A target behavior pattern mining and abnormal behavior monitoring based on multidimensional similarity metric.

In recent years, electromagnetic spectrum has become an indispensable national strategic resource in the information age and an important strategic support for the development of national informatization. With the rapid proliferation of wireless data today, the demand for electromagnetic spectrum is also growing rapidly, and spectrum resources are limited, leading to the increasingly prominent problem of spectrum scarcity. In response to the traditional spectrum prediction methods for spectrum prediction with extended time and low accuracy, this paper analyzes the spectrum correlation of different channels that are in the same service, performs the similarity measure of frequency dimension data, and proves the correlation between the spectrum; and predicts the spectrum data with different time occupancy, and conducts deep mining of the spectrum target behavior from the time–frequency dimension. The experiments mainly use sequence to sequence (Seq2Seq) based Long Short-Term Memory (LSTM) to predict the data of each occupancy degree, and compare with the traditional LSTM network, Autoregressive Integrated Moving Average model (ARIMA), etc. The prediction accuracy of the method in this paper is higher. At the same time, the abnormal spectrum is monitored using the Mahalanobis distance algorithm, the detection accuracy reaches 100%. [ABSTRACT FROM AUTHOR]