# Hardware Resource Optimized Detection of LFM Signals with Unknown Start Frequency and Frequency Rate

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## Abstract

Detection of very low-SNR LFM signals with unknown start frequency and frequency rate is of great interest both in electronic support measure (ESM), and radio astronomy. The direct method for LFM signal detection needs a bank of matched-filters which is a really hardware consuming solution. As another solution, a bank of de-ramping blocks, followed by FFT units, can be used with the same performance as matched-filters bank. In such an alternative solution, with no optimization constraint, it is quite likely to reach a hardware extensive solution with limited processing gain. In this paper, a novel method based on de-ramping bank is proposed. Also, an optimization problem is developed, which could determine the optimum values for detection structure’s parameters, e.g. number of channels, as well as FFT length. It is shown that, the optimized detector features better processing gain in comparison to the non-optimized versions. Furthermore, adding a moving average at the output of the FFT could make remarkable improvement on detection performance. Moreover, the proposed detector is compared against the conventional methods in terms of detection performance and computational complexity characteristic, which aptly prove the superiority of the proposed method.

## Keywords

LFM signal LPI detection Hardware-optimized implementation Low computational complexity## Notes

## References

- 1.Pace, P. E. (2004).
*Detecting and Classifying Low Probability of Intercept Radar*. Norwood: Artech House.Google Scholar - 2.Wiley, R. G. (2006).
*ELINT: The Interception and Analysis of Radar Signals*. Norwood: Artech House.Google Scholar - 3.Waters, W. M., & Jarrett, B. R. (1982). Bandpass Signal Sampling and Coherent Detection.
*IEEE Transactions on Aerospace and Electronic Systems, AES-18*(6), 731–736.CrossRefGoogle Scholar - 4.Tsui, J. B. (2004).
*Digital Techniques for Wideband Receivers*. Lucknow: Institution of Engineering and Technology.Google Scholar - 5.Abatzoglou, T. J. (1986). Fast Maximnurm Likelihood Joint Estimation of Frequency and Frequency Rate.
*IEEE Transactions on Aerospace and Electronic Systems, AES-22*(6), 708–715.CrossRefGoogle Scholar - 6.Djuric, P. M., & Kay, S. M. (1990). Parameter estimation of chirp signals.
*IEEE Transactions on Acoustics, Speech, and Signal Processing, 38*(12), 2118–2126.CrossRefGoogle Scholar - 7.Barbarossa, S. (1995). Analysis of multicomponent LFM signals by a combined Wigner-Hough transform.
*IEEE Transactions on Signal Processing, 43*(6), 1511–1515.CrossRefGoogle Scholar - 8.Cirillo, L., Zoubir, A., & Amin, M. (2008). Parameter Estimation for Locally Linear FM Signals Using a Time-Frequency Hough Transform.
*IEEE Transactions on Signal Processing, 56*(9), 4162–4175.MathSciNetCrossRefGoogle Scholar - 9.Kishore, T.R., D.S. Sidharth, and K.D. Rao (2015).
*Analysis of linear and non-linear frequency modulated signals using STFT and hough transform*. in*2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)*.Google Scholar - 10.Capus, C., & Brown, K. (2003). Short-time fractional Fourier methods for the time-frequency representation of chirp signals.
*The Journal of the Acoustical Society of America, 113*(6), 3253–3263.CrossRefGoogle Scholar - 11.Xiang-Gen, X. (2000). Discrete chirp-Fourier transform and its application to chirp rate estimation.
*IEEE Transactions on Signal Processing, 48*(11), 3122–3133.MathSciNetCrossRefGoogle Scholar - 12.Hamschin, B. M., Ferguson, J. D., & Grabbe, M. T. (2017). Interception of Multiple Low-Power Linear Frequency Modulated Continuous Wave Signals.
*IEEE Transactions on Aerospace and Electronic Systems, 53*(2), 789–804.CrossRefGoogle Scholar - 13.Chen, S., et al. (2017). Chirplet Path Fusion for the Analysis of Time-Varying Frequency-Modulated Signals.
*IEEE Transactions on Industrial Electronics, 64*(2), 1370–1380.CrossRefGoogle Scholar - 14.Bouchikhi, A., et al. (2014). Analysis of multicomponent LFM signals by Teager Huang-Hough transform.
*IEEE Transactions on Aerospace and Electronic Systems, 50*(2), 1222–1233.MathSciNetCrossRefGoogle Scholar - 15.Shing-Chow, C. and H. Ka-Leung.
*Efficient computation of the discrete Wigner-Ville distribution*. in*IEEE International Symposium on Circuits and Systems*. 1990.Google Scholar - 16.
- 17.Yang, J., et al.
*Detecting driver phone use leveraging car speakers*. in*Proceedings of the 17th annual international conference on Mobile computing and networking*. 2011.*ACM.*Google Scholar - 18.Majorkowska-Mech, D., & Cariow, A. (2017). A Low-Complexity Approach to Computation of the Discrete Fractional Fourier Transform.
*Circuits, Systems, and Signal Processing, 36*(10), 4118–4144.CrossRefGoogle Scholar - 19.Dezfuli, A. A., et al. (2018). Reduced complexity and near optimum detector for linear-frequency-modulated and phase-modulated LPI radar signals.
*IET Radar, Sonar and Navigation, 13*(4), 593–600.CrossRefGoogle Scholar