doi: 10.52899/24141437_2025_01_75
UDK: 621.396

Detection of a random signal in non-Gaussian noise with non-ideal training interference samples

Самаров Е. К.
Article language: English
Citation Link: Samarov EK. Detection of a random signal in non-Gaussian noise with non-ideal training interference samples. Transactions of the Saint Petersburg State Marine Technical University. 2025;4(1):75–79. DOI:https://doi.org/10.52899/24141437_2025_01_75

Annotation

BACKGROUND: In many problems of statistical radio engineering and radiophysics, statistical conclusions are quite often based on both observations and a priori assumptions about the studied case, e.g. in the form of certain distributions in the studied model. The papers generally solve the problem of combining independent channels used to detect a random signal against a random interference of independent intensity under the assumption of normality of all random variables. AIM: To detect a random signal against additive non-Gaussian noise with non-ideal training interference samples based on the maximin decision rule used to test assumptions. MATERIALS AND METHODS: In this paper, a similar issue is studied for non-Gaussian non-stationary random variables with non-ideal training interference samples. RESULTS: The detection problem is solved based on data from 2K independent channels. In this case, n samples of complex amplitudes are made from signal plus noise mixture in primary K channels and sample interference values are derived from auxiliary K channels. The problem is solved using the maximin decision rule to test the H0 assumption against the alternative H1 assumption. CONCLUSIONS: The article reviews an example of detecting a random signal against an additive non-Gaussian non-stationary interference with a probability distribution density described by the Laplace law. Keywords: training sample; a priori assumptions; signal detection; signal plus noise mixture; maximum decision rule; complex parametric hypotheses; stability region of the algorithm.

Bibliography

1. Kogan IM. Near-field radar. Moscow: Soviet Radio; 1973. 272 p. (In Russ.)
2. Polyakov PF. Signal reception in multipath channels. Moscow: Radio and Communication; 1986. 248 p. (In Russ.)
3. Repin VG, Tartakovsky GP. Statistical synthesis under a priori uncertainty and adaptation of information systems. Moscow: Soviet Radio; 1977. 432 p. (In Russ.)
4. Artyushenko VM, Volovach VI, Tyazhev AI. Simulation of continuous markov processes in discrete-time by the example of radar signals described by stochastic differential equations. Radioengineering. 2016;(12):22–28. EDN: XRLFCB
5. Artyushenko VM, Volovach VI, Shakursky MV. The demodulation signal under the influence of additive and multiplicative non- Gaussian noise. In: Proceedings of 2016 IEEE East-West design and test symposium, EWDTS 2016. Yerevan, 2017. ID 7807704. doi: 10.1109/EWDTS.2016.7807704
6. Artyushenko VM, Volovach VI. Comparative analysis of discriminators efficiency of tracking meters under influence of non-Gaussian broadband and band-limited noise. In: 11th International IEEE scientific and technical conference «Dynamics of systems, mechanisms and machines». 2017. P. 1–4. doi: 10.1109/Dynamics.2017.8239430
7. Artyushenko VM, Volovach VI. Synthesis and analysis of discriminators under influence of non-Gaussian noise. J Phys: Conf Ser. 2018;944:012004. doi: 10.1088/1742-6596/944/1/012004
8. Lehmann E. Statistical hypothesis testing. Moscow: Nauka; 1979. 408 p. (In Russ.)
9. Hajek J, Shidak Z. Theory of ranking criteria. Bolshev LN, editor; Chibisov DM, transl. from Engl. Moscow: Nauka; 1971. 375 p. (In Russ.)