low frame rate (LFR) of capturing results. The proposed algorithm can be further used for fish abundance analysis and monitoring marine endangered species. However, the drawback of using a single imaging sensor is that it is unable  
Figure 5: Classification of the underwater target tracking algorithms. to capture the interested target all the time due to the limited field of view (FOV) and abrupt motion of the target. Researchers in [50] developed a tracking filter that fuses ultrashort-baseline (USBL) measurements and acoustic image measurements to realize reliable underwater target tracking. The filter provides robust tracking performance, even in the case that either USBL or image measurements are not available or faulty. USBL measurements are used for tracking underwater objects with the advantage of easy deployment and relatively long tracking range while providing target position with high variance [51]. The rough target position measurement provided by USBL is used by the imaging sensor to set the region of interest in where the target is located. Then the estimator updates the target position with precise image measurements when the imaging sensor finds the target in its region of interest. In addition to this, the authors in [50] are devoted to adapting the region of interest of the imaging sensor by using the estimated covariance of tracking filter, which effectively avoids false image measurements. In some dark or murky underwater situations, the images obtained from imaging sensors cannot be used to complete detecting and tracking tasks. The authors in [52] are inspired by the natural phenomenon that neotropical nocturnal freshwater fish generate the electric field to detect the object according to the perturbations in the self-generated electric field. The technique named electrical impedance tomography (EIT), which has been developed for medical applications is extended to the area of underwater tracking to estimate the position and velocity of the target in close range. The simulation results demonstrate that the tracking algorithm based on EIT has lower computation complexity; however, the range between the tracking platform and the target is limited. Based on the interested target being well detected by some vision-based target detection techniques such as feature-based detection and template-matching detection, the authors in [53] review two typical vision-based tracking techniques: optical flow tracking and mean shift tracking. Optical flow tracking estimates the position by calculating the flow velocity in two consecutive images. However, the performance of optical flow tracking is dependent on the number of reliable corner features tracked from the detected target areas.