This paper will propose a new method of measuring path integrated turbulence using Quick Response (QR) codes. The QR turbulence estimation theory will be presented, and results using a normal camera and the Digital Adaptive Optics system under development at the Naval Information Warfare Center Pacific will be explored.
This publication will depict ongoing efforts in development and ground based validation of an aerial transmissometer utilizing a ground control station composed of a collocated altazimuth mounted transmitter and receiver along with a gimbal mounted retroreflector operating on an Un-crewed Aerial System (UAS). The transmitter is composed of multiple super luminous LEDs of different wavelengths. The system measures bulk point-to-point transmission through the atmosphere and enables an investigation into atmospheric species due to wavelength dependent absorption. The measurements will be along dynamic propagation paths and enable the development of hemispherical ground truth datasets.
Digital adaptive optics created using homodyne encoding can mitigate atmospheric turbulence in passive imaging systems. This work demonstrates a self-referencing homodyne interferometry technique that combines the passive imaging utility of multi-frame algorithmic procedures with the single-frame correction capability of the Shack-Hartmann adaptive optics technique. As an expansion of recent progress on three sub-aperture assemblies, this work showcases the latest results from the NIWC Pacific team to include coherently reconstructing 18 sub-apertures.
Digital adaptive optics (DAO) created using homodyne encoding can mitigate atmospheric turbulence in passive imaging systems. This work demonstrates a self-referencing homodyne interferometry technique that combines the passive imaging utility of multiframe algorithmic procedures with the single-frame correction capability of the Shack–Hartmann adaptive optics technique. This paper presents image reconstruction improvements through the addition of (1) phase diversity modulation techniques within the interferometry reconstruction algorithm and (2) temporal image processing techniques applied after the interferometry reconstruction algorithm. By imaging quick response codes through a turbulent air chamber in the laboratory, it was possible to quantify the machine-readable performance gain provided by DAO when compared with a standard imaging camera. Results from this research verify that DAO from homodyne encoding provides turbulence mitigation for single frames of data, paving the way for environmentally robust, high-speed, self-contained imaging systems.
This publication will demonstrate recent advances of a self-referencing homodyne interferometry technique for mitigating atmospheric turbulence. The results will be quantified by using QR codes to document the machine-readable performance gain by using Digital Adaptive Optics when compared to a traditional imaging camera.
This publication will demonstrate recent advances of a self-referencing homodyne interferometry technique for mitigating atmospheric turbulence. The results will be quantified by using QR codes to document the machine-readable performance gain by using Digital Adaptive Optics when compared to a traditional imaging camera.
The paper will present an overview of atmospheric turbulence, turbulence limited imaging, and a Digital Adaptive Optics system that mitigates atmospheric turbulence in passive imaging systems.
This effort designed and tested new algorithms and deployable scintillometer hardware for ocean optical turbulence characterization. Novel features include a hand-deployable design, a non-laser optical source, a rapidly adjustable propagation length, and a collocated multi-instrument environmental sensor package. Undersea testing was contingent on several accomplishments, including developing robust algorithms and data logging methods, integrating compact optics and electronics, and engineering handheld-sized pressure vessels suitable for field experimentation. The test assembly was deployed in 428-m Pacific Ocean water from a small boat. Direct measurements revealed the ocean’s refractive-index structure parameter (Cn2 from 1.9×10−11 m−2/3 to 2.3×10−10 m−2/3) and the inner scale of optical turbulence (l0 from 0.5 mm to 1.5 mm). Onboard temperature, depth, beam attenuation, and backscattering sensors corroborated key regions of interest, namely the thermocline. By integrating turbulence, temperature, depth, attenuation, and backscattering measurements within a single hand-portable assembly, we increased our understanding of ocean optical dynamics while demonstrating the practicality of a low size, weight, and power scintillometer.
The inherent and apparent optical properties (IOPs and AOPs) of seawater limit the performance of free-space optical (FSO), underwater wireless optical communication (UWOC), and imaging systems. Absorption, scattering, and downwelling irradiance are three such properties that influence system performance and often evolve independently. In situ measurements of multiple IOPs and AOPs would provide environmental sensing for fielded optical systems, but such comprehensive measurements are typically expensive or impractical. This effort analyzed existing oceanographic data sets to uncover wavelength-dependent correlations between IOPs, AOPs, test depths, and ocean depths. We then employed machine learning (ML) methods to predict the optical properties of diffuse attenuation (Kd) and backscatter (bb) using beam attenuation (c) and compared these results to ground-truth values. Predicted values of Kd and bb were well matched to their ground-truth data. Furthermore, we demonstrate ML-based Jerlov optical water type classification using beam attenuation as the optical data input. With our methods validated, we collected new optical data sets and processed them using our ML models. Results are promising and indicate future in situ classification and prediction capability. To highlight one practical application, we present a preliminary ML-enabled performance estimator for a generic FSO or UWOC system.
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