According to the statistics of the last five years, fishing vessel accidents accounted for about 80% of collisions of all ships and has led to many casualties. To prevent collision accidents, it is important to assess the collision risk potential related to the sailing characteristics of fishing vessels. The authors represented the traffic patterns of vessels that sail around Wando waters based on Automatic Identification System (AIS) and Radio Detecting and Ranging (RADAR) data. The authors analyzed the statistical near miss data between fishing vessels and non-fishing vessels in the Wando Vessel Traffic Services (VTS) area and assessed the risk of ship collisions. From this research, the authors identified waters with a high risk of ship collisions. The analyzed results can be used as basic data to develop collision prevention strategies which aides the decision making and efficient operation of VTS officers (VTSO.)

The maritime risk assessment is important not only to evaluate the safety level of the ports and waterways but also to reduce potential maritime accidents at sea in terms of the proactive measures of the maritime accidents. In this paper, the collision risk assessment in Mokpo waterways has been carried out based on the IALA recommended model, IWRAP. To evaluate the accident probabilities in Mokpo waterways, all data of vessels were collected from AIS and Radar observations data and the computer simulations were carried out. To assess the risk on the traffic, the scenario-base approach has been applied to the Mokpo waterway by using the maritime accident statics over the past 5 years.

The problem for parameters estimation of the received signals impinging on array sensors has long been of great research Interest in a great variety of applications, such as radar, sonar, and land mobile communications systems. Conventional subspace-based algorithms, such as MUSIC and ESPRIT, require an extensive computation of inverse matrix and eigen-decomposition In this paper, we propose a new parameters estimation algorithm via nonlinear minimization, which is simplified computationally and estimates signal parameters simultaneously.