• Українська
  • English
  • Русский
ISSN 2415-3400 (Online)
ISSN 1028-821X (Print)

Features of the quasi-optimal discrete signal processing

heading: 
STATISTICAL RADIOPHYSICS
Sytnik, OV
Organization: 

O. Ya. Usikov Institute for Radiophysics and Electronics of the National Academy of Sciences of Ukraine
12, Proskura st., Kharkov, 61085, Ukraine
E-mail: ssvp11@ire.kharkov.ua
https://doi.org/10.15407/rej2019.04.063
Language: russian
Abstract: 

 

Subject and Purpose. The subject of the research is a digital signal processing system of a radar, consisting of an input filter, an analog-to-digital converter and a processor that implements an algorithm for detecting of information signal in accordance with some criterion given in advance. The aim of the work is to estimate losses in the signal-to-noise ratio during the transition from optimal to quasi-optimal signal processing taking into account the spectral characteristics of signals and interference.

The methods and methodology of work are based on the analysis of the spectral functions of useful signals and noise and the calculation of errors of their approximation using lattice functions at real sample durations. It is shown that the approximation errors of the spectrum of the useful signal and the noise spectrum at the output of the smoothing filter have a different effect on the signal processing result. To accumulate the signal and reduce the noise level in real time, it was proposed by minimizing the signal-to-noise losses during quasi-optimal signal processing by choosing the frequency, duration of the lattice function samples and the width of the filter frequency response.

Results of work. Analytical relations have been obtained for estimating losses in the signal-to-noise ratio for quasi-optimal signal processing with respect to the optimal one. It is shown that at limiting frequencies an additional loss reduction can be obtained by changing the sampling duration.

Conclusion Discrete quasi-optimal signal processing at fixed sample durations and frequency bands of matching filters can lead to significant signal-to-noise losses. To reduce losses, it is necessary to find a compromise between the requirements for the speed of the processing system, the accuracy of the approximation of the spectral characteristics of signals and noise, and the passband of the matching filter.
Keywords: algorithm, criterion, discrete processing, sampling, sampling frequency, spectral function, trellis function, white noise

Manuscript submitted 18.03.2019
PACS: 42.30.sy
Radiofiz. elektron. 2019, 24(4): 63-69
Full text (PDF)

References: 
1. Levin, B.R., 1977. Theoretical Foundations of Statistical Radio Engineering. Moscow: Sov. Radio Publ. B. 1. (in Russian).
2. Tihonov, V.I., 1982. Statistical Radio Engineering. Moscow: Sov. Radio Publ. (in Russian).
3. Kramer, G., Lidbetter, M., 1969. Stationary and related stochastic processes. Sample function properties and their applications. Translated from English by Yu.K. Belyaev, M.P. Ershov. Moscow: Mir Publ. (in Russian).
4. Van Trees, H.L., 1972. Detection, Estimation, and Modulation Theory. Translated from English and ed. by V.I. Tikhonov. Moscow: Sov. Radio Publ. Pt. 1. (in Russian).
5. Dixon, W.G., Massey, F.J., 1968. Introduction to Statistical Analysis. Mc Graw-Hill, N.Y.
6. Otnes, R.K., Enochson, L., 1982. Applied Time Series Analysys. J. Willey, N.Y.
7. Gold, B., Rader, C.M., 1973. Digital Processing of Signals. Translated from English and ed. by A.M. Trakhtman. Moscow: Sov. Radio Publ. (in Russian).
8. Biguesh, M., Gershman, A., 2006.Training-based MIMO Channel Estimation: a Study of Estimator Tradeoffs and Optimal Training Signals. IEEE Trans. Signal Process., 54(3), pp. 884-893. DOI: https://doi.org/10.1109/TSP.2005.863008
9. Lo, N., Falconer, D.D., Sheikh, A.H., 2018. Adaptive Equalization and Diversity Combining for Mobile Radio Using Interpolated Channel Estimates. IEEE Trans. Veh. Technol., 40(3), pp. 636-645. DOI: https://doi.org/10.1109/25.97518
10. Gonorovsky I. S. Radio engineering circuits and signals. Moscow: Sov. Radio, 1978. 304 p. (in Russian).
11. Proakis, J., Salehi, M., 2013. Digital Communications. 5th ed. McGraw-Hill Higher Education.
12. Lathi, B.P., Ding, Z., 2019. Modern Digital and Analog Communication Systems. 4th ed. Oxford University Press.
13. Sytnik, O.V., Vyazmitinov, I.A., Myroshnychenko, Ye.I., Kopylov, Yu.A., 2009. Spectral Selection of Very-Low Frequencies Processes. Telecommunications and Radio Engineering, 68(2), pp. 137-144. DOI: https://doi.org/10.1615/TelecomRadEng.v68.i2.50
14. Vyazmitinov, I.A., Myroshnichnko, Ye.I., Sytnik, O.V., 2012. Statistical Properties of Spectral Estimates of Information Signals for Sounding of Slowly Moving Targets. Physical Bases of Instrumentation, 1(4(5)), pp. 78-85 (in Russian). DOI: https://doi.org/10.25210/jfop-1204-078085
15. Sytnik, O.V., 2003. Invariant Transformation in Identification Theory. Telecommunications and Radio Engineering, 60(10-12), pp. 20-32. DOI: https://doi.org/10.1615/TelecomRadEng.v60.i1012.30