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A Survey on Detection, Classification, and Tracking of Aerial Threats using Radar and Communications Systems
Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U.
A Survey on Detection, Tracking, and Classification of Aerial Threats using Radars and Communications Systems
Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are provided.
Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors
Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones.
Sub-Terahertz and mmWave Penetration Loss Measurements for Indoor Environments
In this work, we measured the penetration loss and the attenuation of several common constructional materials at mmWave (28 and 39 GHz) and sub-THz (120 and 144 GHz) bands.
28 GHz Indoor and Outdoor Propagation Analysis at a Regional Airport
What is more, in non-line-of-sight (NLOS) propagation, the attenuation of mmWave is larger compared to the lower frequencies, which can seriously degrade the performance.
Base Station and Passive Reflectors Placement for Urban mmWave Networks
Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area and the gNB placement aided with PMRs requires fewer gNBs to cover the same area, which in turn reduces the deployment cost.
EMR: A New Metric to Assess the Resilience of Directional mmWave Channels to Blockages
Millimeter-wave (mmWave) communication systems require narrow beams to increase communication range.
RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks
As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily.
RSS-Based Q-Learning for Indoor UAV Navigation
Received signal strength (RSS) at the UAV, which is a function of the UAV and source positions, is fed to a Q-learning algorithm and the UAV is navigated to the vicinity of the source.
