ANALYSIS OF THE EFFECTIVENESS OF ALGORITHMS FOR ESTIMATING PARAMETERS OF AUTOREGRESSIVE MODELS IN THE PROBLEM OF SIGNAL DETECTION IN INTERFERENCE CONDITIONS
DOI:
https://doi.org/10.18372/2310-5461.65.19928Keywords:
adaptive algorithms, autoregressive model, parameter estimation, clutter compensation, clutter, signal detection, data processingAbstract
Improving the accuracy of the probability of correct detection under interference conditions remains a pressing task, especially in an environment with dynamic and non-stationary interference. In this context, there is growing interest in using adaptive detection algorithms that can change their parameters following the statistical characteristics of the background noise. One of the key aspects of the synthesis of such algorithms is adequate modeling of interference. Autoregressive models allow for effective modeling of interference using the dependence between the previous values of signals, which is important for optimal interference compensation.
The effectiveness of building such models largely depends on the accuracy of estimating their parameters, which directly affects the quality of adaptive interference compensation and, accordingly, the detection characteristics of the general algorithm. Therefore, this article investigates the algorithms for estimating the parameters of AR models - in particular, maximum likelihood methods, recursive and classical Yule-Walker, and Levinson–Durbin approaches.
Attention is also paid to studying the impact of the selected estimation algorithm on the accuracy of approximation of the statistical characteristics of the noise background, as well as on the subsequent effectiveness of adaptive signal detection. For this purpose, a two-stage computer simulation was implemented: at the first stage, a comparative analysis of the accuracy of estimates of the parameters of the AR model was carried out; at the second stage, the impact of the obtained estimates on the probabilistic characteristics of adaptive detection in interference conditions was assessed.
References
Skolnik M.I., Radar Handbook (3rd ed), Boston: McGraw-Hill, 1990.
Prokopenko I., Yanovsky F., Ligthart L., Adaptive algorithms for weather ra-dar. First European Radar Conference, 2004. EURAD. pp. 329-332. Amster-dam, Netherlands, 2004.
Prokopenko I., Omelchuk I., Chyrka Y., RADAR signal parameters estimation in the MTD tasks. Int. J. Electron. Telecommun. 58(2), pp. 159–164, 2012.
Prokopenko I.G., Dmytruk A.Yu., Implementation of Adaptive Algorithms in the Task of MTDI Filtration, 2021 IEEE International Conference on Infor-mation and Telecommunication Technologies and Radio Electronics, pp. 226-231, 2021.
Lekhovytskyi D.I., Kirillov I.G., Modeling of passive interference by pulse ra-dars based on arbitrary order autoregressive processes. Information Processing Systems. pp. 90-101, 2008.
Box G.E., Jenkins G.M., Time Series Analysis: Forecasting and Control. 3rd Edition, Prentice Hall, Englewood Cliffs, 1994.
Haykin S., Adaptive filter theory, Prentice Hall, Upper Saddle River, NJ, 4th edition, 2002.
Tan W. Y., Lin V., Some Robust Procedures for Estimating Parameters in an Autoregressive Model, Sankhyā: The Indian Journal of Statistics, Series B (1960-2002) 55, no. 3, pp. 415–35, 1993. http://www.jstor.org/stable/25052807.
Sengupta D., Kay S., Efficient estimation of parameters for non-Gaussian auto-regressive processes, in IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 6, pp. 785-794, 1989, doi: 10.1109/ASSP.1989.28052.
Stoica P., Moses R. L., Spectral Analysis of Signals, Pearson Prentice Hall, 2005.
Hamilton J. D., Time Series Analysis, Princeton University Press, 1994. https://doi.org/10.2307/j.ctv14jx6sm.
Haykin S.S., Adaptive Filter Theory 4th Edition, 2002.
Zadiraka V.K., Semenov V.Y., Semenova Y.V., Method of Noise-Robust Es-timation of Parameters of an Autoregressive Model in the Frequency Domain. Cybern Syst Anal 57, 836–842, 2021. https://doi.org/10.1007/s10559-021-00409-y
Prokopenko I.G., Dmytruk A.Yu., Prokopenko K.I., Application of robust al-gorithms in the problem of detection of moving targets on the background of non-gaussian clutter. Science-based Technologies, № 1, p. 58-66. 2023. doi: https://doi.org/10.18372/2310-5461.57.17445.
Brockwell P. J., Davis R. A. Introduction to Time Series and Forecasting. Springer International Publishing. 2016.
Prokopenko I.G., Dmytruk A.Yu., Prokopenko K.I., Application of robust al-gorithms in the problem of detection of moving targets on the background of non-gaussian clutter. Science-based Technologies, № 1, pp 58-66. 2023. doi: https://doi.org/10.18372/2310-5461.57.17445.
