# Editorial for Special Issue: Underwater Acoustics, Communications, and Information Processing
## 4 Underwater Target Detection and Localization  
Once proper communication systems are implemented for harsh underwater environments, there are abundant applications to be extended further towards ocean engineering, such as underwater navigation, underwater exploitation, and underwater monitoring and engineering, for which target detection, ranging, object localization and tracking, and ocean property tomography are some key technologies to be further refined.  
In [17], \"Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements\", [PERSON] et al. investigated the classical object localization issue but against the harsh underwater environment. This underwater target localization problem presumes received signal strength (RSS) measurements, especially in cases of unknown transmit power condition, and a fast implementation algorithm is proposed by transforming the non-convex problems into a generalized trust region subproblem framework. The authors used computer simulations to show the superior performance of the proposed methods in the realistic underwater environment.  
Underwater multi-object tracking while moving is another interesting problem in active sonar systems. [PERSON] et al. proposed to improve the conventional probability hypothesis density (PHD) and cardinalized PHD (CPHD) algorithms for an active acoustic tracking system in [18]. Particularly when the detection probability is not available as a priori, it is necessary to estimate simultaneously the number of multi-targets, the detection probability, and their states. The detection probability is proposed as \"Pd\", analytically formulated for a fixed false alarm rate, and adding the adaptive ellipse gate strategy to reduce the computational load in the conventional PHD and the cardinalized PHD CPHD algorithms. Finally, under the linear Gaussian mixture (GM) channel assumption, the proposed Pd-GM-PHD and Pd-GM-CPHD algorithms are more realistic and accurate in underwater active sonar tracking systems.  
The issue of detecting objects bottoming on the sea floor is interesting in a practical sense, and [PERSON] et al. proposed an underwater cylindrical object detection algorithm using the spectral features of active acoustic signals in [19]. The main idea in their work was to use the logistic regression model to discriminate the target from the clutter and to train the model using sufficient data in the real world. Once the logistic regression model is trained by the simulated data that are generated based on the mathematical model for the backscattering of the cylindrical object, the results verify that the proposed method is effective under the circumstance with insufficient experimental data, when a mathematical model of the target is available.  
The guest editors of the Special Issue of Applied Sciences would like to thank all authors and anonymous reviewers for contributing their original work to this Special Issue, regardless of the final editorial decision of the submitted manuscripts. We also acknowledge the entire staff of the journal's editorial board for providing their cooperation regarding this Special Issue. We hope that the researchers and scientists who participated in editing this Special Issue enjoyed their time and efforts. This research was a part of the project titled \"Development of Automatic Identification Monitoring System for Fishing Gears, funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare no conflict of interest.