Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T09:52:39.308Z Has data issue: false hasContentIssue false

Estimation of intrapulse modulation parameters of LPI radar under noisy conditions

Published online by Cambridge University Press:  03 December 2021

Chilukuri Raja Kumari*
Affiliation:
Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh 522502, India Department of ECE, VNRVJIET, Hyderabad 500090, India
Hari Kishore Kakarla
Affiliation:
Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh 522502, India
K. Subbarao
Affiliation:
Department of Electronics and Communication Engineering (Retd.), Osmania University, Hyderabad 500007, India
*
Author for correspondence: Chilukuri Raja Kumari, E-mail: [email protected]

Abstract

Low probability of intercept (LPI) radars utilize specially designed waveforms for intra-pulse modulation and hence LPI radars cannot be easily intercepted by passive receivers. The waveforms include linear frequency modulation, nonlinear frequency modulation, polyphase, and polytime codes. The advantages of LPI radar are wide bandwidth, frequency variability, low power, and the ability to hide their emissions. On the other hand, the main purpose of intercept receiver is to classify and estimate the parameters of the waveforms even when the signals are contaminated with noise. Precise measurement of the parameters will provide necessary information about a threat to the radar so that the electronic attack or electronic warfare support system could take instantaneous counter action against the enemy. In this work, noisy polyphase and polytime coded waveforms are analyzed using cyclostationary (CS) algorithm. To improve the signal quality, the noisy signal is pre-processed using two types of denoising filters. The denoised signal is analyzed using CS techniques and the coefficients of spectral correlation density are computed. With this method, modulation parameters of nine types of waveforms up to −12 dB signal-to-noise ratio with an accuracy of better than 95% are extracted. When compared with literature values, it is found that the results are superior.

Type
Radar
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alrubeaan, T, Albagami, K, Ragheb, A, Aldosari, S, Altamimi, M and Alshebeili, S (2019) An investigation of LPI radar waveforms classification in RoF channels. IEEE Access 7, 19.CrossRefGoogle Scholar
Kishore, TR and DeerghaRao, K (2017) Automatic intrapulse modulation classification of advanced LPI radar waveforms. IEEE Transaction on Aerospace and Electronic Systems 53, 901914.CrossRefGoogle Scholar
Vanhoy, G, Schucker, T and Bose, T (2017) Classification of LPI radar signals using spectral correlation and support vector machine. Analog Integrated Circuits and Signal Processing 91, 305313.CrossRefGoogle Scholar
Phillip, EP (2009) Detecting and Classifying Low Probability of Intercept Radars, 2nd Edn. Norwood: Artech House.Google Scholar
Taboada, F, Lima, A, Gau, J, Jarp, AP and Pace, PE (2002) Intercept receiver signal processing techniques to detect low probability of intercept radar signals, Center for Joint Services, Electronic Warfare Naval Postgraduate School, Monterey, Canada.Google Scholar
Chilukuri, RK, Kakarla, HK and Subbarao, K (2020) Estimation of modulation parameters of LPI radar using cyclostationary method. Journal of Sensing and Imaging 21, 120,Google Scholar
Shyam sunder, M (2020) Classification and estimation of modulation parameters of LPI radar signals. Ph.D. thesis, Osmania University, Hyderabad, India.Google Scholar
Stephens, JP (1996) Advances in signal processing technology for electronic warfare. IEEE AES Systems Magazine 11, 3138.CrossRefGoogle Scholar
Singh, AK and Subba Rao, K (2014) Digital receiver based electronic intelligent system configuration for the detection and identification of intra pulse modulated radar signals. Defence Science Journal 64, 152158.CrossRefGoogle Scholar
Ma, Z, Huang, Z, Lin, A and Huang, G (2020) LPI radar waveform recognition based on features from multiple images. Sensors 20, 123.Google ScholarPubMed
Biswal, B, Dash, PK and Biswal, M (2011) Time frequency analysis and FPGA implementation of modified S transform for de-noising. International Journal of Signal Processing, Image Processing and Pattern Recognition 4, 119136.Google Scholar
Shyam Sunder, M and Subbarao, K (2015) Cyclostationary analysis of polytime coded signals for LPI radars. International Journal of Research in Engineering and Technology 04, 544560, Available at http://www.ijret.org.Google Scholar
Liu, Y, Xiao, P, Wu, H and Xiao, W (2015) LPI radar signal detection based on radial integration of Choi-Willliams time-frequency image. IEEE Journal of Systems Engineering and Electronics 26, 973981.CrossRefGoogle Scholar
Bouillaut, L and Sidahmed, M (2002) Cyclostationary approach and bilinear approach: comparison, applications to early diagnosis for helicopter gear box and classification method based on HOCS. Mechanical Systems and Signal Processing 15, 923943,CrossRefGoogle Scholar
Prithivi Raj, V, Saran Kumar, B, Kalaiyarasan, A, Praveen Chandru, P and Nandakumar Singh, N. (2011) Cyclostationary analysis method of spectrum sensing for cognitive radio, IEEE ACCESS, International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronics Systems Technology (Wireless VITAE), 3, pp.1–5, DOI: 10.1109/WIRELESSVITAE.2011.5940821.CrossRefGoogle Scholar
Gardner, WA (1986) The spectral correlation theory of cyclostationary time-series. Signal Processing 11, 1336.CrossRefGoogle Scholar
Lima, AF Jr (2002) Analysis of low probability of intercept (LPI) radar signals using cyclostationary processing, Master's Thesis, Naval Postgraduate School, Monterey, California.Google Scholar
Vellanki, R and Satish Babu, K (2013) Modeling and analysis of LPI radar signal. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) 8, 1926.CrossRefGoogle Scholar
Skolnik, MI (2003) Introduction to Radar Systems, 3rd Edn. New York: McGraw-Hill Education.Google Scholar
Fielding, JE (1999) Polytime coding as a means of pulse compression. IEEE Transaction on Aerospace and Electronic Systems 35, 716721.CrossRefGoogle Scholar
Dombi, J and Dineva, A (2017) Adaptive multi-round smoothing based on the Savitzky-Golay filter. International Workshop Soft Computing Applications 633, 446454.CrossRefGoogle Scholar
Schafer, RW (2011) What is a Savitzky-Golay filter? IEEE Signal Processing Magazine 28, 111117.CrossRefGoogle Scholar
Neves, SR, de Oliveira, A, Serra, R, Segadilha, LE, Monteiro, F and Lopez, J-M (2016) Using wavelet packets to analyze FM LPI radar signals, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), IEEE Xplore, 15. doi: 10.1109/SAM.2016.7569703Google Scholar
Siva Sankara Reddy, V and Thirumala Rao, D (Oct. 2012) Denoising of radar signals by using wavelets and Doppler estimation by S-transform. International Journal of Advancements in Research & Technology 1, 14, ISSN .Google Scholar
Orfanidis, SJ (2010) Introduction to Signal Processing. Prentice-Hall, Inc.,USA, ISBN 0-13-209172-0.Google Scholar
Angrisani, L, Capriglione, D, Cerro, G, Ferrigno, L and Miele, G (2014) The effect of Savitzky-Golay smoothing filter on the performance of a vehicular dynamic spectrum access method, 20th IMEKO TC4 International Symposium and 18th International Workshop on ADC Modelling and Testing Research on Electric and Electronic Measurement for the Economic Upturn Benevento, Italy, pp. 1116–1121, ISBN-14: 978-92-990073-2-7.Google Scholar