Enviar artigo por via de e-mail Adaptation of detection thresholds based on the results of multi-frame accumulation radar signals against the background of non-gaussian noise
- Autores: Koshelev V.I.1, Belokurov V.A.1
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Afiliações:
- Ryazan State Radiotechnical University named after V.F. Utkin
- Edição: Volume 69, Nº 8 (2024)
- Páginas: 802-808
- Seção: К 90-ЛЕТИЮ М.С. ЯРЛЫКОВА
- URL: https://bulletin.ssaa.ru/0033-8494/article/view/684811
- DOI: https://doi.org/10.31857/S0033849424080106
- EDN: https://elibrary.ru/HNTNLE
- ID: 684811
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Resumo
An algorithm for stabilizing the false alarm rate is proposed, based on the approximation of the probability distribution density of the decisive statistics counts during multi-frame accumulation against the background of noise described by the Rayleigh and Weibull distributions. It is shown that the proposed algorithm for stabilizing the false alarm rate is adaptive and invariant to the noise distribution law.
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Sobre autores
V. Koshelev
Ryazan State Radiotechnical University named after V.F. Utkin
Email: belokurov.v.a@rsreu.ru
Rússia, Gagarin str., 59/1, Ryazan, 390005
V. Belokurov
Ryazan State Radiotechnical University named after V.F. Utkin
Autor responsável pela correspondência
Email: belokurov.v.a@rsreu.ru
Rússia, Gagarin str., 59/1, Ryazan, 390005
Bibliografia
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Fig. 1. Illustration of the issue of taking into account the target movement model between two adjacent surveys. The dotted line highlights the area of possible transition of target parameters between the k-th and (k−1)-th surveys.
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Fig. 2. Formation of a “sliding” window based on reviews.
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Fig. 3. Structural diagram of the false alarm level stabilization block.
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Fig. 4. Dependences of the false alarm probability on the sample size M for the Rayleigh distribution at qдqАЗ=40 (a) and qдqАЗ=20 (b): K = 6 (1) and 4 (2); the dashed line is the specified value of the false alarm probability.
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Fig. 5. Dependences of the false alarm probability on the sample size M for the Weibull distribution with K = 6, qдqАЗ = 40: curve 1 – coefficients of scale 2.0 and shape 1.0, curve 2 – coefficients of scale 1.0 and shape 2.0.
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Fig. 6. Detection characteristics of the inter-review accumulation algorithm: curve 1 corresponds to the known algorithm for calculating the detection threshold [10], curve 2 – to the proposed algorithm.
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