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The infrared small target detection technology has a wide range of applications in maritime defense warning and maritime border reconnaissance, especially in the maritime and sky scenes for detecting potential terrorist attacks and monitoring maritime borders. However, due to the weak nature of infrared targets and the presence of background interferences such as wave reflections and islands in maritime scenes, targets are easily submerged in the background, making small infrared targets hard to detect. We propose the multidimensional information fusion network(MIFNet) that can learn more information from limited data and achieve more accurate target segmentation. The multidimensional information fusion module calculates semantic information through the attention mechanism and fuses it with detailed information and edge information, enabling the network to achieve more accurate target position detection and avoid detecting one target as multiple ones, especially in high-precision scenes such as maritime target detection, thus effectively improving the accuracy and reliability of detection. Moreover, experiments on our constructed dataset for small infrared targets in maritime scenes demonstrate that our algorithm has advantages over other state-of-the-art algorithms, with an IoU of 79.09%, nIoU of 79.43%, F1 score of 87.88%, and AuC of 95.96%. [ABSTRACT FROM AUTHOR]

On the basis of nonlinear programming model and solution of radar target polarization enhancement, a novel efficient iterative algorithm is presented. The optimization efficiency and precision are improved. The performance of algorithm is analyzed and verified by experiments. [ABSTRACT FROM AUTHOR]

Examines the problems of radar target polarization enhancement. Discussion of the derivative properties of the monotone; Effects of transforming a constrained nonlinear optimization problem into a zero-search problem of a monotone; Techniques in achieving zero point.

Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking. However, the clutter process in the ET‐PHD filter is modelled as Poisson RFS, which is reasonable for only some scenarios in reality. An easily implemented ET‐PHD filter for arbitrary clutter process, has not yet been studied. In this work, another form of the general PHD filter, suitable for arbitrary clutter and target measurement is derived using a probability generating functional (PGFL)‐based RFS framework. The proposed filter is equivalent to the general PHD filter proposed by Clark and Mahler, but it is easily implemented in an arbitrary clutter process because its clutter process is denoted by the probability density function. Then, an arbitrary clutter ET‐PHD (AC‐ET‐PHD) filter is simplified from the general PHD filter derived by us. To reduce the computational complexity of the proposed filter, a variant of the distance partitioning algorithm is put forward. Simulation results show that the AC‐ET‐PHD filter can be applied to the scenarios with the non‐Poisson clutter process, which means that it will be useful for multiple extended targets tracking in a more complicated clutter process. [ABSTRACT FROM AUTHOR]