基于区域电离层高分辨率重构的到达时差定位分析

doi:10.12305/j.issn.1001-506X.2025.11.01

Abstract

Abstract:

Aiming at the short-wave time-difference-of-arrival （TDOA） localization problem, an ionospheric false height correction algorithm based on multi-source data assimilation and convolutional neural network （CNN） reconstruction is proposed. Firstly, this method uses Kalman filtering to assimilate the data from the ionospheric oblique return sounding and global navigation satellite system （GNSS） tomography results. Secondly, the CNN is used instead of ordinary interpolation to obtain the ionospheric parameters under the spatio-temporal grid points required for solving the TDOA localization problem. Finaly, the analytical ray-tracing is used for the integrated estimation of the position of radiation sources and false heights. Based on the oblique measurement results for accuracy analysis, this method has improved the estimation accuracy by 62.93%, 64.54%, and 20.48% compared to GNSS tomography, linear interpolation, and peak height half thickness estimation, respectively. Meanwhile, this paper analyzes the stability of the ionosphere by using the time stationary series test method. By comparing the positioning performance of short-wave TDOA, it is clear that there is a significant positive correlation between the stability of the ionosphere and the positioning performance.

Key words: multi-source data assimilation, convolution neural network （CNN）, regional ionospheric model, short-wave time-difference-of-arrival （TDOA） localization

CLC Number:

TN 957.51

Lingchen KONG, Tongxin LIU, Chen ZHOU, Zhengyu ZHAO. Time-difference-of-arrival localization analysis based on regional ionospheric high resolution reconstruction[J]. Systems Engineering and Electronics, 2025, 47(11): 3521-3530.

Figures/Tables 11

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Time-difference-of-arrival localization analysis based on regional ionospheric high resolution reconstruction

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