Energy Detector of Stochastic Signals in Noise Uncertainty




stochastic signal, energy detector, time parameters, frequency channel, noise level, threshold, integration interval


Wide use of software-defined radio has led to a significant sophistication of electronic environment. This is mainly due to ability of generation signals of almost any shape. To detect signals with an unknown dynamic frequency-time structure, it is advisable to use advanced energy detector algorithms. The purpose of this article is to automate processes of stochastic signals detection and time parameters estimation under the conditions of unknown frequency-time structure of signals and noise power. The essence of proposed method is to detect and track temporal energy changes averaged over L samples of received signal in selected frequency channel. Threshold value for a given probability of false alarm is calculated using current estimates of signal power. This threshold is dynamic and is refined only in time intervals free from the signals. In those time windows where energy exceeds threshold, a decision is made about the presence of a signal. An algorithm for detecting stochastic signals is proposed. If a signal is present at the initial moment of time, proposed algorithm can detect only its end by a sharp decrease of signal energy. After that, new noise level is estimated and threshold value is refined. Detection curves of proposed algorithm are obtained. It is shown that when number of samples L is increased by an order, the gain in signal-to-noise ratio in signal detection is about 4 dB. The maximum value of correct detection probability of a pulse signal is achieved with the same pulse duration and the length of the integration interval. Compared to method of signal smoothing with moving average window, proposed method has less computational complexity, simplifies the search for signal time boundaries, and gives smaller errors in signal duration estimates. Recommendations for the implementation of developed algorithm are formulated.

Author Biography

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

Candidate of Engineering Sciences, Senior Researcher



Captain K. M., Joshi M. V. (2022). Spectrum Sensing for Cognitive Radio. Fundamentals and Applications. CRC Press, 256 p. doi: 10.1201/9781003088554.

Elmasry F. G. (2021). Dynamic Spectrum Access Decisions: Local, Distributed, Centralized, and Hybrid Designs. Wiley, 728 p.

Liang Y.-C. (2020). Dynamic Spectrum Management. From Cognitive Radio to Blockchain and Artificial Intelligence. Springer, 180 р. DOI: 10.1007/978-981-15-0776-2.

Kailath T., Poor H. V. (1998). Detection of stochastic processes. IEEE Transactions on Information Theory, Vol. 44, Iss. 6, pp. 2230-2231. doi: 10.1109/18.720538.

Luo J., Zhang G., Yan C. (2022). An Energy Detection-Based Spectrum-Sensing Method for Cognitive Radio. Wireless Communications and Mobile Computing, Article ID 3933336, 10 p. doi: 10.1155/2022/3933336.

Savaux V. (2019). Detector based on the energy of filtered noise. IET Signal Processing, Vol. 13, Iss. 1, pp. 36-45. doi: 10.1049/iet-spr.2018.5099.

Yar E., Kocamis M. B., Orduyilmaz A., Serin M., Efe M. (2019). A Complete Framework of Radar Pulse Detection and Modulation Classification for Cognitive EW. 27th European Signal Processing Conference (EUSIPCO), pp. 1-5. doi: 10.23919/EUSIPCO.2019.8903045.

Licursi de Mello R. G., Rangel de Sousa F. (2018). Precise techniques to detect superimposed radar pulses on ESM systems. IET Radar, Sonar & Navigation, Vol. 12, Iss. 7, pp. 735-741. doi: 10.1049/iet-rsn.2017.0563.

Albaker B. M., Rahim N. A. (2011). Detection and parameters interception of a radar pulse signal based on interrupt driven algorithm. Scientific Research and Essays, Vol. 6 (6), pp. 1380-1387.

Nikonowicz J., Mahmood A., Gidlund M. (2020). A Blind Signal Separation Algorithm for Energy Detection of Dynamic PU Signals. Cornell University, 5 р. doi: 10.48550/arXiv.2003.09057.

Li H., Hu Y., Wang S. (2021). A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio. Entropy, Vol. 23, Iss. 4, 448, 28 р. doi: 10.3390/e23040448.

Buhaiov M. V. (2023). Stochastic signals detector. Seventeenth International Scientific and Technical Conference ``MODERN CHALLENGES IN TELECOMMUNICATIONS'', 18-21 April 2023. Kyiv: NTUU KPI, pp. 278-280.

Zar J. H. (1978). Approximations for the Percentage Points of the Chi-Squared Distribution. Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 27, No. 3, pp. 280-290. doi: 10.2307/2347163.

Buhaiov M. V. (2023). Method of Complex Envelope Processing for Signal Edges Detection. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, Iss. 92, pp. 54-59. doi: 10.20535/RADAP.2023.92.54-59.




How to Cite

Buhaiov , M. V. (2023) “Energy Detector of Stochastic Signals in Noise Uncertainty”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (94), pp. 32-40. doi: 10.20535/RADAP.2023.94.32-40.



Telecommunication, navigation, radar systems, radiooptics and electroacoustics