As a typical application of indirect-time-of-flight (ToF) technology, photonic mixer device (PMD) solid-state array Lidar has gained rapid development in recent years. With the advantages of high resolution, frame rate and accuracy, the equipment is widely used in target recognition, simultaneous localization and mapping (SLAM), industrial inspection, etc. The PMD Lidar is vulnerable to several factors such as ambient light, temperature and the target feature. To eliminate the impact of such factors, a proper calibration is needed. However, the conventional calibration methods need to change several distances in large areas, which result in low efficiency and low accuracy. To address the problems, this paper presents an improved calibration method based on electrical analog delay. The method firstly eliminates the lens distortion using a self-adaptive interpolation algorithm, meanwhile it calibrates the grayscale image using an integral time simulating based method. Then, the grayscale image is used to estimate the parameters of ambient light compensation in depth calibration. Finally, by combining four types of compensation, the method effectively improves the performance of depth calibration. Through several experiments, the proposed method is more adaptive to multiscenes with targets of different reflectivities, which significantly improves the ranging accuracy and adaptability of PMD Lidar.

The accuracy of target distance obtained by a frequency modulated continuous wave (FMCW) laser ranging system is often affected by factors such as white Gaussian noise (WGN), spectrum leakage, and the picket fence effect. There are some traditional spectrum correction algorithms to solve the problem above, but the results are unsatisfactory. In this article, a decomposition filtering-based dual-window correction (DFBDWC) algorithm is proposed to alleviate the problem caused by these factors. This algorithm reduces the influence of these factors by utilizing a decomposition filtering, dual-window in time domain and two phase values of spectral peak in the frequency domain, respectively. With the comparison of DFBDWC and these traditional algorithms in simulation and experiment on a built platform, the results show a superior performance of DFBDWC based on this platform. The maximum absolute error of target distance calculated by this algorithm is reduced from 0.7937 m of discrete Fourier transform (DFT) algorithm to 0.0407 m, which is the best among all mentioned spectrum correction algorithms. A high performance FMCW laser ranging system can be realized with the proposed algorithm, which has attractive potential in a wide scope of applications.

To solve the poor real-time performance of the existing fault diagnosis algorithms on transmission system rotating components, this paper proposes a novel high-dimensional OT-Caps (Optimal Transport–Capsule Network) model. Based on the traditional capsule network algorithm, an auxiliary loss is introduced during the offline training process to improve the network architecture. Simultaneously, an optimal transport theory and a generative adversarial network are introduced into the auxiliary loss, which accurately depicts the error distribution of the fault characteristic. The proposed model solves the low real-time performance of the capsule network algorithm due to complex architecture, long calculation time, and oversized hardware resource consumption. Meanwhile, it ensures the high precision, early prediction, and transfer aptitude of fault diagnosis. Finally, the model’s effectiveness is verified by the public data sets and the actual faults data of the transmission system, which provide technical support for the application.

Correlation-based time-of-flight (ToF) imaging enables a diverse range of applications for its high frame rate, high resolution and low cost. However, the non-uniformity of the sensor significantly affects the flat-field accuracy of the ToF imaging system. In this paper, we analyze the sources of the non-uniformity and propose a systematic non-uniformity correction (NUC) method. The method utilizes the amplitude image, which can directly reflect the non-uniformity characteristics of the ToF sensor, to conduct NUC. Based on the established NUC system, the effectiveness and feasibility of the proposed NUC method are verified. Compared with the traditional methods, the RMSE was significantly reduced, while the SNR and PSNR were effectively improved. We believe this study provides new insights into the understanding of noise in the correlation-based ToF imaging system, and also provides effective references for the NUC of the three-dimensional measuring instruments.

The quality of depth maps acquired by a time-of-flight three-dimensional ghost imaging (3DGI) system is limited by dynamic ambient light and electrical noise. We developed a novel method that integrates the differential-correlation-sampling (DCS) method and a modulated continuous-wave laser source to realize the 3DGI and reduce the noise influence. The simulation results for the proposed method, DCS-3DGI, verify its feasibility. The analysis of mean-square-error, peak signal-to-noise ratio, structural similarity index measure, and edge preservation index demonstrates a superior anti-interference performance than conventional 3DGI methods.