The effectiveness of the air target localization method based on time difference of arrival (TDOA) is assessed using passive radar based on the global navigation satellite system (GNSS). The equations of localization and their weighted least squares solution are derived. To determine the weight matrix, an elevation-dependent model and a signal to noise ratio(SNR)-dependent model are introduced. Simulation and outdoor experiment results show that the elevation-dependent model and SNR-dependent model can effectively reduce the position error of the TDOA based air target localization algorithm compared with the unweighted model. When the number of satellites used for localization is fewer than seven, the position performance improves quickly as the number of satellites used increases. However, the improvement trend slows down if more than seven satellites are employed. The evaluation of geometric dilution of precision shows that there is a positive linear correlation between the geometric dilution of precision and position error. In outdoor experiment, the maximum position error of airliners is 206.30 m, and the minimum position error is 13.85 m.

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°.