NOISE FEATURES OF BREATH AND HEARTBEAT INFORMANT SIGNALS
Abstract
Subject and Purpose. The subject of research is the flicker noise present in informant signals of search-and-rescue radars, specifically its properties and the effect it may have on algorithms for detecting and identifying manifestations of human breath and heartbeat processes during rescue operations. The work has been aimed at creating a suitable description of flicker noise for developing optimal algorithms of digital signal processing for quick detection and identification of informant signals during rescue missions.
Method and Methodology. The low-frequency flicker noise has been modeled within the polynomial equations technique, proceeding from an analysis of real data on noise components in the output signals from a coherent search-and-rescue radar. A comparative analysis is done for a variety of approximating functions suggested for representing the low frequency portion of the spectrum observed.
Results. For the low-frequency range wherein spectral components of the informative signal owing to respiration and heartbeat of humans are concentrated, an adequate model of the fluctuating interference is the flicker noise model built on the basis of polynomial equations. The problem of optimized model representation of the noise in digital signal processing algorithms has been analyzed for the case of a coherent search-and-rescue radar. A model of the fluctuating process has been suggested, based on a polynomial approximation for the spectral function in the low-frequency range of the signals observed at the radar output.
Conclusion. Spectral characteristics of both interference and informant signals have been investigated. A structural diagram has been proposed for a high sensitivity, coherent search-and-rescue radar implementing a signal storage algorithm based on the polynomial model of the fluctuating process. The advantages and disadvantages of the radar are discussed, with examples given of real signal implementations and of noise spectrograms. Methods of effective estimation of Doppler signal phases are presented. The paper suggests an analysis of basic requirements as to parameters and performance characteristics of the rescue radar.
Manuscript submitted 23.05.2022
Radio phys. radio astron. 2022, 27(4): 284-288
REFERENCES
Sytnik, O.V., 2007. Identification of Slow-Moving Targets Out-side Optically Opaque Obstacles. Telecommunications and Radio Engineering, 66(18), pp. 1677–1683. DOI: https://doi.org/10.1615/TelecomRadEng.v66.i18.60
Sytnik, O.V., 2015. Adaptive Radar Techniques for Human Breathing Detection J. Mechatron., 3(4), pp. 1–6. DOI: https://doi.org/10.1166/jom.2015.1114
Murao, K., Kohda, T., 1984. Intermittency with 1/f Power Spectrum in One-Dimensional Discrete Dynamical Systems. In: Proc. Int. Symp. on Noise and Clutter Rejection in Radars and Image Sensors, Tokyo, Japan, pp. 94–99. DOI: https://doi.org/10.1109/ISEMC2.1984.7568109
Coram, G.J., McAndrew, C.C., Gullapalli, K.K. and Kundert, K.S., 2020. Flicker Noise Formulations in Compact Models. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39(10), pp. 2812–2821. DOI: https://doi.org/10.1109/TCAD.2020.2966444
Allan, D.W., Shoaf, J.H. and Halford, D., 1974. Statistics of Time and Frequency Data Analysis. NBS Monograph140. Washington D.C., pp. 151–204.
Levanon, N., Mozeson, E., 2004. Radar Signal. Hoboken, NJ: John Wiley & Sons, Inc. DOI:
https://doi.org/10.1002/0471663085Nguyen, T.V., Liqiong, T., Veysel, D., Syed, F.H., Nguyen, D.M. and Subhas, M., 2019. Review-Microwave Radar Sensing Systems for Search and Rescue Purposes. Sensors, 19(13), p. 2879. DOI: https://doi.org/10.3390/s19132879
Zhang, J., Du, Y. and Yan, He, 2020. Code Design for Moving Target-Detecting Radar in Nonhomogeneous Signal-Dependent Clutter. Math.l Probl. Eng., id. 7609547. DOI: https://doi.org/10.1155/2020/7609547
Sytnik, O.V., 2014. Quasi-Optimal Receiver with Non-Coherent Discriminators for Rescuer Radar. J.Commun.Eng. Netw.,2(2), pp. 55–62. DOI: https://doi.org/10.18005/JCEN0202001
Taylor, J.D., 2012. Ultrawideband Radar. Applications and Design. Boca Raton, FL: CRC Press. DOI: https://doi.org/10.1201/b12356-2
Mishali, M., Yonina, C., 2009. Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals. IEEE Trans. Signal Process, 57(3), pp. 993–1009. DOI: https://doi.org/10.1109/TSP.2009.2012791
Keywords
Full Text:
PDF
Licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) .