Single layer ionosphere models are frequently used for ionospheric modeling and estimation using GPS measurements from a network of GPS reference stations. However, the accuracies of single layer models are inherently constrained by the assumption that the ionospheric electrons are concentrated in a thin shell located at an altitude of about 350 km above Earth’s surface. This assumption is only an approximation to the physical truth because the electrons are distributed in the entire ionosphere region approximately from 50 to 1,000 km. To provide instantaneous ionospheric corrections for the real-time GPS positioning applications, the ionospheric corrections need to be predicted in advance to eliminate the latency caused by the correction computation. This paper will investigate ionospheric total electron content (TEC) predictions using a multiple-layer tomographic method for ionospheric modeling over a local area GPS reference network. The data analysis focuses on the accuracy evaluation of short-term (5 min in this study) TEC predictions. The results have indicated that the obtainable TEC prediction accuracy is at a level of about 2.8 TECU in the zenith direction and 95% of the total electron content can be recovered using the proposed tomography-based ionosphere model.

Ionospheric TEC predictions over a local area GPS reference network

Ionospheric variations over Chinese EIA region using foF2 and comparison with IRI-2016 model

The international reference ionosphere (IRI) model is a generally accepted standard ionosphere model. It describes the ionosphere environment in quiet state and predicts the ionosphere parameters within a certain precision. In this communication, we have made a breakthrough in the application of the IRI model by modifying the model for Chinese region. Construct the ionosphere parameters foF2 and M(3000)F2 by using the Chinese Reference Ionosphere (CRI) coefficients, appropriately increase hmE, hmF2 height, reduce the thickness of F layer, validate the parameter by the measured values and solve the electron concentration distribution with QPS (quasi-parabolic segments). In this communication, 3D ray tracing algorithm was constructed based on the modified IRI model and IGRF geomagnetic field model. In short-wave detection, it can be used to predict the electromagnetic parameters of the receiving point, such as, the receiving area, maximum available frequency and the distribution of the group delay etc., which can help to determine the suitability of the communication. As an example, we estimated the short wave EM parameters around Qingdao in the detection from Zhengzhou to Qingdao using the modified IRI and IGRF model and provided technical support for the communication between the two cities.

The HF Channel EM Parameters Estimation Under a Complex Environment Using the Modified IRI and IGRF Model

High frequency (HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on long-term and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.

Deep spectrum prediction in high frequency communication based on temporal-spectral residual network

Since the narrowband ionospheric channel model is applicable only to a limited set of actual conditions, in 1997 the Institute for Telecommunication Science (ITS) (Vogler and Hoffmeyer) developed a wideband HF ionospheric model, the so-called ITS model. However, the widely used ITS model has some restrictions in application: the parameters are complex and difficult to obtain; only the undisturbed situation is considered; and the multipath delay is fixed and cannot represent the stochastic behavior of the ionosphere. In this paper, the ITS model is modified by introducing the International Reference Ionosphere (IRI) and adding a parameter amending module to provide more accurate parameters, and based on the ITS mathematical and modeling structures, a wideband HF random time-varying ionosphere channel model is presented. With this model, the characteristics of the ionospheric channel under different conditions have been analyzed, and the simulation results show that it can represent the ionospheric channel more accurately and has good consistence with the measured data.

Prediction of the HF Ionospheric Channel Stability Based on the Modified ITS Model

In this paper, an artificial-neural-network (ANN) method is employed to predict the HF-communication MUF (maximum useable frequency) in the region of the South China Sea. Historical observation data of f/sub 0/F/sub 2/ from six ionospheric observatories around the South China Sea are used to train the network. The comparison of ANN predictions and International Reference Ionosphere (IRI) models with the observations shows that the ANN predictions are closer to the observed results than are the IRI predictions.

Zhang Xun-Jie

Papers

Predictions of HF communication MUF in the region of the South China Sea