# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 3 Visual Perception Navigation Technology
### Single Perception
#### 3.1.2 Radar  
Radar, radio detection and ranging technology comprise an electronic system that uses electromagnetic waves for target detection and localization. It realizes precise measurement of the position, speed and shape of a target by transmitting electromagnetic waves and receiving reflected signals. The working principle of radar is based on the propagation characteristics of electromagnetic waves, and target information is obtained by analyzing the time delay, frequency change and phase difference of the echo signal. Radar technology is also being developed to be gradually applied in visual perception navigation systems. [PERSON] et al. (2022) innovatively proposed a visual transformer framework based on contextual joint representation learning, referred to as CRTransSar, for the synthetic aperture radar (SAR) target detection task, combining the global contextual information perception capability of a transformer with the local feature representation capability of a convolutional neural network (CNN), which improves detection accuracy [88]. [PERSON] et al. (2023) proposed a novel robust CFAR procedure for background clutter fitting, which provides a new interdisciplinary perspective for SAR image segmentation and achieves excellent detection performance [89]. [PERSON] et al. (2019) introduced a maritime target detection method based on improved Faster R-CNN for navigation radar PPI (Plane Position Indicator) images, which improved the target detection performance of radar [90]. In addition to ordinary sea navigation, ice navigation is likewise an important research problem in path planning. [PERSON] et al. (2021) used nautical radar imaging to reconstruct ice navigation scenarios, establish a sea-ice warning visualization function and select the optimal path planning scheme [91]. [PERSON] et al. (2021), on the other hand, evaluated the accuracy of ship positioning based on the observation of navigational radar echoes from electronic nautical charts, providing the prerequisites for an autonomous navigation system that replaces the Global Navigation Satellite System (GNSS) [92]. [PERSON] et al. (2022) proposed a maritime target detection method based on Marine-Faster R-CNN algorithm in complex environments, which utilizes convolutional neural networks (CNNs) for feature extraction and target identification of PPI images generated by radar echoes with enhanced generalization capability [93]. In order to solve the problems of the low target resolution of traditional radar and insufficient intuition of ship navigation, [PERSON] et al. (2022) proposed a multi-camera ship \"video radar\" enhanced navigation system and a deep learning-based feature point detection method (SRSuperpoint) to improve the navigation interface stitching effect and present a more comprehensive and intuitive navigational environment [94]. [PERSON] et al. (2023) used radar sequence images to estimate ship dynamic features, which provides a reliable method for ship collision risk assessment in ship traffic service centers and improves operator interactivity [95].