The warning LiDAR used in industries such as autonomous driving and smart mining obtains target distance and alerts by threshold detection of echo. When an warning LiDAR works in an adverse environment (fog, dust, etc.), the emitted laser pulse will be scattered when it hits water drops or dust. Backscattered light will form backscattering echo, and forwardscattered light will be reflected when encountering an object to form a target reflection echo. Backscattering echo will interfere with the detection of target reflection echo. Therefore, backscattering echo compression is essential for the warning LiDAR.For the detection of surface targets, the intensity of the LiDAR echo is inversely proportional to the square of the distance, resulting in the intensity of the near-distance echo being several orders of magnitude greater than the intensity of the far-distance echo. Because the optical path of the backscattering echo is shorter than that of the target reflection, the backscattering echo is in front of the target reflection echo. The optical and mechanical structure of the LiDAR transmission module obstructing the receiving field of view can affect the near-distance responsibility, resulting in the echo strength not monotonically decreasing with distance, but first increasing and then decreasing with distance. By reverse utilizing this attribute, we can model the LiDAR echo based on the optical and mechanical structural parameters (overlap factor) to achieve near-distance echo compression, thereby compressing backscattering echo in adverse environments (fog, dust, etc.) without affecting target reflection echo, and thus reducing false alarm rate. A dynamic range compression method for coaxial warning LiDAR based on overlap factor is proposed utilizing the influence of coaxial occlusion and non-uniformity of laser spot energy distribution on overlap factor (Fig.4-7).LiDAR with parameters in Tab.1 is used to measure response curve and parameters in Tab.1 is used to get simulated response curve. Table 2 presents datas of the measured normalized response curves, simulated normalized response curves, and overlap factors. The response curve plotted according to Tab.2 is shown (Fig.10). Comparative analysis of measured data and simulation data is conducted. The measured data showed a peak echo intensity at 1 096 mm and decreased to both sides; The echo intensity of the simulated data before correction monotonically decreases with the target distance and does not match the measured data; The simulated data after correction showed a peak echo intensity at 895 mm, which was closer to the measured peak position of 1 096 mm. Moreover, the corrected echo intensity decreased towards both sides of the peak and showed a similar trend to the measured data. Considering the influence of factors such as processing and adjustment errors in the manufacturing process of LiDAR, the actual peak position should be slightly larger than the predicted 895 mm by the modified model, which is closer to the measured 1 096 mm. The Pearson correlation coefficient between the corrected simulation data and the measured data is 0.908 5, indicating a strong correlation between the simulation response curve and the measured response curve, effectively proving the effectiveness of the model built in this paper. Based on the above conclusions, simulation analysis is conducted to investigate the effects of four parameters on the response curve, which are the radius of the transmitting lens d, the radius of the aperture R, the divergence angle of the laser 2t, and the focal length of the receiving lens f. The simulation results show that an increase in the radius d of the transmitting lens, a decrease in the laser divergence angle 2t, or an increase in the focal length f of the receiving lens can slightly compress the dynamic range of the response curve, but not significantly; Increasing the aperture radius R can significantly compress the dynamic range of the response curve. Therefore, in the optical and mechanical design stage of LiDAR, it is possible to effectively compress the response at near distance relative to far distance by increasing the aperture radius of the warning LiDAR, thereby achieving compression of the backscattering echo relative to the target reflection echo to reduce the impact of adverse weather conditions on warning LiDAR. This model has been used to guide the optimization of existing warning LiDAR products, especially for the optimization design of warning LiDAR applications in adverse weather environments such as autonomous driving and smart mining. It has important practical guidance significance and broad application prospects.