This article, sourced from the ESS Open Archive, presents a technical analysis of iterative signal deconvolution algorithms and their sensitivity to noise when applied to simulated full-waveform LiDAR (Light Detection and Ranging) data. LiDAR technology is a pivotal remote sensing method that provides highly accurate three-dimensional data for various applications, including topographic mapping, forestry, urban planning, and autonomous vehicles. However, LiDAR signals are inherently susceptible to noise, which can degrade data quality and impact the accuracy of derived information. This noise can originate from environmental conditions, sensor limitations, and the target's reflectivity. Full-waveform LiDAR systems, which record the entire backscattered laser pulse, offer more detailed information than discrete-return systems but also present complex processing challenges, especially concerning noise. The research explores how different deconvolution algorithms perform in recovering accurate signal profiles from noisy, simulated full-waveform LiDAR data. Deconvolution is a signal processing technique used to reverse the blurring or smearing effect on signals, theoretically restoring lost resolution. Studies show that algorithms like Richardson-Lucy, Wiener filter, and non-negative least squares are commonly employed for this purpose, with the Richardson-Lucy algorithm often demonstrating superior performance in recovering true cross-sections and detecting subtle features. Mitigating data noise and improving the signal-to-noise ratio is critical for high-precision LiDAR applications. This scientific investigation contributes to the ongoing efforts to develop robust data processing techniques, which are essential for maximizing the utility and reliability of LiDAR data in diverse fields globally, including India, where LiDAR applications are expanding rapidly in infrastructure, agriculture, and smart city projects.