Cognitive passive radar system: software defined radio and deep learning approach. Issue 21 (20th September 2019)

• Record Type: Journal Article
• Authors: Wang, Qing; Du, Panfei; Dou, Tongdong; Gao, Lirong; Li, Chun
• Is Part Of: Journal of engineering. Volume 2019:Issue 21(2019)
• Page Start: 7326
• Page End: 7330
• Publication Date: 2019-09-20
• Subjects: passive radar -- optimisation -- adaptive signal processing -- software radio -- learning (artificial intelligence) -- cognitive radar -- convolutional neural nets -- recurrent neural nets -- signal classification -- electromagnetic wave propagation -- waveform analysis -- radar computing; software defined radio platform -- wideband signal processing -- flexible signal processing -- deep learning framework -- long short‐term memory -- innovative passive perception‐action cycle -- CPR prototype system -- adaptive signal processing ability -- cognitive passive radar system -- electromagnetic environment cooperating -- optimal strategy -- illumination source availability -- waveform evaluation -- performance gain -- multidimension optimisation -- passive radar illumination source -- multistandard multischeme compatible wideband CPR framework -- electromagnetic waveform recognition -- classifier -- convolutional neural networks -- CNN -- LSTM -- real‐time high accuracy modulation recognition ability; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0628
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : Cognitive passive radar (CPR) system is amenable to electromagnetic environment cooperating and optimal strategy making tasks on account of illumination source availability and waveform evaluation. In fact, intelligence will provide performance gain from multi‐dimension optimisation. Therefore, we propose a multi‐standard multi‐scheme compatible wideband CPR framework which selects the most suitable waveform as the passive radar illumination source. First, we develop a software defined radio platform to realise wideband and flexible signal processing, which provides the feasibility of cognition. Then in order to recognise the electromagnetic waveforms, we present a deep learning framework which can be integrated in CPR system as a classifier using convolutional neural networks (CNNs) and long short‐term memory (LSTM). Via the proposed innovative passive perception‐action cycle, the CPR prototype system demonstrates the real‐time, high accuracy modulation recognition ability and the adaptive signal processing ability. Based on our experiments, it can be demonstrated that our CPR prototype system integrated with deep learning is effective and promising.