Fractional Fourier Transform Based Joint Adaptive Subspace Detection for Hyperspectral Anomaly Detection

Zhang, Lili; Cheng, Baozhi; Tan, Shumei; Wang, Yimeng

doi:10.1109/lgrs.2022.3150929

Cited by 5 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The forgetting factor f t in the forgetting gate determines how much information from the previous moment's cell state C t−1 is preserved to the current moment's cell state C t ; the input gate determines how much of the current moment's network input x t is preserved to the cell state C t ; and the output gate determines how much information from C t is output to the current output value h t of the LSTM, whose formula is shown in Equation (10).…”

Section: Wfmentioning

confidence: 99%

“…Traditional electromagnetic spectrum anomaly detection algorithms are based on feature extraction. Kullback-Leibler Divergence [5], Brier's-Score [6], Compressed-Sensing [7], Random-Forest [8], Hidden-Markov [9], Fractional Fourier [10], and Machine Learning [11] have also been used for anomaly detection and achieved certain results. With the continuous development of the artificial intelligence industry, deep learning has demonstrated its powerful data analysis ability and scholars have made some achievements in anomaly detection using deep learning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Anomaly Detection Based on Spatio-Temporal Network Prediction

Peng

Wang

2022

Electronics

View full text Add to dashboard Cite

With the miniaturization of communication devices, the number of distributed electromagnetic devices is increasing. In order to achieve effective management of the electromagnetic spectrum, prediction and anomaly detection of the spectrum has become increasingly critical. This paper proposes an algorithmic framework for detecting spectrum anomalies using deep learning techniques. More specifically, the framework includes spectrum prediction and anomaly detection. We use the sliding window method to divide the time series, construct multi-timescale historical data, and train the model with normal data to have high accuracy spectrum prediction capability. We analyze and determine the discriminant function to distinguish the spectral anomalies by calculating the differences between the predicted and real data. The experimental results show that the proposed method outperforms existing baseline algorithms based on real-world spectrum measurement data and simulated anomaly data.

show abstract

Section: Wfmentioning

confidence: 99%