Method for Selecting Pulsed Signals by Their Duration in Fading Channels

Authors

DOI:

https://doi.org/10.20535/RADAP.2024.96.14-20

Keywords:

pulsed signal, fading, signal selection, normal distribution, exponential distribution, sample

Abstract

The duration of pulsed signals is one of the main parameters to be estimated in radio monitoring systems. When signals propagate in channels with deep fading, even at high signal-to-noise ratios, the pulse shape will be distorted. In sophisticated electronic environment, it is also may be random interference in signal processing channel, which leads to the occurrence of false pulses with random durations. Therefore, the values of the signal pulses durations will be concentrated near their true value, and the rest of the detected pulses will have a significantly random duration. That’s why, the development and study of methods for selecting pulse signals by their durations in sophisticated signal environment is actual scientific problem.

The aim of the work is improving pulsed signals processing methods in fading channels by selecting its’ durations.

The study found that the estimates of signal pulse durations are normally distributed. Pulse durations that are not related to signals are subjected to an exponential distribution. The input data for the proposed method is only a sample of measured pulse durations. The values of the parameters of both the exponential and normal distributions are unknown. In this case, the problem of selecting pulses by their durations is formalized to the estimation of the mean values of normal distributions. To do this, it is proposed to search for the maxima of the smoothed estimate of the probability density function.

The scientific novelty of the obtained results is that a method for estimating the mean value of a normal distribution at the background of exponentially distributed values was proposed. An example of this approach is the estimation of pulsed signal durations in channels with deep fading and impulse interference. Based on the developed method, algorithms for automatic pulse selection for radio monitoring systems can be implemented.

Author Biography

M. V. Buhaiov , S. P. Korolov Military institute, Zhytomyr, Ukraine

Candidate of Engineering Sciences, Senior Researcher

References

References

Su L., Deng L., Zhu W., Zhao S. (2019). Detection and Extraction of Weak Pulse Signals in Chaotic Noise with PTAR and DLTAR Models. Mathematical Problems in Engineering, Vol. 2019, 12 p., doi: 10.1155/2019/4842102.

Dematties D., Wen C., Zhang S.-L. (2022). A Generalized Transformer-Based Pulse Detection Algorithm. ACS Sensors, Vol. 7, pp. 2710-2720, doi: 10/1021/acssensors.2c01218.

Adamek K., Armour W. (2020). Single-pulse Detection Algorithms for Real-time Fast Radio Burst Searches Using GPUs. The Astrophysical Journal Supplement Series, Vol. 247, Num. 2, 26 p., doi: 10.3847/1538-4365/ab7994.

Green D., Tummala M., McEachen J. (2021). Pulsed Signal Detection Utilizing Wavelet Analysis with a Deep Learning Approach. IEEE Military Communications Conference, pp. 396-401, doi: 10.1109/MILCOM52596.2021.9652942.

Ranney K., Tom K. (2020). A Survey of Methods for Estimating Pulse Width and Pulse Repetition Interval. DEVCOM, ARTL-TR-8974, 14 p.

Silva A. et al. (2020). A Robust ToA and Pulse Width Estimator for Electronic Warfare Applications. XXXVIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2020), 5 p., doi: 10.14209/SBRT.2020.1570657578.

Chan Y. T., Lee B. H., Inkol R., Chan F. (2010). Estimation of Pulse Parameters by Autoconvolution and Least Squares. IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, Iss. 1, pp. 363-374, doi: 10.1109/TAES.2010.5417168.

Bang J.-H., Park D.-H., Lee W., Kim D., Kim H.-N. (2023). Accurate Estimation of LPI Radar Pulse Train Parameters via Change Point Detection. IEEE Access, Vol. 11, pp. 12796-12807, doi: 10.1109/ACCESS.2023.3242684.

Kay S. M. (2013). Fundamentals of statistical signal processing: Practical algorithm development, Vol. 3. Prentice Hall, New Jersey. 403 p.

Sha M., Xie Y. (2016). The Study of Different Types of Kernel Density Estimators. 2nd International Conference on Electronics, Network and Computer Engineering, Atlantis Press, pp. 332-336. DOI:10.2991/icence-16.2016.67.

Węglarczyk S. (2018). Kernel density estimation and its application. ITM Web of Conferences, Vol. 23, 8 p. doi: 10.1051/itmconf/20182300037.

Silverman B. W. (1986). Density estimation for statistics and data analysis. Chapman and Hall, London. 176 р. doi: 10.1002/bimj.4710300745.

Downloads

Published

2024-06-30

How to Cite

Buhaiov , M. V. and Zakirov , S. V. (2024) “Method for Selecting Pulsed Signals by Their Duration in Fading Channels”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (96), pp. 14-20. doi: 10.20535/RADAP.2024.96.14-20.

Issue

Section

Telecommunication, navigation, radar systems, radiooptics and electroacoustics

Most read articles by the same author(s)

1 2 > >>