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On the efficient computation of range and Doppler data in noise radar

Published online by Cambridge University Press:  04 February 2019

Christoph Wasserzier  and
Gaspare Galati
Christoph Wasserzier*
Affiliation:
Tor Vergata University, via del Politecnico 1, Rome, Italy Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Fraunhoferstr. 20, 53343 Wachtberg, Germany
Gaspare Galati
Affiliation:
Tor Vergata University, via del Politecnico 1, Rome, Italy CNIT, National Inter-University Consortium for Telecommunications
*
Author for correspondence: Christoph Wasserzier christoph.wasserzier@fhr.fraunhofer.de
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Abstract

In noise radar, digital signal processing algorithms for implementing the computation of the Cross Ambiguity Function through range correlation and Doppler compensation call for optimized solutions. In fact, to achieve a high coherent processing gain, they often compute a large amount of data beyond the maximum range and/or the maximum radial velocity of interest, adding useless information. A novel, efficient algorithm, called Range Filter Bank, is proposed to implement a scope-tailored computation of range/Doppler data in continuous emission noise radar. Downstream its theoretical analysis, the algorithm has been applied to a real-life case study based on dedicated field experiments, in which good quality kinematic data of a car moving at various speeds have been extracted.

Keywords

Noise radar technologyrange filter bankrange-doppler mapmoving target detection
Type
MIKON 2018
Information
International Journal of Microwave and Wireless Technologies , Volume 11 , Special Issue 7: MIKON 2018 / EuMW 2018 (Part II) , September 2019 , pp. 584 - 592
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2019 

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References

1Thayaparan, T et al. (2008) Mutual interference and low probability of interception capabilities of noise radar. IET Radar, Sonar & Navigation 2, 294–305.Google Scholar
2Galati, G, Pavan, G and De Palo, F (2016) Compatibility problems related with pulse-compression, solid state marine radars. IET Radar, Sonar & Navigation 10, 791–797.Google Scholar
3Pace, PE (2009) Detecting and Classifying Low Probability of Intercept Radar. Boston: Artech House.Google Scholar
4Wasserzier, C and Galati, G (2018) Advanced range-Doppler processing in noise radar, 22nd International Microwave and Radar Conference (MIKON), pp. 464–467.Google Scholar
5Poullin, D (2005) Passive detection using digital broadcasters (DAB, DVB) with COFDM modulation. IEE Proceedings - Radar, Sonar and Navigation 152, 143–152.Google Scholar
6Sturm, C et al. (2013) Spectrally interleaved multi-carrier signals for radar network applications and multi-input multi-output radar. IET Radar, Sonar & Navigation 7, 261–269.Google Scholar
7Woodward, PM (1953) Probability and Information Theory with Applications to Radar. London: Pergamon Press Ltd.Google Scholar
8Levanon, N and Mozeson, E (2004) Radar Signals. Hoboken, NJ: John Wiley & Sons.Google Scholar
9Richards, MA et al. (2010) Principles of Modern Radar. Edison, NJ: Scitech publishing.Google Scholar
10Kulpa, K (2013) Signal Processing in Noise Waveform Radar. Boston: Artech House.Google Scholar
11Malanowski, M and Kulpa, K (2012) Detection of moving targets with continuous-wave noise radar: theory and measurements. IEEE Transactions on Geoscience and Remote Sensing 50, 3502–3509.Google Scholar
12Min, WK et al. Numerical and experimental verifications of digital correlator model for random noise radar. IEEE Antennas and Propagation Society International Symposium, Charleston, June 2009.Google Scholar
13Moscardini, C et al. (2015) Batches algorithm for passive radar: a theoretical analysis. IEEE Transactions on Aerospace and Electronic Systems 51, 1475–1487.Google Scholar
14Papoulis, A (1977) Signal Analysis. New York: McGraw Hill.Google Scholar
15Dongards, J and Sullivan, F (2000) Guest editors' introduction to the top 10 algorithms. Computing in Science & Engineering 2, 22–23.Google Scholar