Free‑space optical communication (FSO-Com) systems are strongly affected by turbulence‑induced signal fading, which limits link performance and reliability. This paper presents an approach for time-ahead forecasting of received fiber‑coupled laser power signals at FSO-Com terminals using temporal convolutional networks (TCNs), aimed at proactive mitigation of atmospheric turbulence effects. The study utilized atmospheric field measurements obtained over a 7-km propagation path and synthetic time-series datasets generated using wave-optics numerical modeling and simulation. The developed TCN architecture demonstrated the capability to accurately forecast received optical power fluctuations 10–20 timesteps ahead with average inference time of approximately 1.2 ms per forecast. The results demonstrate the potential of TCN‑based received signal processing for enhancing FSO-Com link performance and reliability.

In this paper, we present the results of an experimental analysis of atmospheric optical turbulence dynamics during the 2024 total solar eclipse (TSE). Changes in atmospheric optical turbulence strength throughout the entire TSE event were investigated by measuring the refractive index structure parameter (Cn2) and analyzing the corresponding short-exposure intensity scintillation patterns of a laser beam propagating over a 7 km near-ground atmospheric path. Data collection was conducted using a commercial large-aperture scintillometer and the recently developed AI-based TurbNet sensor, which enables high temporal resolution for Cn2 evaluation. The observed Cn2 parameter time evolution was characterized by the presence of dual turbulence quiescent (neutral stratification) phases and the generation of quasi-periodic oscillatory patterns during totality and for several minutes afterward.

In this paper, we introduce atmospheric adaptive optics (AO) system architectures that utilize scintillation-resistant wavefront sensors based on iterative phase retrieval (IPR) techniques (described in detail in Part I) for closed-loop mitigation of atmospheric turbulence-induced wavefront aberrations in strong intensity scintillation conditions. The objective is to provide a framework (mathematical and numerical models, performance metrics, control algorithms, and wave-optics modeling and simulation results) for the potential integration of IPR-based wavefront sensing techniques into the following major atmospheric optics system types: directed energy laser beam projection, remote laser power delivery (remote power beaming), and free-space optical communications. Theoretical analysis and numerical simulation results demonstrate that the proposed closed-loop AO system architectures and control algorithms can be uniquely applicable for addressing one of the most challenging AO problems of turbulence effects mitigation in the presence of strong-intensity scintillations.

The objective of this study, which is divided into two parts, is twofold: to address long-standing challenges in the sensing of atmospheric turbulence-induced wavefront aberrations under strong scintillation conditions via a comparative analysis of several basic scintillation-resistant wavefront sensing (SR-WFS) architectures and iterative phase retrieval (IPR) techniques (Part I, this paper), and to develop a framework for the potential integration of SR-WFS techniques into practical closed-loop non-astronomical atmospheric adaptive optics (AO) systems (Part II). In this paper, we consider basic SR-WFS mathematical models and phase retrieval algorithms, tradeoffs in sensor design and phase retrieval technique implementation, and methodologies for WFS parameter optimization and performance assessment. The analysis is based on wave-optics numerical simulations imitating realistic turbulence-induced phase aberrations and intensity scintillations, as well as optical field propagation inside the SR-WFSs. Several potential issues important for the practical implementation of SR-WFS and IPR techniques, such as the requirements for phase retrieval computational grid resolution, tolerance with respect to optical element misalignments, and the impact of camera noise and input light non-monochromaticity, are also considered. The results demonstrate that major wavefront sensing requirements desirable for AO operation under strong intensity scintillations can potentially be achieved by transitioning to novel SR-WFS architectures, based on iterative phase retrieval techniques.

We emphasize the requirement for a validated simulation environment to assess the impact of changes in atmospheric turbulence strength throughout the day and year on the performance of atmospheric electro-optics (EO) systems such as directed energy, power beaming, and laser communications. In the proposed approach, site-specific data from persistent measurements of atmospheric parameters, for instance, turbulence strength, represent the variability of atmospheric propagation conditions. Together with atmospheric models and numerical weather predictions, the measured data are utilized to build atmospheric propagation models for wave-optics simulations of atmospheric EO systems. We demonstrate this approach with a database of continuous (24/7) C-n(2) and meteorological measurements on a 7 km atmospheric propagation path at the University of Dayton test range for predictive modeling of laser beam propagation parameters and their fluctuations during diurnal and monthly cycles. We analyze a specific laser beam director system configuration, both with and without the implementation of adaptive optics beam control. The proposed technique provides a pathway to a comprehensive predictive performance evaluation of diverse atmospheric EO systems, allowing for statistical and interactive parametric analysis.

The impact of the laser beacon size on the performance of an ideal phase-conjugation-type adaptive optics (AO) system is analyzed using wave-optics-based numerical simulation. The analysis includes wavefront aberration sensing and closed-loop control for laser beam propagation both in vacuum and in distributed (volume) atmospheric turbulence with the beacon beam scattering off a flat extended target with Lambertian surface roughness. For mitigation of the impact of target-induced speckle effects on the performance of AO systems, speckle-average wavefront sensing and control approaches are introduced and analyzed. The results demonstrate that proposed speckle-average phase conjugation control algorithms enable partial mitigation of turbulence-induced aberrations in presence of strong speckle modulations.

