Research on the detection probability curve characteristics of long-range target based on SPAD array lidar

Using single photon avalanche diode (SPAD) array lidar to detect long-range target, the difference of target surface characteristics will cause the distortion of the detection probability curve. This distortion severely limits the ability of target feature extraction and recognition. In this paper, a detection probability model for detecting long-range target using SPAD array lidar is established, which considers the influence of the difference of target surface structural characteristics (distance and pose) and reflection characteristics (reflectivity and cross-sectional area). Based on the finite element analysis method, the proposed model can calculate the detection probability when the target surface has the above characteristics difference. The full width at half maximum (FWHM) and skewness are used to evaluate the distortion of the detection probability curve. It reveals that with the increase of the structural characteristic difference of the target, the increasing trend of FWHM satisfies the cubic polynomial, while the decreasing trend of skewness satisfies the cubic polynomial. As the ratio of the reflection characteristic of the target increases, the decreasing trend of FWHM satisfies the first-order polynomial, while the increasing trend of skewness satisfies the first-order polynomial. The experimental results show that the feature vector composed of the detection probability curve features (FWHM, skewness, and total detection probability) can be used to identify the pixels containing target surfaces with different distances. And the identifiable distance difference is less than the system pulse width distance. This paper lays a theoretical foundation for target characteristic analysis and target recognition based on SPAD array lidar.