Wave Optics Simulator for Lasers in Time-Evolving Turbulence

被引:1
|
作者
Badura, Greg [1 ]
Fernandez, Cody [1 ]
Stewart, John [1 ]
机构
[1] Georgia Tech Res Inst, Atlanta, GA 30332 USA
来源
关键词
atmospheric optical turbulence; laser beam propagation; wave optics simulation; phase screens; PHASE SCREENS; PROPAGATION;
D O I
10.1117/12.2558922
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Atmospheric optical turbulence can be characterized as refractive index variations along a beam's propagation path due to local fluctuations in temperature and humidity. Turbulence randomly perturbs the wavefront of a beam traveling through the medium, leading to effects such as scintillation and beam wandering. Wave optics simulations use phase screens and Fourier techniques to accurately model phase change of light sources as they travel through turbulence. Georgia Tech Research Institute has enhanced the open-source wave optics toolbox known as WavePy to accurately simulate the propagation of a laser beam over a path length of time-evolving horizontal turbulence. The simulation tool incorporates an optimization routine designed to accept scenario parameters and return receiver and source plane sampling parameters that ensure accuracy and fidelity of the simulation output. This simulation tool is designed to minimize the potential for common faults of wave optics simulations, including: phase-wrapping of the atmospheric phase screens over time, energy loss of the beam over the propagation path, and aliasing of scintillation effects at the receiver plane. This simulator has applications towards informing the design of detectors that can accommodate the changing angle of divergence of the beam as it approaches the detector, which is an important consideration for systems such as laser beam rider missile guidance systems. Initial results towards modeling the effects of varying beam parameters and simulation conditions are presented and analyzed.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Holistic Prediction on a Time-Evolving Attributed Graph
    Yamasaki, Shohei
    Sasaki, Yuya
    Karras, Panagiotis
    Onizuka, Makoto
    PROCEEDINGS OF THE 61ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2023): LONG PAPERS, VOL 1, 2023, : 13676 - 13694
  • [22] A tutorial on time-evolving dynamical Bayesian inference
    Tomislav Stankovski
    Andrea Duggento
    Peter V. E. McClintock
    Aneta Stefanovska
    The European Physical Journal Special Topics, 2014, 223 : 2685 - 2703
  • [23] Predicting Path Failure In Time-Evolving Graphs
    Li, Jia
    Han, Zhichao
    Cheng, Hong
    Su, Jiao
    Wang, Pengyun
    Zhang, Jianfeng
    Pan, Lujia
    KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 1279 - 1289
  • [24] Efficient Centrality Monitoring for Time-Evolving Graphs
    Fujiwara, Yasuhiro
    Onizuka, Makoto
    Kitsuregawa, Masaru
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PT II: 15TH PACIFIC-ASIA CONFERENCE, PAKDD 2011, 2011, 6635 : 38 - 50
  • [25] Comparison of access methods for time-evolving data
    Salzberg, B
    Tsotras, VJ
    ACM COMPUTING SURVEYS, 1999, 31 (02) : 158 - 221
  • [26] Exact time-evolving scattering states in open quantum-dot systems with an interaction: discovery of time-evolving resonant states
    Nishino, Akinori
    Hatano, Naomichi
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2024, 57 (24)
  • [27] A tutorial on time-evolving dynamical Bayesian inference
    Stankovski, Tomislav
    Duggento, Andrea
    McClintock, Peter V. E.
    Stefanovska, Aneta
    EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2014, 223 (13): : 2685 - 2703
  • [28] Probabilistic clustering of time-evolving distance data
    Julia E. Vogt
    Marius Kloft
    Stefan Stark
    Sudhir S. Raman
    Sandhya Prabhakaran
    Volker Roth
    Gunnar Rätsch
    Machine Learning, 2015, 100 : 635 - 654
  • [29] Efficient Time-Evolving Stream Processing at Scale
    Liao, Xiaofei
    Huang, Yu
    Zheng, Long
    Jin, Hai
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2019, 30 (10) : 2165 - 2178
  • [30] Anomalous Change Detection in Time-evolving OSNs
    Laleh, Naeimeh
    Carminati, Barbara
    Ferrari, Elena
    2016 15TH IFIP MEDITERRANEAN AD HOC NETWORKING WORKSHOP (MED-HOC-NET 2016), 2016,