The 101-channel full-waveform hyperspectral LiDAR (FWHSL) is able to simultaneously obtain geometric and spectral information of the target, and it is widely applied in 3D point cloud terrain generation and classification, vegetation detection, automatic driving, and other fields. Currently, most waveform data processing methods are mainly aimed at single or several wavelengths. Hidden components are revealed mainly through optimization algorithms and comparisons of neighbor distance in different wavelengths. The same target may be misjudged as different ones when dealing with 101 channels. However, using the gain decomposition method with dozens of wavelengths will change the spectral intensity and affect the classification. In this paper, for hundred-channel FWHSL data, we propose a method that can detect and re-decompose the channels with outliers by checking neighbor distances and selecting specific wavelengths to compose a characteristic spectrum by performing PCA and clustering on the decomposition results for object identification. The experimental results show that compared with the conventional single channel waveform decomposition method, the average accuracy is increased by 20.1%, the average relative error of adjacent target distance is reduced from 0.1253 to 0.0037, and the degree of distance dispersion is reduced by 95.36%. The extracted spectrum can effectively characterize and distinguish the target and contains commonly used wavelengths that make up the vegetation index (e.g., 670 nm, 784 nm, etc.).

Photon counting lidar has emerged as a strong candidate technology for active detection applications because of its advantages of single photon sensitivity and high-ranging accuracy. The timing histogram of a single pixel for photon counting lidar contains the target’s range information, while the laser echo of full-waveform lidar contains abundant structure and reflection information of the target. Based on the previous work of full waveform correction for stationary targets, we propose a new method of full waveform recovery for moving targets, aiming at the issue of obtaining the characteristics of ultra-long-range moving targets under high-flux conditions. Our method achieves full-waveform recovery by means of data preprocessing, motion compensation, and photon waveform correction. Through simulation calculations, we analyze and compare the effectiveness of each step of the method. Compared with the raw histogram, the normalized root mean square error (NRMSE) of the recovery full waveform and the ideal waveform is reduced from 0.137 to 0.032. Furthermore, we validate the algorithm’s robustness. As the speed increases from 5 to 340 m/s, the NRMSE is always less than 0.04. The results indicate that the recovery waveform of targets hardly varies with changes in velocity. For an accumulation of 200 pulses, when the signal photons are 0.019–3 and the signal-to-noise ratio is below 0.033, the algorithm consistently exhibits excellent performance. Besides, we have demonstrated that for single-layer moving targets, multilayer moving targets, and round-trip moving targets, the algorithm has good performance on the recovery of the targets’ full-waveform, and the NRMSE is less than 0.0054. This provides a new idea for obtaining the shape of targets with variable speeds at a single pixel and provides exciting news for applications such as the detection and recognition of ultra-long-range aerial targets and the detection of space debris.

Photon counting LiDAR has revolutionized the field of LiDAR technology with its exceptional single-photon sensitivity and picosecond-level time resolution. It is particularly effective for detecting ultralong-range targets and measuring global ecosystems. Timing histograms and waveforms play vital roles in these applications, as they contain rich structural information about the targets. To systematically explore the performance boundary model for full-waveform applications in photon counting LiDAR, we have developed a model based on underlying theory and feasibility, which overcomes the limitation of detecting fewer than 5% of illumination cycles. By using the cumulative emission pulse number as the objective function, we establish the performance boundary model for full-waveform in photon counting LiDAR, revealing the relationship between the accuracy of the full-waveform and system parameters. The model’s accuracy is verified through theoretical analysis and experimental validation. Subsequently, we utilize Pareto optimality to determine the optimal parameters for the full-waveform performance boundary model. Experimental data indicates that to ensure a normalized root mean squared error (nRMSE) of less than 0.03 between full-waveform and ideal waveform, the performance boundaries are as follows: the optimal time bin width is 256 ps, the signal intensity falls within [0.8, 1.6], the tolerable noise is [0, 0.63 M] Hz, and the minimum cumulative pulses required is between [282, 319], given that the echo width is 5 ns. Finally, we discuss the practical application of the full-waveform performance boundary in photon counting LiDAR for complex target detection scenarios. Under the optimal parameter configuration, the R-square ( ${R^{2}}$ ) between the full-waveform and the ideal waveform consistently exceeds 90%. This work not only expands the range of applications for photon counting LiDAR in the field of full-waveform, but also establishes a strong connection with full-waveform processing algorithms.

