scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

2021 ◽  
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Network Approach ◽  
Neural Network Approach ◽  
Aerial Vehicles ◽  
Accurate Detection ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance. In this paper, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach.

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

2021 ◽  
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Network Approach ◽  
Neural Network Approach ◽  
Aerial Vehicles ◽  
Accurate Detection ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance. In this paper, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach.

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1678
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Moving Average ◽  
Feature Engineering ◽  
Neural Network Approach ◽  
Accurate Detection ◽  
Channel Input ◽  
Rf Signals ◽  
Frequency Components ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

scholarly journals An optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modelling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Abdulkareem Ahmed ◽  
Biswajeet Pradhan ◽  
Subrata Chakraborty ◽  
Abdullah Alamri ◽  
Chang-Wook Lee
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Traffic Noise ◽  
Vehicular Traffic ◽  
Network Approach ◽  
Neural Network Approach
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