Toward Metacognitive Radars: Concept and Applications

cognitive radar;wireless channels;radar performance;dynamic wireless channel;neurobiological concept;metacognition;metacognitive radar;metacognitive radio;cognitive radar algorithms;metacognitive radars;neurobiological research;antenna selection;transmit-receive hardware;Cognitive radar;cognitive radio;deep learning;metacognitive radar;resource sharing;transfer learning

Abstract :

[en] 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.

Disciplines :

Computer science

Author, co-author :

Mishra, K. V.

Shankar, M. R. B.

Ottersten, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)

External co-authors :

yes

Language :

English

Title :

Toward Metacognitive Radars: Concept and Applications

Publication date :

11 June 2020

Event name :

Toward Metacognitive Radars: Concept and Applications

Event place :

Washington, United States - District of Columbia

Event date :

from 28-04-20 to 30-04-20

Main work title :

2020 IEEE International Radar Conference (RADAR), Toward Metacognitive Radars: Concept and Applications

Pages :

77-82

Peer reviewed :

Peer reviewed

Commentary :

2640-7736

Available on ORBilu :

since 12 January 2021

Statistics

Number of views

86 (2 by Unilu)

Number of downloads

231 (8 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

Bibliography

S. Haykin, “Cognitive radar: A way of the future,” IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30-40, 2006.
J. R. Guerci, Cognitive radar: The knowledge-aided fully adaptive approach. Artech House, 2010.
K. V. Mishra and Y. C. Eldar, “Performance of time delay estimation in a cognitive radar,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2017, pp. 3141-3145.
K. V. Mishra and Y. C. Eldar, “Sub-Nyquist radar: Principles and prototypes,” in Compressed Sensing in Radar Signal Processing, A. D. Maio, Y. C. Eldar, and A. Haimovich, Eds. Cambridge University Press, 2019, pp. 1-48.
A. F. Martone, K. I. Ranney, K. Sherbondy, K. A. Gallagher, and S. D. Blunt, “Spectrum allocation for noncooperative radar coexistence,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 1, pp. 90-105, 2017.
Z. Slavik and K. V. Mishra, “Cognitive interference mitigation in automotive radars,” in IEEE Radar Conference, 2019, in press.
J. W. Owen, B. Ravenscroft, B. H. Kirk, S. D. Blunt, C. T. Allen, A. F. Martone, K. D. Sherbondy, and R. M. Narayanan, “Experimental demonstration of cognitive spectrum sensing & notching for radar,” in IEEE Radar Conference, 2018, pp. 0957-0962.
B. Ravenscroft, J. W. Owen, J. Jakabosky, S. D. Blunt, A. F. Martone, and K. D. Sherbondy, “Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar,” IET Radar, Sonar & Navigation, vol. 12, no. 12, pp. 1466-1475, 2018.
D. Cohen, K. V. Mishra, and Y. C. Eldar, “Spectrum sharing radar: Coexistence via Xampling,” IEEE Transactions on Aerospace Electronic Systems, vol. 29, pp. 1279-1296, 3 2018.
M. Alaee-Kerahroodi, K. V. Mishra, M. R. B. Shankar, and B. Ottersten, “Discrete phase sequence design for coexistence of MIMO radar and MIMO communications,” in IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2019, pp. 1-5.
N. Sharaga, J. Tabrikian, and H. Messer, “Optimal cognitive beamforming for target tracking in MIMO radar/sonar,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 8, pp. 1440-1450, 2015.
K. L. Bell, C. J. Baker, G. E. Smith, J. T. Johnson, and M. Rangaswamy, “Cognitive radar framework for target detection and tracking,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 8, pp. 1427-1439, 2015.
A. M. Elbir, K. V. Mishra, and Y. C. Eldar, “Cognitive radar antenna selection via deep learning,” IET Radar, Sonar & Navigation, vol. 13, no. 6, pp. 871-880, 2019.
X. Jiang, F. Zhou, Y. Jian, and H. Xi, “An optimal POMDP-based anti-jamming policy for cognitive radar,” in IEEE Conference on Automation Science and Engineering, 2017, pp. 938-943.
J. H. Flavell, “Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry.” American psychologist, vol. 34, no. 10, p. 906, 1979.
J. A. Livingston, “Metacognition: An overview,” Educational Resources Information Center, U. S. Department of Education, Tech. Rep. TM034808, 2003.
J. Metcalfe and A. P. Shimamura, Metacognition: Knowing about knowing. MIT press, 1994.
J. Dunlosky and J. Metcalfe, Metacognition. Sage Publications, 2008.
H. Asadi, H. Volos, M. M. Marefat, and T. Bose, “Metacognitive radio engine design and standardization,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 4, pp. 711-724, 2015.
H. Asadi, H. Volos, M. M. Marefat, and T. Bose, “Metacognition and the next generation of cognitive radio engines,” IEEE Communications Magazine, vol. 54, no. 1, pp. 76-82, 2016.
X. Song, P. Willett, S. Zhou, and P. B. Luh, “The MIMO radar and jammer games,” IEEE Transactions on Signal Processing, vol. 60, no. 2, pp. 687-699, 2012.
J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” Ph.D. dissertation, Royal Institute of Technology, Sweden, 2000.
A. Achtziger, S. E. Martiny, G. Oettingen, and P. M. Gollwitzer, “Metacognitive processes in the self-regulation of goal pursuit,” Social metacognition, pp. 121-139, 2012.
A. M. Elbir and K. V. Mishra, “Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks,” arXiv e-prints, 2019.
A. M. Elbir and K. V. Mishra, “Sparse array selection across arbitrary sensor geometries with deep transfer learning,” 2020, under review.
K. V. Mishra, M. R. Bhavani Shankar, V. Koivunen, B. Ottersten, and S. A. Vorobyov, “Toward millimeter wave joint radar communications: A signal processing perspective,” IEEE Signal Processing Magazine, vol. 36, pp. 100-114, 2019.
A. Ayyar and K. V. Mishra, “Robust communications-centric coexistence for turbo-coded OFDM with non-traditional radar interference models,” in IEEE Radar Conference, 2019, in press.
S. H. Dokhanchi, B. S. Mysore, K. V. Mishra, and B. Ottersten, “A mmWave automotive joint radar-communications system,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, pp. 1241-1260, 2019.
G. Duggal, S. Vishwakarma, K. V. Mishra, and S. S. Ram, “Doppler-resilient 802.11ad-based ultra-short range automotive radar,” arXiv preprint arXiv:1902.01306, 2019.
M. Palola, M. Matinmikko, J. Prokkola, M. Mustonen, M. Heikkilä, T. Kippola, S. Yrjölä, V. Hartikainen, L. Tudose, A. Kivinen et al., “Live field trial of licensed shared access (LSA) concept using LTE network in 2.3 GHz band,” in IEEE International Symposium on Dynamic Spectrum Access Networks, 2014, pp. 38-47.
P. Liu, Y. Liu, T. Huang, Y. Lu, and X. Wang, “Cognitive radar using reinforcement learning in automotive applications,” arXiv preprint arXiv:1904.10739, 2019.
K. V. Mishra, A. Martone, and A. I. Zaghloul, “Power allocation games for overlaid radar and communications,” in URSI Asia-Pacific Radio Science Conference (AP-RASC), 2019, pp. 1-4.
A. M. Elbir and K. V. Mishra, “Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks,” arXiv preprint arXiv:1905.03107, 2019.
P. Stoica and A. Nehorai, “MUSIC, maximum likelihood, and Cramér-Rao bound: Further results and comparisons,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, no. 12, pp. 2140-2150, 1990.
R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276-280, 1986.