We propose a 3D Hough transform (3D-HT) algorithm that can overcome the disadvantages of high complexity and large data storage space of the existing 3D-HT-based small target detection algorithms. The proposed algorithm uses two coordinates at different times and coordinate errors to construct a 3D pipeline. Subsequently, it counts the number of points in the 3D pipeline and confirms the presence of a target trajectory in the pipeline when the number of points exceeds a predefined threshold. Finally, it performs trajectory merging and filtering before outputting the target trajectory coordinates. The proposed algorithm has low complexity because the used trajectory parameters are the coordinates in the data space, and only a linear transform between the coordinates is required. Unlike the existing algorithms that use an accumulator array to represent the Hough space, the proposed algorithm uses only a single position-adaptive cumulative cell in the Hough space. Therefore, there is no limitation on data storage in the Hough space. Simulation and analysis show that the small target detection algorithm based on the proposed transform is robust to noise, requires small data storage space, and has high computational efficiency. The proposed algorithm can be used in infrared and radar small target trajectory detection systems.

中文翻译：

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三维流水线Hough变换用于小目标检测

我们提出了一种3D Hough变换（3D-HT）算法，该算法可以克服现有基于3D-HT的小目标检测算法复杂度高和数据存储空间大的缺点。所提出的算法在不同时间使用两个坐标和坐标误差来构建3D管线。随后，它计算3D管线中的点数，并在点数超过预定义阈值时确认管线中目标轨迹的存在。最后，它在输出目标轨迹坐标之前执行轨迹合并和过滤。由于所使用的轨迹参数是数据空间中的坐标，并且仅需要坐标之间的线性变换，因此该算法具有较低的复杂度。与现有的使用累加器数组表示霍夫空间的算法不同，该算法仅在霍夫空间中使用单个位置自适应累积单元。因此，对霍夫空间中的数据存储没有限制。仿真和分析表明，基于该变换的小目标检测算法具有较强的抗噪能力，所需的数据存储空间小，计算效率高。该算法可用于红外和雷达小目标弹道探测系统。仿真和分析表明，基于该变换的小目标检测算法具有较强的抗噪声能力，所需的数据存储空间小，计算效率高。该算法可用于红外和雷达小目标弹道探测系统。仿真和分析表明，基于该变换的小目标检测算法具有较强的抗噪声能力，所需的数据存储空间小，计算效率高。该算法可用于红外和雷达小目标弹道探测系统。