Tracking-based formation of vector radio images

Objectives. The aim of this article is to investigate the possibility of forming vector radio images of small-size objects (SSO) using a digital vector network analyzer (VNA) with a controllable bandwidth and frequency step of the tracking generator.

Methods. The work is based on the comprehensive application of the following methods: analysis of radio wave processes in the context of radio vision, the apparatus of N -pole theory, numerical methods of statistical radio engineering, and computer modeling (including both electromagnetic and functional-block simulation).

Results. A new method for vector tracking-formation of signal radio images of SSO has been developed. Its fundamental difference from traditional pulsed methods of near-field radio vision lies in the use of an S -parametric model of a spatial N -pole to describe the inhomogeneous medium, as opposed to approaches based on scalar signal transformation. To analyze the accuracy of the method, a model of operator ψ-transformation has been proposed, allowing for the evaluation of projective distortions in vector radio images of SSO signatures. Verification of the method was carried out on a custom-developed software-hardware complex based on the R&S ZNLE6 VNA. The complex provides coherent processing and verification of signal radio images of SSOs recorded under the influence of non-fluctuating interference.

Conclusions. During the research, a method for vector tracking-based formation of radio images with spectrotemporal unfolding of the power spectral density function was proposed. This method is free from the limitations of pulsed analogs and can be used as one of the solutions for verifying SSO in near-range radio vision. The foundation of the proposed method can be a model of an inhomogeneous medium as a spatially-oriented system of coupled N -poles, described by S -parameters. The practical effectiveness has been experimentally confirmed: the probability of verifying a SSO reaches at least 0.94 (Neumann–Pearson criterion) with a signal-to-noise ratio ≥15 dB and a signal-to-interference ratio ≥9 dB. These results are of practical value for the development of near-field radio vision technologies.

1. Shadinov S.S. Method of scanning spectral-temporal sweep for obtaining radio images. Oboronnyi kompleks nauchnotekhnicheskomu progressu Rossii = Defense Industry Achievements Russian Scientific and Technical Progress. 2024;4(164):57–60 (in Russ.). https://elibrary.ru/bmhwsc

2. Shadinov S.S., Kostin S.S., Konyashkin G.V., Korchagin A.S., Romanovskii M.Y., Gusein-zade N.G. Vector S -parametric analysis of signal phase dynamic radio images. Doklady Rossiiskoi akademii nauk. Fizika, tekhnicheskie nauki = Doklady Physics. 2023;512(1):78–86 (in Russ.). https://doi.org/10.31857/S2686740023050115

3. Shadinov S.S., Konyashkin G.V. Program for Capturing and Recognizing Vector Radio Images: Certificate of State Registration of Computer Program 2025614644 RF. Publ. 25.02.2025 (in Russ.).

4. Kostin M.S., Boikov K.A. Digital technologies of signal radio vision and radio monitoring. Russian Technological Journal. 2024;12(4):59–69. https://doi.org/10.32362/2500-316X-2024-12-4-59-69

5. Song S., Lu J., Xing S., et al. Near Field 3D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation. Sensors. 2022;22(1):4509. https://doi.org/10.3390/s22124509

6. Xie Q., Quan X., He B. A Remote Sensing and Meteorological Data-Based Methodology for Wildfire Danger Assessment for China. In: IGARSS 2020 2020 IEEE International Geoscience and Remote Sensing Symposium. 2020. P. 6798–6801. https://doi.org/10.1109/IGARSS39084.2020.9324063

7. Li X., Li D., Liu H., Wan J., Chen Z., Liu Q. A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection. Remote Sens. 2022;14(15):3829–3847. https://doi.org/10.3390/rs14153829

8. Papadopoulos K., Jelali M. A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar. Appl. Sci. 2023;13(23):12728–12761. https://doi.org/10.3390/app132312728

9. Kulikov G.V., Dang X.Kh. Influence of quadrature transformation imbalance on the noise immunity of signal reception with amplitude-phase shift keying. Russian Technological Journal. 2024;12(1):59–68. https://doi.org/10.32362/2500316X-2024-12-1-59-68

10. Yanik M.E., Wang D., Torlak M. Development and Demonstration of MIMO-SAR mmWave Imaging Testbeds. IEEE Access. 2020;8:126019–126038. https://doi.org/10.1109/ACCESS.2020.3007877

11. Chen H., Long Z., Niu L., et al. Millimeter-wave SFCW SAR imaging system based on in-phase signal measurement with simplified transceiver. Opt. Express. 2020;28(2):1526–1538. https://doi.org/10.1364/oe.380266

12. Kim S., Krieger G., Villano M. Volume Structure Retrieval Using Drone-Based SAR Interferometry with Wide Fractional Bandwidth. Remote Sens. 2024;16(8):1352. https://doi.org/10.3390/rs16081352

13. Manzoni M., Tebaldini S., Monti-Guarnieri A.V., et al. Automotive SAR imaging: potentials, challenges, and performances. Int. J. Microwave Wireless Technol. 2023;16(1):3–12. https://doi.org/10.1017/S1759078723000326

14. Bolkhovskaya O., Maltsev A., Sergeev V. The wavefront estimation and signal detection in multi-element antenna arrays at low SNR. In: Proceedings 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS). 2018. Р. 497–501. https://doi.org/10.1109/EECS.2018.00097

15. Bolkhovskaya O.V., Maltsev A.A., Sergeev V.A. A passive system for source detection and distance measurement based on signal wavefront estimation. Radiotekhnika = J. Radioengineering. 2022;86(9):98–112 (in Russ.). https://doi.org/10.18127/j00338486-202209-11