A deep neural network (DNN) model designed for the refractive index structure parameter Cn2 prediction based on processing of pupil and focal plane laser beam intensity patterns was used for analysis of turbulence inner and outer scale impact on spatial features of intensity and wavefront phase. Wave-optics numerical simulations were used to generate large datasets of short-exposure intensity distributions in an optical receiver pupil and focal planes for a remotely (7 km) located Gaussian laser beacon under various turbulence distributions and turbulence inner and outer scales. These datasets were used for DNN model training, validation, and inference.

Atmospheric turbulence strength (Cn2 parameter) sensing based on processing of intensity scintillation patterns with deep neural network (DNN) is considered. It is shown that DNN re-training with propagation distance change can be avoided by scaling of Cn2 values obtained using a DNN trained for a nominal distance L0 . The required Cn2 scaling factor can be obtained using either an analytical expression derived from the Kolmogorov turbulence theory (theory-based scaling), or through wave-optics numerical modeling and simulations (M&S-based scaling).

A deep machine learning-based electro-optics system (TurbNet sensor) was developed to measure atmospheric turbulence refractive index structure parameter (C2n) at a high temporal resolution by processing short-exposure intensity scintillation patterns. The TurbNet sensor was composed of a remotely located LED beacon, an optical receiver telescope with a CCD camera for capturing short exposure pupil-plane intensity scintillation patterns, and a Jetson Xavier Nx embedded AIcomputing platform to implement the deep neural network (DNN)-based processing of LED beam scintillation images. Performance of the TurbNet sensor was evaluated over a 7 km atmospheric propagation path.

This study introduces electro-optical (EO) sensors (TurbNet sensors) that utilize a remote laser beacon (either coherent or incoherent) and an optical receiver with CCD camera and embedded edge AI computer (Jetson Xavier Nx) for in situ evaluation of the path-averaged atmospheric turbulence refractive index structure parameter Cn2 at a high temporal rate. Evaluation of Cn2 values was performed using deep neural network (DNN)-based real-time processing of short-exposure laser-beacon light intensity scintillation patterns (images) captured by a TurbNet sensor optical receiver. Several pre-trained DNN models were loaded onto the AI computer and used for TurbNet sensor performance evaluation in a set of atmospheric propagation inference trials under diverse turbulence and meteorological conditions. DNN model training, validation, and testing were performed using datasets comprised of a large number of instances of scintillation frames and corresponding reference (“true”) Cn2 values that were measured side-by-side with a commercial scintillometer (BLS 2000). Generation of datasets and inference trials was performed at the University of Dayton’s (UD) 7-km atmospheric propagation test range. The results demonstrated a 70–90% correlation between Cn2 values obtained with the TurbNet sensors and those measured side-by-side with the scintillometer.

The non-stationary and non-uniform laser heating of a material object is considered. Two computationally efficient reduced complexity approaches of numerical solution of the heat transfer equation are suggested. The first one is based on spatial Fourier transform. The second is based on the analytical solution for Gaussian functions. Both methods result in temporal dynamics of the 3-dimensional temperature distribution in the object material. Suggested approaches are illustrated with two examples of calculations for single- and multi-beam laser systems. The calculations for these examples on a standard desktop computer take several seconds, which is much less than it could take with standard finite differences approach.

An electro-optics system (TurbNet sensor) composed of a remotely located laser beacon, optical receiver telescope with a CCD camera capturing short-exposure intensity scintillation patterns, and deep neural network (DNN)-based processor implemented on Jetson Xavier Nx embedded AI-computing platform was developed and utilized for real-time sensing of the atmospheric turbulence refractive index structure parameter (2Cn) over a 7 km propagation path at high temporal resolution.

A deep machine learning signal processing paradigm is applied for prediction of the atmospheric turbulence refractive index structure parameter Cn^2 via DNN-based processing of data received from electro-optical sensors and evaluation of the most prominent turbulence models.

New capabilities for atmospheric turbulence spatio-temporal dynamics characterization and refractive index structure parameter ( C n 2 ) sensing via white light imaging of remotely located non-cooperative targets using an optical system with a neuromorphic (event) based sensor are demonstrated through a set of experimental trials conducted over a 7 km imaging path and proposed event data processing technique.

A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric turbulence theoretical models and to evaluate them against the experimentally measured data.

A concept of atmospheric turbulence characterization using laser light backscattered off a moving unresolved target or a moving target with a glint is considered and analyzed through wave-optics numerical simulations. The technique is based on analysis of the autocorrelation function and variance of the power signal measured by the target-in-the-loop atmospheric sensing (TILAS) system composed of a single-mode-fiber-based optical transceiver and the moving target. It is shown that the TILAS received power signal autocorrelation function strongly depends on the turbulence distribution and is weakly sensitive to the turbulence strength, while the signal variance equally depends on these parameters. Assuming the atmospheric turbulence model can be represented by a single spatially localized turbulence layer and the target position and speed are known independently, consecutive analysis of the autocorrelation function and variance of the TILAS signal allows evaluation of both the turbulence layer strength and position along the optical propagation path. It is also demonstrated that the autocorrelation function can potentially be used for the atmospheric turbulence outer scale estimation.

A new class of multi-aperture phase contrast (MAPCO) complex field sensor is introduced and analyzed using wave-optics numerical simulations. The presented analysis of the MAPCO technique demonstrates a potential for high-resolution (256 x 256 pixels) complex field sensing under strong input field intensity scintillations with sub-msec phase retrieval frame rates.