This research was a part of the project titled 'Marine digital AtoN information management and service system development(1/5) (20210650)', funded by the Ministry of Oceans and Fisheries, Korea.."ABSTRACT This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN).Converting every signature into an image induces additional computation overhead; further CNN model is trained considering fixed learning rate. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. This paper present an im-proved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies when compared with the existing CNN-based drone classification model.
INDEX TERMSConvolutional neural network, drone detection, micro doppler signature (MDS), millimeter-wave, radar cross-section, unmanned aerial vehicle.
The evolution of the internet has led to the growth of smart application requirements on the go in the vehicular ad hoc network (VANET). (VANET) enables vehicles to communicate smartly among themselves wirelessly. Increasing usage of wireless technology induces many security vulnerabilities. Therefore, effective security and authentication mechanism is needed to prevent an intruder. However, authentication may breach user privacy such as location or identity. Cryptography-based approach aids in preserving the privacy of the user. However, the existing security models incur communication and key management overhead since they are designed considering a third-party server. To overcome the research issue, this work presents an efficient security model namely secure performance enriched channel allocation (S – PECA) by using commutative RSA. This work further presents the commutative property of the proposed security scheme. Experiments conducted to evaluate the performance of the proposed (S – PECA) over state-of-the-art models show significant improvement. The outcome shows that (S – PECA) minimizes collision and maximizes system throughput considering different radio propagation environments.
This paper briefly introduces the background, significance, and development status of 3D radar technology at home and abroad, and then explains the concept, working principle, system composition, and workflow of the radar system. Combined with the current development trend of smart grids, it focuses on the application scope of this technology in the field of transmission line construction, operation, and maintenance. Then, through the specific implementation of the project cases, the daily operation and maintenance of four 500 kV transmission lines in Nanjing have played a certain guiding role. Finally, according to the development trend of smart grid and the actual demand of power system production and business integration, this paper briefly prospects the function expansion of this technology in transmission line operation evaluation, fault analysis and diagnosis, emergency rescue plan formulation, and other fields.
Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models.
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