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- W4320803070 abstract "The new complex scenarios wherein radars operate are characterized by high levels of heterogeneity. As a consequence, the conventional detection/estimation algorithms might be no longer effective since most of them require a suitable amount of training samples. In this paper, we propose an innovative detection architecture that incorporates a neural network (NN) to adaptively estimate the interference covariance matrix. The neural network is firstly trained with a suitable data set that encompasses cases of practical interest and then the resulting estimate is used in place of the true covariance matrix in the decision statistic of the matched filter. Actually, this paper represents a preliminary study towards the application of NN to interference covariance estimation in radar. The extension of this approach to the structure classification and overall estimation of the interference covariance matrix is currently under investigation. The results presented here (also in comparison with a classical detector) highlight that the proposed approach has an inherent high potential that can be further developed." @default.
- W4320803070 created "2023-02-15" @default.
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- W4320803070 date "2022-06-20" @default.
- W4320803070 modified "2023-10-01" @default.
- W4320803070 title "A NN-based Approach to ICM Estimation and Adaptive Target Detection" @default.
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- W4320803070 doi "https://doi.org/10.1109/sam53842.2022.10041862" @default.
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