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An atmospheric refractivity inversion method based on deep learning is introduced. Atmospheric duct is an anomalous phenomenon of electromagnetic waves in the atmosphere that affects the use of radio equipment, obtaining real-time atmospheric duct information is of great significance for ship communication, navigation and radar detection. In order to achieve a real-time inversion system, a multilayer perceptron (MLP), a quintessential deep learning model is chosen as the inversion method. After trial-and-error, a five-hidden-layer MLP with rectified linear unit activation function is chosen. Since refractivity inversion is a regression problem, the mean-squared error is utilized to construct the loss function, and the adaptive moment estimation (Adam) algorithm is chosen to accelerate the training convergence. A pregenerated database is used to train the MLP, and thus invert the refractivity profile. The results demonstrate the feasibility and efficiency of this MLP-based inversion method.

Orthogonal frequency division multiplex (OFDM) system is a special cognitive radio system that is widely used in military and civilian applications. As a crucial aspect of spectrum monitoring and electronic countermeasures reconnaissance, it is important to identify the OFDM signal. An identification method based on fractal box dimension and pseudo-inverse spectrum (PIS) has been proposed in this paper for the recognition problem of OFDM signal under multipath channel. Firstly, by theoretically analyzing the fractal box dimension of OFDM signal and single carrier (SC) signal, it can be concluded that the fractal box dimension of OFDM signal and SC signal has obvious differences. Thus, the fractal box dimension of the two types of signal is used to discriminate OFDM signal and SC signal. Then, the PIS of an OFDM signal is constructed according to the characteristics of the OFDM signal. Through theoretical analysis and the experimental simulation, it illustrates that the classification feature could be extracted by detecting the periodical peak of the PIS of OFDM signal and used for identifying OFDM signal in the Gaussian noise. Simulation results demonstrate that the proposed algorithm has better performance than the conventional algorithm based on autocorrelation.