Toward Metacognitive Radars: Concept and Applications

We introduce a metacognitive approach to optimize the radar performance for a dynamic wireless channel. Similar to the origin of the cognitive radar in the neurobiological concept of cognition, metacognition also originates from neurobiological research on problem-solving and learning. Broadly defined as the process of learning to learn, metacognition improves the application of knowledge in domains beyond the immediate context in which it was learned. We describe basic features of a metacognitive radar and then illustrate its application with some examples such as antenna selection and resource sharing between radar and communications. Unlike previous works in communications that only focus on combining several existing algorithms to form a metacognitive radio, we also show the transfer of knowledge in a metacognitive radar. A metacognitive radar improves performance over individual cognitive radar algorithms, especially when both the channel and transmit/receive hardware are changed.

[1]  Anthony F. Martone,et al.  Spectrum Allocation for Noncooperative Radar Coexistence , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Yonina C. Eldar,et al.  Spectrum Sharing Radar: Coexistence via Xampling , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Yonina C. Eldar,et al.  Performance of time delay estimation in a cognitive radar , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  R E Chesley,et al.  The Educational Resources Information Center , 1979, Exceptional children.

[5]  Anthony Martone,et al.  Power Allocation Games for Overlaid Radar and Communications , 2019, 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC).

[6]  S. Haykin,et al.  Cognitive radar: a way of the future , 2006, IEEE Signal Processing Magazine.

[7]  Bjorn Ottersten,et al.  Toward Millimeter-Wave Joint Radar Communications: A signal processing perspective , 2019, IEEE Signal Processing Magazine.

[8]  Ahmet M. Elbir,et al.  Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks , 2019, IEEE Transactions on Wireless Communications.

[9]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[10]  Michael M. Marefat,et al.  Metacognition and the next generation of cognitive radio engines , 2016, IEEE Communications Magazine.

[11]  Kumar Vijay Mishra,et al.  Robust Communications-Centric Coexistence for Turbo-Coded OFDM with Non-Traditional Radar Interference Models , 2019, 2019 IEEE Radar Conference (RadarConf).

[12]  Tianyao Huang,et al.  Cognitive Radar Using Reinforcement Learning in Automotive Applications , 2019, 1904.10739.

[13]  Joseph Tabrikian,et al.  Optimal Cognitive Beamforming for Target Tracking in MIMO Radar/Sonar , 2015, IEEE Journal of Selected Topics in Signal Processing.

[14]  Jarkko Paavola,et al.  Live field trial of Licensed Shared Access (LSA) concept using LTE network in 2.3 GHz band , 2014, 2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN).

[15]  J. Flavell Metacognition and Cognitive Monitoring: A New Area of Cognitive-Developmental Inquiry. , 1979 .

[16]  Joel T. Johnson,et al.  Cognitive Radar Framework for Target Detection and Tracking , 2015, IEEE Journal of Selected Topics in Signal Processing.

[17]  Peter B. Luh,et al.  The MIMO Radar and Jammer Games , 2012, IEEE Transactions on Signal Processing.

[18]  Michael M. Marefat,et al.  Metacognitive Radio Engine Design and Standardization , 2015, IEEE Journal on Selected Areas in Communications.

[19]  Kumar Vijay Mishra,et al.  Cognitive Interference Mitigation in Automotive Radars , 2019, 2019 IEEE Radar Conference (RadarConf).

[20]  Ahmet M. Elbir,et al.  Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning , 2020, ArXiv.

[21]  J. Livingston,et al.  Metacognition: An Overview. , 2003 .

[22]  J. Metcalfe,et al.  Metacognition : knowing about knowing , 1994 .

[23]  J. R. Guerci,et al.  Cognitive radar: A knowledge-aided fully adaptive approach , 2010, 2010 IEEE Radar Conference.

[24]  Bjorn Ottersten,et al.  A mmWave Automotive Joint Radar-Communications System , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Yonina C. Eldar,et al.  Cognitive radar antenna selection via deep learning , 2018, IET Radar, Sonar & Navigation.

[26]  Ram M. Narayanan,et al.  Experimental demonstration of cognitive spectrum sensing & notching for radar , 2018, 2018 IEEE Radar Conference (RadarConf18).

[27]  Yonina C. Eldar,et al.  Sub-Nyquist Radar: Principles and Prototypes , 2018, Compressed Sensing in Radar Signal Processing.

[28]  Anja Achtziger,et al.  Metacognitive Processes in the Self-Regulation of Goal Pursuit , 2012 .

[29]  Anthony F. Martone,et al.  Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar , 2018 .

[30]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[31]  Petre Stoica,et al.  MUSIC, maximum likelihood, and Cramer-Rao bound: further results and comparisons , 1990, IEEE Trans. Acoust. Speech Signal Process..

[32]  Björn E. Ottersten,et al.  Discrete-Phase Sequence Design for Coexistence of MIMO Radar and MIMO Communications , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[33]  Kumar Vijay Mishra,et al.  Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Radar , 2019 .

[34]  Jian Yang,et al.  An optimal POMDP-based anti-jamming policy for cognitive radar , 2017, 2017 13th IEEE Conference on Automation Science and Engineering (CASE).