Detection of typhoons and estimation of eye position using satellite-based GNSS-reflectometry

The singularity of the delay-Doppler map (DDM) is the primary focus of study on typhoons using global navigation satellite system (GNSS) reflectometry. However, due to the presence of ambiguity functions, this singularity is blurred, which affects detection performance. The normalized bistatic radar cross section (NBRCS) is reconstructed from deconvolving DDM and mapped to the spatial domain, based on which the detection of typhoon events and estimation algorithm of eye position is studied. The distribution sequence of NBRCS in the spatial domain can be obtained by reconstructing the DDM time series in the typhoon field. A detector of typhoon features is defined, and a sliding window anomaly detection method based on the confidence interval distance radius is used to detect typhoon events. A typhoon eye position estimation algorithm is proposed, which matches the reconstructed spatial scattering coefficient with the model dataset through a model-matching algorithm to obtain the eye position. The findings demonstrate how the signal-to-noise ratio (SNR) affects the typhoon detection algorithm's detection performance, with higher SNRs indicating greater detection performance. When the SNR is 8, the area under curve (AUC) of the receiver operating characteristic (ROC) curve of the typhoon detection performance is 0.80. The average deviation of the eye position estimation under four SNRs does not exceed 0.3°, and the root mean square deviation does not exceed 0.6°.