In the military and civil fields, detecting small aircraft is of great significance. In recent years, the rapid development of LiDAR technology has made it possible to detect small aircraft at long distances. However, the scale change and attitude change make the detection difficult. Therefore, a detection network of multi-attitude small aircraft based on LiDAR anchorfree is proposed in this paper. The network structure is improved on the basis of the CenterNet network; using the encoder-decoder network structure, the extended convolutional module is designed to improve the receptive field and obtain the multi-scale information of the object. The IOU sensing branch is added to the detection header of the network to improve the localization accuracy of the object. The experimental results show that the detection accuracy of the improved network on the self-built simulation data set is 2.12% higher than that before the improvement and finally reaches 92.45%. Therefore, using this method can effectively improve the detection accuracy of the object.
When using Gm-APD Lidar for depth imaging through realistic fog, the echo signal of the target is submerged in the background noise due to the strong absorption and scattering characteristics of the fog particles, resulting in serious defect of the recovered depth image of the target. To solve this problem, this paper proposes a dual-parameter estimation algorithm based on continuous wavelet transform (CWT) and maximum likelihood estimation (MLE) to improve the accuracy of fog signal estimation. Then the target and the fog signal are separated by estimating the fog signal of each pixel. Finally, the depth image of the separated target is processed by cross pixel complement and median filtering algorithms to improve the integrity of the target image. The experimental results show that, compared with the traditional algorithm, the target recovery of the reconstructed image is improved by 0.337, and the relative average ranging error is reduced by 0.3897. This research improves the weather adaptability of Gm-APD Lidar.
Gm-APD arrays lidar has the advantages of long imaging distance, small volume and low power consumption. Because of its unique high resolution three-dimensional range profile, it is expected to solve the problems of UAV safe flight, autonomous obstacle avoidance and so on. In this paper, according to the dynamic imaging requirements of UAV lidar, a joint image stabilization control algorithm of adaptive Kalman filter and PID is proposed to suppress the disturbance of UAV platform to lidar system and make the laser beam point to the target stably. The vibration test experiment of Gm- APD lidar system is made. Under the condition of horizontal amplitude 5mm and frequency 15Hz sine wave disturbance, the gyro drift is less than 1.7 °/ s, and the target drift is no more than 4 pixels. It is proved that Gm-APD lidar can be applied in the field of UAV safe flight.
In the process of underwater lidar wake detection, the multipath effect leads to pulse stretching of the echo signal, which is a distinguishing feature to distinguish the wake echo signal. In order to explore the factors that affect the pulse stretching of the echo signal, this paper convolves the instantaneous echo energy of the bubbles at different distance layers with the transmitted pulse, and establishes a distance layer summation model(DLSM) of underwater bubbles. This paper analyzes the effect of bubble density on the backscattering coefficient of the bubbles and the pulse width of the echo signal, and introduces the attenuation length of the water and the multipath effect. The experimental results show that when the attenuation length increases from 1.2 to 2.0, the half-peak width of the echoes of the bubbles increases by 1.3ns. However, when the attenuation length increases from 1.5 to 2.2, the echo pulse width of the strong backscatter target decreases by 0.5ns. The thickness of the bubbles increases from 2cm to 5cm, the peak shifts by 0.4ns, and the echo pulse width increases by 0.6ns. The simulation model and experimental results provide an effective basis for distinguishing ship wakes and strong backscattering targets, and have an important role in improving wake detection and recognition capabilities.
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