Marine radar monitoring IoT system and case study of target detection based on PPI images

Abstract

To ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy.

Deployment of Remote Sensing Technologies for Effective Traffic Monitoring

“…In such situations, the implementation of intelligent navigation channels, which involves deploying a certain number of radars on the buoys within the navigation channel, can efficiently curb the occurrence of collisions and improve navigation safety. In the maritime field, radar is a piece of important ship-supporting equipment, as it utilizes radio waves to detect and track the locations of vessels and transmit this information to the captains to help them maneuver their vessels [18].…”