| Abstrakt: |
Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the binary and barycentric methods are inadequate, as they do not capture critical information for tracking targets, such as direction. Therefore, in this article, a new method for improving the estimation of target characteristics to improve tracking accuracy based on the processing of high-resolution radar images is proposed. The proposed method consists of three modules. Firstly, the radar images of the target are decomposed into layers to determine local maximum regions and to estimate target characteristics such as reflected energy and area and the centroids of plots. In the second module, the plots are grouped using a fuzzy logic system. The inputs of the fuzzy logic system include the above-estimated parameters of the targets. The output is the chance that the plot is at the center of the target. The plots with the highest chances are considered target centers, and the other plots are grouped into their respective target. At the end, the true target center is recalculated. This process is called modified fuzzy C-means (FCM-M). In the last stage, the estimated target center coordinates are fed into a Kalman filter (KF) to solve filtering and tracking problems. The performance is evaluated using a measured radar dataset. The experimental results show that the proposed method performs better than traditional methods based on binary image processing. Additionally, the proposed method offers extra information about the targets, such as their direction, the energy of each reflected part, and the area, which traditional methods does not provide. [ABSTRACT FROM AUTHOR] |