Single-photon lidar stands out as a promising technology for long-distance lidar applications, owing to its attributes of single-photon sensitivity and high repetition rate. Existing single-photon lidar systems typically rely on single-point scanning for positioning and tracking, necessitating intricate and precise scanning control. In pursuit of a more concise and efficient positioning, we incorporate the four-quadrant theory to articulate the signal formula of photon detection, and propose a novel single-photon four-quadrant positioning method. Our method, which includes signal preprocessing, compensation for longitudinal motion, extraction of pixel intensity, and acquisition of lateral motion, facilitates motion acquisition and positioning for targets. Through simulation calculations, we analyze and compare the effectiveness of each step of the method. With longitudinal and lateral speeds of 100 m/s and 50 m/s, respectively, the trajectory error is 1.7%, and the average speed error is 1.8%. Moreover, for various verification experiments, the trajectory errors are all below 4.2%, and the average speed errors remain under 5.4%, effectively verifying the validity of our method in acquiring the motion information and positioning of targets. It provides an excellent option for acquiring motion information and tracking small moving targets over long distances.

The challenges of optical interference and attacks are crucial in LiDAR sensors. Random modulation of transmitted laser can effectively suppress interference and counter attacks. The robustness of LiDAR sensors to interference and attacks completely depends on the use of high-quality random numbers. Quantum Random Number Generation (QRNG) provides an excellent solution for true random number generation due to the inherent probability properties of quantum processes. Using the built-in SPAD array of LiDAR to obtain random numbers has become an efficient QRNG solution. However, the current QRNG method only utilizes the temporal information of single photon detection, resulting in low QRNG rates, especially under low light flux conditions. To this end, we propose a method to improve the QRNG rate, which generates quantum random numbers through pixel grouping in the spatial dimension and arrival time difference in the temporal dimension (GpDiff-QRNG). Experimental results indicate that the GpDiff-QRNG method significantly enhances the QRNG rate. In scenarios with a photon event probability of 1%, the QRNG rate of the GpDiff-QRNG method is 37 times higher than that of the traditional Diff-QRNG method. Furthermore, the random sequences generated by the GpDiff-QRNG method exhibit no periodicity and belong to pure random sequences of white noise. Our method greatly expands the application of QRNG for LiDAR anti-interference and anti-attack.

Single-photon counting lidar exhibits distinct advantages in detection sensitivity and spatiotemporal resolution, making it crucial for precision target detection. However, laser point clouds are susceptible to noise interference due to the high sensitivity of single-photon detectors (SPDs), and achieving stable target tracking still requires further research. This paper proposes a dual-branch tracking method (DBTM) for single-photon lidar target detection. First, a multi-layer denoising approach is performed in a coarse-to-fine manner to filter out noise point-clouds, separating the echo data of the moving target from the background interference. Subsequently, a dual-branch bidirectional guidance technique is designed, with a parameter estimation method integrated into the adaptive kernel Kalman filtering algorithm to estimate the target position information. This strategy fully utilizes both target intensity and depth information under typical water mist interference conditions. Simulation results show that the DBTM can still track the target effectively and accurately even under a photon noise rate of 1 MHz. Field experiments demonstrate the real-time ranging and tracking of a ship model at a distance of 84 m under water mist interference conditions, with the positioning error of 0.161 m, which verifies the effectiveness of DBTM. Performance analyses confirm that the DBTM outperforms typical conventional approaches in trajectory fitting, error control, and positioning accuracy. Moreover, the dual-branch structure supports adaptation to diverse feature scenarios and holds significant practical value for the precise positioning of small moving objects at a long range in complex environments.