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2 results

  • 10 - Lidar Data Analysis

  • Gary G. Gimmestad, Georgia Institute of Technology, David W. Roberts, MicroDynamics LLC
  • Book:
    Lidar Engineering
    Published online:
    16 February 2023
    Print publication:
    23 February 2023, pp 268-296

  • Summary

    Data analysis starts with preprocessing raw lidar data. Algorithms are presented and explained for digital filtering, background subtraction, range correction, and merging profiles from multiple receiver channels or from hybrid analog/digital data systems. Analysis techniques for cloud and aerosol lidar data are then illustrated, with examples of raw and range-corrected data followed by the scattering ratio, which can be used to find the transmittance of a cloud or aerosol layer. Analysis of depolarization data from co-polar and cross-polar receiver channels is discussed, and an algorithm is included for separating aerosol depolarization from the total atmospheric depolarization. Other simple techniques that do not require data inversion are then covered, including the slope method and multi-angle lidar. Finally, elastic backscatter lidar inversions are described, with a derivation of the Klett method for a single-component atmosphere (aerosols). The algorithms for a two-component atmosphere (molecules and aerosols) are presented, along with the limitations of this method.

  • An Improved Initial Alignment Method for Rocket Navigation Systems

  • Lihua Zhu, Xianghong Cheng
  • Journal:
    The Journal of Navigation / Volume 66 / Issue 5 / September 2013
    Published online by Cambridge University Press:
    21 June 2013, pp. 737-749
    Print publication:
    September 2013
    • Article
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  • This paper proposes an algorithm for the initial alignment method for rocket navigation systems. It uses the inertial freezing alignment to resolve the attitude matrix with respect to its fast and robust characteristics. Due to disturbances from the swaying base environment, such as people walking and wind effect, which would consequently result in a great lever arm effect, a Finite Impulse Response (FIR) filter is utilized to decrease the noise in the accelerometers' measurement. In addition, there are sensor errors in the system; the online estimation of gyroscopes' drift with a Kalman filter is adopted to achieve compensation. Numerical results from a simulated rocket initial alignment experiment are reported to demonstrate the effectiveness of the method.