E.V. Egorova1, M.H.Aksayitov2, A.N. Ribakov3

1 Moscow Technological University (MIREA) (Moscow, Russia) (Moscow, Russia)
2 JSC «Concern Granit-Electron» (Saint Petersburg, Russia)
3 All-Russian Scientific Research Institute of Automation named after N. L. Dukhov (Moscow, Russia)
1 calipso575@gmail.com, 2 ak-marat@bk.ru

The growing need for modern digital data processing systems with more than one sampling rate has led to the development of a new subfield of digital signal processing known as multi-rate processing. Multi-rate processing uses two basic operations, decimation and interpolation, to efficiently alternate data rates. Decimation reduces the sampling rate, effectively compressing the data and retaining only the necessary information. Interpolation, on the other hand, increases the sampling rate. Often, converting data to a new rate makes it easier to process (e.g., makes it more computationally efficient) or ensures compatibility with another system. Multi-rate processing is an efficient method of digitally changing the sampling rate of a signal, which exploits the strengths of traditional digital signal processing. The primary benefit of multi-rate systems is the ability to leverage the advantages of digital signal processing, in particular the ability to use digital signal processing to bandlimit a signal to nearly the Nyquist frequency (half the sampling rate) with significant attenuation and without violating the conditions of the frequency representation theorem. The advantages of this approach have been demonstrated in many areas: digital filtering, data acquisition, and high-resolution data acquisition. In fact, the performance of a multi-rate radar data processing system is critically dependent on the type and quality of the filter used. Note that both finite impulse response (FIR) and infinite impulse response (IIR) filters can be used for decimation and interpolation, with FIR filters being the preferred choice. In the area of multi-rate signal processing, FIR filters offer comparable computational efficiency to IIR filters in contrast to conventional digital signal processing. In practice, sampling rate changes are implemented in two or more stages to achieve maximum computational efficiency or minimum memory requirements. Digital filters used in multi-rate radar data processing systems have less stringent specifications, resulting in fewer coefficients and therefore less sensitivity to finite-bit-depth effects. The main advantage of multi-rate radar data processing systems is the ability to take advantage of digital signal processing, in particular the use of DSP to bandlimit the signal to near the Nyquist frequency with significant attenuation and without violating the conditions of the frequency representation theorem. The advantages of this approach have been demonstrated in many areas, including digital filtering, data acquisition, and high-resolution data acquisition. The use of multi-rate processing techniques allows very efficient implementations by allowing filtering to be performed at a much lower rate, which greatly reduces the filter order. This article will be useful to developers and researchers of modern radar systems for various purposes, designed for efficient signal processing, as well as graduate and undergraduate students studying modern radio engineering systems.

Egorova E.V., Aksyaitov M.Kh., Rybakov A.N. Digital processing of radar signals at several data rates. Science Intensive Technologies. 2025. V. 26. № 3. P. 32−40. DOI: https://doi.org/ 10.18127/j19998465-202503-04 (in Russian)

1. Adams R.W. Design and implementation of an audio 18 bit analog-to-digital converter using oversampling techniques. J. Audio Engineering Society. 1986. V. 34(3). P. 153–166.
2. Crochiere R.E., Rabiner L.R. Optimum FIR digital filter implementations for decimation, interpolation, and narrow-band filtering. IEEE Trans. Acoustics, Speech and Signal Processing. 1975. V. 23(5). P.444–456.
3. Crochiere R.E., Rabiner L.R. Multirate Digital Signal Processing. Englewood Cliffs NJ: Prentice-Hall. 1983.
4. Anisimov B.V., Kurganov V.D., Zlobin V.K. Raspoznavanie i cifrovaya obrabotka izobrazhenij: Ucheb. posobie dlya studentov vuzov. M. 1983. S. 295 (in Russian).
5. Aksyaitov M.H., Egorova E.V., Rybakov A.N. Optimal'nyj algoritm opredeleniya rakursa prostranstvenno-raspredelennoj celi. Naukoemkie tekhnologii. 2024. T. 25. № 6. S. 26–33. DOI: https://doi.org/10.18127/j19998465-202406-04 (in Russian).
6. Hemming R.V. Cifrovye fil'try: Per s angl.; Pod red. O.A. Potapova. M.: Nedra. 1987. S. 221 (in Russian).
7. Crochiere R.E., Rabiner L.R. Further considerations in the design of decimators and interpolators. IEEE Trans. Acoustics, Speech and Signal Processing. 1976. V. 24. P. 296–311.
8. Egorova E.V., Aksyaitov M.H., Rybakov A.N. Veroyatnostnaya procedura raspoznavaniya i identifikaciya prostranstvennyh ob"ektov. Uspekhi sovremennoj radioelektroniki. 2023. T. 77. № 3. C. 53–63. DOI: 10.18127/j20700784-202303-05 (in Russian).
9. Tatarskij B.T., D'shorec R.Z. Sintez algoritmov prinyatiya resheniya dlya mnogoporogovogo obnaruzhitelya. Radiotekhnika. 1999. № 2 (in Russian).
10. Welland D.R., Del Signore B.P., Swanson E.J., Tanaka T., Hamashita K., Hara S., Takasuka K. A stereo 16-bit delta-sigma A/D converter for digital audio. J. Audio Engineering Society. 1989. V. 37(6). P. 476–486.
11. Westgate J.A., Keith R.D.F., Gurnow J.S.K., Ifeachor E.C., Greene K.R.G. Suitability of fetal scalp electrodes for fetal electrocardiogram during labour. J. Clin. Physics & Physiological Measurement. 1990. V. 11(4). P. 297–306.