English
    Frank V
    Occupation:SMS Broadcaster LV.1
    Introduction:Validating the performance of advanced smart jammer designs. Smart jammers, commonly referred to as deceptive jammers or digital radio frequency memory (DRFM) jammers, based on their technology, are widely used by the defense industry in electronic attack and defense suites to deceive hostile radars and protect friendly assets. Test and measurement solutions from Rohde & Schwarz address all challenges during the design phase, system performance validation and production process. The DRFM technique involves sampling the RF signal, digitally storing and recreating the signal while modifying some or all of the signal parameters based on the desired deception technique.
    Smart Jammer
    2021-06-29 13:54:46 Author:Frank PV(2739)

    Validating the performance of advanced smart jammer designs. Smart jammers, commonly referred to as deceptive jammers or digital radio frequency memory (DRFM) jammers, based on their technology, are widely used by the defense industry in electronic attack and defense suites to deceive hostile radars and protect friendly assets. Test and measurement solutions from Rohde & Schwarz address all challenges during the design phase, system performance validation and production process. The DRFM technique involves sampling the RF signal, digitally storing and recreating the signal while modifying some or all of the signal parameters based on the desired deception technique.

    Since smartphones have been one of the most prior thing for humans, the death rate from car crash has increased massively. Smart Jammer prevents pedestrians to use mobile devices by jamming their signals while acrossing the crosswalk.

    Cellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause spectrum scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, the broadcasting nature of CR and the dominant line-of-sight links of UAV makes the Cognitive-UAV-Radios susceptible to jamming attacks. In this paper, we propose a framework to detect smart jammer, which locates and attacks the UAV commands with low Jamming-to-Signal-Power-Ratio (JSR). Smart jammer is more challenging than the types of jammers that always require high power values. Our work focuses on learning a Dynamic Bayesian Network (DBN) to model and analyze the signals' behaviour statistically. A Markov Jump Particle Filter (MJPF) is employed to perform predictions and consequently detect jamming signals. The results are satisfactory in terms of detection probability and false alarm rate that outperform the conventional Energy Detector approach.

    Telecommunication researchers are focusing on Unmanned Aerial Vehicles (UAVs) due to their attractive features such as dynamic deployment ability, high mobility and availability of Line-of-Sight (LoS) links facilitating wireless broadcast and supporting high data rate transmissions. UAVs are already being studied for 4G LTE (Long Term Evolution) and they are expected to play an important role in the upcoming 5G technology as mentioned in. UAVs can be used as Flying Base Stations for improving reliability, coverage and capacity of wireless networks or as Aerial Users by connecting them to a cellular system. According to the Federal Aviation Administration (FAA) report, the fleet of connected UAVs will be more than doubled from an estimated 1.1 million in 2017 to 2.4 million units by 2022. This huge number of connected UAVs will overcrowd the spectrum bands and lead to spectrum scarcity. Incorporation of Cognitive Radio (CR) and UAVs, which we refer to as the Cognitive-UAV-Radios has been proposed to mitigate the spectrum scarcity problem.

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