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Theoretical Performance of Low Rank Adaptive Filters in the Large Dimensional Regime

Abstract : This paper proposes a new approximation of the theoretical Signal to Interference plus Noise Ratio (SINR) loss of the Low-Rank (LR) adaptive filter built on the eigenvalue decomposition of the sample covariance matrix. This new result is based on an analysis in the large dimensional regime, i.e. when the size and the number of data tend to infinity at the same rate. Compared to previous works, this new derivation allows to measure the quality of the adaptive filter near the LR contribution. Moreover, we propose a new LR adaptive filter and we also derive its SINR loss approximation in a large dimensional regime. We validate these results on a jamming application and test their robustness in a Multiple Input Multiple Output Space Time Adaptive Processing (MIMO-STAP) application where the data size is large
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Submitted on : Wednesday, February 26, 2020 - 10:13:28 AM
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Alice Combernoux, Frédéric Pascal, Guillaume Ginolhac, Marc Lesturgie. Theoretical Performance of Low Rank Adaptive Filters in the Large Dimensional Regime. IEEE Transactions on Aerospace and Electronic Systems, Institute of Electrical and Electronics Engineers, 2019, 27 (6), pp.3347 - 3364. ⟨10.1109/TAES.2019.2906418⟩. ⟨hal-02083430⟩



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