基于背景特征参数的激光雷达目标检测

激光雷达的弱小目标检测是激光雷达的关键技术, 其主要研究难点之一是在低信噪比下, 可用于区分目标与背景噪声的特征少。研究的对象是激光雷达的远距离目标回波, 主要指空中飞机目标。根据试验得到的数据, 发现目标点在背景中往往是一些孤立的点, 与背景的相关性较小。而背景中的任一点与前后背景点相关性较强, 可以用周围的点进行线形或非线性表示。为解决低信噪比下激光雷达的目标检测问题, 提出了基于背景特征参数的目标检测算法。运用高阶统计量作为背景特征值对杂波数据进行处理。在一个小区域内, 背景的高阶统计量不会有很大的起伏, 而目标在它所在的区域内具有相对突出的变化。信噪比得以提高, 然后通过恒虚警检测和多帧相关检测, 获取真正的目标。试验结果表明该方法非常有效, 实时性强, 具有较高的实用价值。

Signal Detection of Laser Radar Based on the Background Character Parameter

The detection of the faint target in the lidar is one of the key technologies of the lidar. One of its difficulties is that the characteristic distinguishing the target and the noise is very lacking. The target this paper researches is the plane. After researching the practical echo signal, we find that the target often is the isolated dot in the echo signal. The target is irrelevant with the noise. However, the noise is expressed with the neighboring dot. To resolve the problem of the target detection in low signal-to-noise, an algorithm of the target detection based on the background characteristic parameter is presented. The clutter is processed using the background characteristic parameter of mean variance. In the small region, the mean variance of the background is steady. However, the mean variance of the target is distinct. Therefore, the signal-to-noise is improved greatly. Then with CFAR detection and multi-frame relative detection, the true target is captured. The experimentation had proved this algorithm improved the performance of the lidar. This algorithm is effective and does well on real time, it is valuable in practice.