Path of Science: International Electronic Scientific Journal

Currently, global trends in the development of perimeter security technology lead to the general miniaturization of devices and systems and the increase of their autonomy. The primary trend in developing these systems is the maximum processing and classification of signals by built-in tools. The article presents the study results on the possibility of using blind algorithms to calculate the level of operation of the threshold detector CME and FCME in detecting human steps in seismic perimeter security detectors using low-power computing modules. The algorithms were tested on actual data recorded during the experiments. As a result of applying algorithms for the seismic signal envelope, the human steps of the probability of false positives for the CME and FCME algorithms were 23% and 10%, respectively. Neutralizing the signal trend allowed obtaining values of false alarms of 16% for the CME algorithm and 7% for the FCME algorithm, and normalization of the signal amplitude within one analysis interval allowed obtaining the probability of false alarms at 0% for both algorithms. The obtained results give complete information when choosing the type of algorithm depending on the input data. In detecting seismic data by an autonomous sensor, it is most appropriate to use the SME algorithm with pre-normalization of the signal amplitude due to less computational complexity.

CME; FCME; seismic signals; autonomous seismic sensors; threshold detector

On the partition of energy between elastic waves in a semi-infinite solid. (1955). Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 233(1192), 55–69. doi: 10.1098/rspa.1955.0245

Sushko, I. O., Vistyzenko, Y. V., Movchanyuk, A. V., Antypenko, R. V., & Serha, A. V. (2019). Geophone Equivalent Circuit for Simulation Tasks in SPICE Packages. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, 77, 53–59. doi: 10.20535/radap.2019.77.53-59

Richman, M. S., Deadrick, D. S., Nation, R. J., & Whitney, S. (2001). Personnel tracking using seismic sensors. Unattended Ground Sensor Technologies and Applications III. doi: 10.1117/12.441276

Li, J., He, M., Cui, G., Wang, X., Wang, W., & Wang, J. (2020). A Novel Method of Seismic Signal Detection Using Waveform Features. Applied Sciences, 10(8), 2919. doi: 10.3390/app10082919

Vartiainen, J. (2010). Concentrated signal extraction using consecutive mean excision algorithms (Doctoral thesis). Retrieved from http://jultika.oulu.fi/files/isbn9789514263491.pdf

Iwata, H., Umebayashi, K., Tiiro, S., Suzuki, Y., & Lehtomaki, J. J. (2016). A study on Welch FFT segment size selection method for spectrum awareness. 2016 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). doi: 10.1109/wcncw.2016.7552708

Vartiainen, J., Lehtomäki, J., Saarnisaari, H., & Juntti, M. (2010). Analysis of the Consecutive Mean Excision Algorithms. Journal of Electrical and Computer Engineering, 2010, 1–13. doi: 10.1155/2010/459623