With the development of the maritime economy, sea traffic is becoming more and more crowded, and sea accidents are also increasing. Research on maritime search and rescue decision-making technology cannot be delayed. This paper studies the maritime search and rescue decision algorithm, based on the optimal search theory. It also analyzes three important concepts: Probability of containment (POC), probability of detection (POD), and probability of success (POS) involved in the maritime search and rescue decision-making process. In this paper, the calculation methods of POC and POD variables have been improved, and the search success rate has been improved to some extent. Finally, an example analysis of the maritime search and rescue incident is given. Through verification, the algorithm proposed in this paper can support maritime search and rescue decisions.
ABSTRACT:Geometric calibration is able to provide high-accuracy geometric coordinates of spaceborne SAR image through accurate geometric parameters in the Range-Doppler model by ground control points (GCPs). However, it is very difficult to obtain GCPs that covering large-scale areas, especially in the mountainous regions. In addition, the traditional calibration method is only used for single platform SAR images and can't support the hybrid geometric calibration for multi-platform images. To solve the above problems, a hybrid geometric calibration method for multi-platform spaceborne SAR images with sparse GCPs is proposed in this paper. First, we calibrate the master image that contains GCPs. Secondly, the point tracking algorithm is used to obtain the tie points (TPs) between the master and slave images. Finally, we calibrate the slave images using TPs as the GCPs. We take the Beijing-TianjinHebei region as an example to study SAR image hybrid geometric calibration method using 3 TerraSAR-X images, 3 TanDEM-X images and 5 GF-3 images covering more than 235 kilometers in the north-south direction. Geometric calibration of all images is completed using only 5 GCPs. The GPS data extracted from GNSS receiver are used to assess the plane accuracy after calibration. The results after geometric calibration with sparse GCPs show that the geometric positioning accuracy is 3m for TSX/TDX images and 7.5m for GF-3 images.
Traditional static flow field visualization methods suffer from many problems, such as a lack of continuity expression in the vector field, uneven distribution of seed points, messy visualization, and time-consuming calculations. In response to these problems, this paper proposes a multi-scale mapping method based on real-time feature streamlines. The method uses feature streamlines to solve the problem of continuity expression in flow fields and introduces a streamline tracking algorithm which combines adaptive step length with velocity matching to render feature streamlines in a real-time and multi-scale way. In order to improve the stability and uniformity of the seed point layout, this method uses two different point placement methods which utilize a global regular grid distribution algorithm and feature area random distribution algorithm. In addition, this method uses a collision detection algorithm to detect and deal with the unreasonable covering between streamlines, which alleviates visual confusion in the resulting drawing. This method also uses HTML5 Canvas to render streamlines, which greatly improves the drawing speed. Therefore, use of this method can not only improve the uniformity of the seed point layout and the speed of drawing but also solve the problems of continuity expression in the vector field and messy visualization.
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