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@ARTICLE{2026Aeros..13..682Y,
       author = {{Yao}, Liang and {Zhang}, Yasheng and {Tao}, Xuefeng and {Zhang}, Zhongtao and {Zhao}, Shuailong and {Jiang}, Yawen},
        title = "{Deriving Thermospheric Density from GNSS POD: An Orthogonal Polynomial Differentiation Approach for Accelerometer-Free LEO Satellites-A High-Fidelity Simulation Study}",
      journal = {Aerospace},
     keywords = {GNSS POD observations, thermospheric mass density, orthogonal polynomial, LEO satellite, space weather, autonomous navigation},
         year = 2026,
        month = jul,
       volume = {13},
       number = {8},
          eid = {682},
        pages = {682},
     abstract = "{Accurate thermospheric mass density is crucial for precision orbit
        determination, trajectory prediction, and collision warning in
        the increasingly congested Low Earth Orbit (LEO) region.
        Traditional high-precision instantaneous inversion methods rely
        on dedicated onboard accelerometers (e.g., CHAMP and GRACE
        satellites), which are costly, scarce, and unable to meet the
        expansive monitoring needs of modern mega-constellations. In
        recent years, orbit data inversion methods based on high-
        precision orbit determination data have been extensively
        studied. Among them, the interpolation--differentiation method,
        although capable of retrieving instantaneous atmospheric
        density, severely amplifies position errors and measurement
        noise, significantly limiting retrieval accuracy. To address
        this gap, this study proposes a novel atmospheric density
        retrieval scheme for accelerometer-free LEO satellites,
        introducing an orthogonal polynomial (Legendre and Chebyshev)
        sliding-window fitting approach. The proposed method is
        evaluated using high-precision simulated orbit data. Under
        instantaneous inversion conditions, the Legendre method yields
        an average relative error (MRE) of 16.55\%, a normalized root
        mean square deviation (NRMSD) of 12.80\%, and a correlation
        coefficient R of 0.9370. To further evaluate its potential in
        climatological modeling, this paper also proposes a local solar
        time (LST) bin-averaging method to assess atmospheric inversion
        accuracy. Under this LST averaging approach, the inversion
        performance achieves an MRE of 6.83\% and a correlation
        coefficient R exceeding 0.98. This demonstrates the feasibility
        of the inversion method for analyzing diurnal effects on
        atmospheric field evolution.}",
          doi = {10.3390/aerospace13080682},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026Aeros..13..682Y},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}