Deep learning Achievements and Opportunities in Domain of Electronic Warfare Applications

Ammar, Mohamed A.; Abdel-Latif, Mohamed S.; Badran, Khaled; Hassan, Hossameldin Aboubakr Badr

doi:10.1109/icicis52592.2021.9694245

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…The envisioned "event radio" concept seeks to bring these advantages into the RF processing arena. The belief that similar benefits may be realized for an RF event radio is motivated by (1) the existence of hardware such as the Intel Loihi neuromorphic chip [21] that provides an energy-efficient speed improvement when processing RF signals [22], and (2) the use of computationally efficient deep machine learning algorithms in RF electronic warfare applications [23].…”

Section: Research Motivationmentioning

confidence: 99%

Effects of RF Signal Eventization Encoding on Device Classification Performance

Smith,

Temple,

Dean

2024

Electronics

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The results of first-step research activity are presented for realizing an envisioned “event radio” capability that mimics neuromorphic event-based camera processing. The energy efficiency of neuromorphic processing is orders of magnitude higher than traditional von Neumann-based processing and is realized through synergistic design of brain-inspired software and hardware computing elements. Relative to event-based cameras, the development of event-based hardware devices supporting Radio Frequency (RF) applications is severely lagging and considerable interest remains in obtaining neuromorphic efficiency through event-based RF signal processing. In the Operational Technology (OT) protection arena, this includes efficient software computing capability to provide reliable device classification. A Random Forest (RndF) classifier is considered here as a reliable precursor to obtaining Spiking Neural Network (SNN) benefits. Both 1D and 2D eventized RF fingerprints are generated for bursts from NDev = 8 WirelessHART devices. Average correct classification (%C) results show that 2D fingerprinting is best overall using detected events in burst Gabor transform responses. This includes %C ≥ 90% under multiple access interference conditions using an average of NEPB ≥ 400 detected events per burst. This is sufficiently promising to motivate next-step activity aimed at (1) reducing fingerprint dimensionality and minimizing the required computational resources, and (2) transitioning to a neuromorphic-friendly SNN classifier—two significant steps toward developing the necessary computing elements to achieve the full benefits of neuromorphic processing in the envisioned RF event radio.

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Section: Research Motivationmentioning

confidence: 99%

Effects of RF Signal Eventization Encoding on Device Classification Performance

Smith,

Temple,

Dean

2024

Electronics

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Multi‐target cognitive electronic reconnaissance for unmanned aerial vehicles based on scene reconstruction

Zhang,

You,

Yan

et al. 2024

IET Radar Sonar & Navi

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Model‐free deep reinforcement learning (DRL) is regarded as an effective approach for multi‐target cognitive electronic reconnaissance (MCER) missions. However, DRL networks with poor generalisation can significantly reduce mission completion rates when parameters such as reconnaissance area size, target number, and platform speed vary slightly. To address this issue, this paper introduces a novel scene reconstruction method for MCER missions and a mission group adaptive transfer deep reinforcement learning (MTDRL) algorithm. The algorithm enables quick adaptation of reconnaissance strategies for varied mission scenes by transferring strategy templates and compressing multi‐target perception states. To validate the method, the authors developed a transfer learning model for unmanned aerial vehicle (UAV) MCER. Three sets of experiments are conducted by varying the reconnaissance area size, the target number, and the platform speed. The results show that the MTDRL algorithm outperforms two commonly used DRL algorithms, with an 18% increase in mission completion rate and a 5.49 h reduction in training time. Furthermore, the mission completion rate of the MTDRL algorithm is much higher than that of a typical non‐DRL algorithm. The UAV demonstrates stable hovering and repeat reconnaissance behaviours at the radar detection boundary, ensuring flight safety during missions.

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