Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats security-sensitive places like airports and nuclear power plants. The existing drone localization neutralization technologies work on the assumption that has already been detected classified. Although we have observed a tremendous advancement in sensor industry this decade, there is no robust detection classification method...

Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants improving surveillance security measures. Exploiting radio frequency (RF) based drone control communication enables a passive way of detection wide range environments even without favourable line sight (LOS) conditions. In this paper, we evaluate RF classification performance state-of-the-art (SoA) models on new realistic dataset. With the help newly proposed residual Convolutional Neural...

The principal objective behind the development of passive radio system is to detect and localize mini remotely piloted aircraft systems (RPAS) their operators in three dimensions (3D). This paper describes architecture, detection procedure intermediate test results. Goodness-of-Fit (GoF) based spectrum sensing used frequency transmitted signal mini-RPAS its controller. direction arrival (DoA) estimated with MUSIC algorithm. implementation GoF-based wideband one dimensional DoA estimation...

It is well known that OFDM-based systems are susceptible and weak towards subcarrier misalignment, which can lead to erroneous demodulation of the receive signal. In usage OFDM Radar-Communication systems, whether within cooperative or non-cooperative networks, non-overlapping subcarriers assigned all RadCom nodes in vicinity. Even so misalignment still arise from Doppler hardware carrier frequency offset, inter-system interference. Particularly for radar applications, goal interference...

The increasing use of Unmanned Aerial Vehicles (UAVs) in modern civilian and military applications shows the urgency having a robust drone detector that detects unseen RF signals. Ideally, system can also classify known signals from drones. This study aims to develop an incremental-learning framework which signals, further detect novel We propose DE-FEND: Deep residual network-based autoEncoder FramEwork for signal classification, Novelty Detection, clustering. classification novelty...

One of the problems that we face today is illegal use drones. Monitoring RF transmissions has proved to be a reliable technique for detecting and identifying such devices. Although there are some solutions on market detection jamming, they not always successful. The aim this paper analyze typical communications used by drone manufacturers, which usually implement their own algorithms, propose more robust solution would consider range characteristics these protocols have. A wide-band energy...

Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats security-sensitive places like airports and nuclear power plants. The existing drone localization neutralization technologies work on the assumption that has already been detected classified. Although we have observed a tremendous advancement in sensor industry this decade, there is no robust detection classification method...

Neutralizing a drone using protocol-aware RF jammer requires precise knowledge of the occupied spectrum in time and frequency domains. This paper aims to develop an automatic prediction framework utilizing Convolutional Neural Network (CNN) Long Short Term Memory (LSTM) models. We generate synthetic dataset commonly used signal properties parameters, evaluate performance under several realistic scenarios. Our experiment shows that CNN-LSTM model can accurately predict future sequences by...

Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats security-sensitive places like airports and nuclear power plants. The existing drone localization neutralization technologies work on the assumption that has already been detected classified. Although we have observed a tremendous advancement in sensor industry this decade, there is no robust detection classification method...

In this paper, we present an ultra-fast hopping spread spectrum setup that demonstrates the ability of frequency hopped (FHSS) technique to improve multipath resilience. We developed a fast mixer prototype in 65nm CMOS RF process can be added frontend traditional TX-RX pair used setup. Utilizing test show fast-hopping FHSS system decode signals even at −104dBm signal null caused by multipath. With receiver was able QPSK modulated 400Ksymbol/sec with EVM −15.4dB for five ray wireline...

Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants improving surveillance security measures. Exploiting radio frequency (RF) based drone control communication enables a passive way of detection wide range environments even without favourable line sight (LOS) conditions. In this paper, we evaluate RF classification performance state-of-the-art (SoA) models on new realistic dataset. With the help newly proposed residual Convolutional Neural...