FEATURES OF SPATIAL SIGNAL PROCESSING IN A WIDEBAND PASSIVE RADAR SYSTEM
DOI:
https://doi.org/10.34169/2414-0651.2020.1(25).52-59Keywords:
signal intelligence system, radiofrequency monitoring system, radiofrequency emission source, wideband circular antenna array, spatial filtering of the signals, angle of signal arrival estimation, linear constraint minimum variance method, multiple signal classification methodAbstract
We substantiate the necessity of upgrading methods and algorithms used when developing perspective signal intelligence (SIGINT) and/or radiofrequency (RF) monitoring systems. We show this problem is caused by increasing the requirements with respect to capabilities of SIGINT and/or RF monitoring systems which, being wideband systems, operate under complicated interference conditions. These requirements touch on capabilities concerning extracting and detecting the signals in interference background, also capabilities regarding estimating the angles of arrival of RF emission sources (RFES) and resolving the signals over angular coordinate.
We formulate the problem of signal spatial filtering with the use of linear constraint minimum variance (LCMV) method for the purpose of slow direction searching RFES by SIGINT and/or RF monitoring system. We show this problem can be solved by the suggested signal processing algorithm based on wideband circular antenna array. We consider example of implementation of the suggested spatial filtering algorithm operating under strong interference conditions on the basis of wideband circular antenna array. We show that exploiting 19 elements wideband circular antenna array one can provide interference suppression coefficient up to 40 dB so that useful narrowband signal is extracted with satisfactory quality.
We formulate the problem of estimating angle of arrival (AoA) of the signals from a set of RFES using multiple signal classification (MUSIC) method for the purpose of fast RFES direction search under low and intermediate intensity interference conditions. We consider variant of this problem solution on the basis of the suggested signal processing algorithm which provides eliminating injurious influence of dispersion phenomena taking place in wideband circular antenna array. We show an example of implementing the suggested algorithm of estimating AoA of the signals from a set of RFES using MUSIC method exploiting wideband circular antenna array. We draw a conclusion that the obtained results provide the opportunity of forming the corresponding requirements when developing SIGINT and/ or RF monitoring systems.
Downloads
References
Buckley, K. M. (1987). Spatial/spectral filtering with linearly-constrained minimum variance beamformers. IEEE Trans. on ASSP, ASSP-35, pp. 249 — 266. DOI: https://doi.org/10.1109/TASSP.1987.1165142
Frost, O. L. (1972). An algorithm for linearly constrained adaptive array processing. Proc. IEEE, 60, pp. 926 — 935. DOI: https://doi.org/10.1109/PROC.1972.8817
Van Veen, B. & Buckley, K. (1988). Beamforming: a versatile approach to spacial filtering. IEEE ASSP Magazine, 5 (2), pp. 4 — 24. DOI: https://doi.org/10.1109/53.665
Haykin, S. (1991). Advances in Spectrum Analysis and Array Processing. Vol. 1 and 2. Englewood Cliffs, NJ, Prentice Hall.
Johnson, D. H. & Dudgeon, D. E. (1992). Array Signal Processing: Concepts and Methods. Englewood Cliffs, NJ, Prentice Hall.
Haykin, S. (1995). Advances in Spectrum Analysis and Array Processing. Vol. 3. Englewood Cliffs, NJ, Prentice Hall.
Buckley, K. M., Douglass, S. C., Sayed, A. H., Van Veen, B., et al. (1999). Digital Signal Processing Handbook. Ed. by V. K. Madisetti and D. B. Williams. CRC Press, 1690 p.
Monzingo, R. A. & Miller, T. W. (1980). Introduction to Adaptive Arrays. John Wiley and Sons.
Capon, J. (1969). High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE, vol. 57, pp.1408—1418. DOI: https://doi.org/10.1109/PROC.1969.7278
Bangs, W. J. (1971). Array Processing with Generalized Beamformers. PhD thesis, Yale Univ., New Haven, CT.
Schmidt, R. O. (1979). Multiple emitter location and signal parameter estimation. Proc. RADC, Spectral Estimation Workshop, Rome, New York, pp. 243—258.
Schmidt, R. O. (1981). A signal subspace approach to multiple emitter location and spectral estimation. PhD dissertation, Department of Electrical Engineering, Stanford Univ., Stanford.
Johnson, D. H. (1982). Application of spectral estimation methods in bearing estimation problems. Proc. IEEE, vol. 70, pp. 1018—1028. DOI: https://doi.org/10.1109/PROC.1982.12430
Barabel, A. J. (1983). Improving the resolution performance of eigenstructure-based direction finding algorithms. Proc. of the Intern. Conf. on Acoustics, Speech, and Signal Processing, Boston, MA, pp. 336—339. DOI: https://doi.org/10.1109/ICASSP.1983.1172124
Bienvenu, G. (1983). Influence of the spatial coherence of the background noise on high resolution passive methods. Proc. of the Intern. Conf. on Acoustics, Speech, and Signal Processing, Washington, DC, pp. 306—309. DOI: https://doi.org/10.1109/ICASSP.1979.1170720
Kumaresan, R. & Tufts, D. W. (1983). Estimating the angles of arrival of multiple plane waves. IEEE Trans. on Aerospace and Electronic systems, AES-19, pp. 134—139. DOI: https://doi.org/10.1109/TAES.1983.309427
Paulraj, A., Roy, R. & Kailath, T. (1986). A subspace rotation approach to signal parameter estimation. Proc. of IEEE 74 (7), pp. 1044—1046. DOI: https://doi.org/10.1109/PROC.1986.13583
Pillai, S. U. (1989). Array Signal Processing. New York: Springer. DOI: https://doi.org/10.1007/978-1-4612-3632-0
Roy, R. & Kailath, T. (1989). ESPRIT – Estimation of signal parameters via rotational invariance techniques. IEEE Trans. on Acoustics, Speech, and Signal Processing, ASSP-37 (7), pp. 984—995. DOI: https://doi.org/10.1109/29.32276
Viberg, M. & Ottersten, B. (1991). Sensor array processing based on subspace fitting. IEEE Trans. on Signal Processing, 39 (5), pp. 1110—1121. DOI: https://doi.org/10.1109/78.80966
Doron, M., Doron, E. & Weiss, A. (1993). Coherent wide-band processing for arbitrary array geometry. IEEE Trans. on Signal Processing, 41 (1), pp. 414—417. DOI: https://doi.org/10.1109/TSP.1993.193167
Viberg, M. (1995). Subspace-based methods for the identification of linear time-invariant systems. Automatica, 31 (12), pp. 1835—1851. DOI: https://doi.org/10.1016/0005-1098(95)00107-5
Vasilishin, V. I. (2004). Direction finding with superresolution using root implementation of eigenstructure techniques and joint estimation strategy. European Conf. on Wireless Technology. Amsterdam, Netherlands: Proc. of Conf. Pp. 317—320.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Сергій Зібін ,Андрій Попов ,Володимир Твердохлібов

This work is licensed under a Creative Commons Attribution 4.0 International License.