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NASA NTRS · 20170004854

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

Schoenwald, Adam J.·Kim, Seung-Jun·Mohammed, Priscilla N.
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Abstract

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interference-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

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kurtosisPassive remote sensingradiometersmicrowaveseigenvalue methodradio frequency interference mitigation

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BibTeXRIS

Schoenwald, Adam J., Kim, Seung-Jun, Mohammed, Priscilla N.. 2017-07-23. Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis. https://ntrs.nasa.gov/citations/20170004854

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NASA NTRS2005

Kurtosis Approach for Nonlinear Blind Source Separation

In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation.

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