Investigation of Performance Boundaries for Full-Waveform in Photon-Counting Lidar

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 ultra-long-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 square 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 256ps, the signal intensity falls within [0.8, 1.6], the tolerable noise is [0, 0.63M]Hz, and the minimum cumulative pulses required is between [282, 319], given that the echo width is 5ns. 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.