Stacking data analysis method for Langmuir multi-probe payload

There are numerous small-scale electron density irregularities in the ionosphere. The coordination of multiple needle Langmuir probes (m-NLPs) enables in situ measurement of electron density with high spatial resolution. However, the theoretical analysis method based on orbital motion-limited (OML) theory cannot accurately estimate electron density, even at higher resolutions, due to limitations in satellite measurements. In addition, due to the influence of satellite charging and flight wake, the currents collected between multi-probes have low consistency, introducing significant error into the measurement results. This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (N e ) values. The integrated characteristics of the stacking model make full use of the advantages of various models such as multilayer perceptron (MLP), support vector regression (SVR), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). The combination of integrated machine learning methods and IRI models greatly improves the accuracy of electron density measurements obtained by m-NLPs. The results indicate that even with poor consistency among the currents collected by multiple probes, the coefficient of determination (R 2 ) of the prediction results using this method can reach 0.9553, which is 0.5079 higher than that of the traditional diagnostic method.