An Eigenvalue-Based Multi-Antenna RFI Detection Algorithm

Getu, Tilahun M.; Ajib, Wessam; Landry, René

doi:10.1109/vtcfall.2018.8690863

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…Nonetheless, time-domain eigenvalue detection had not been investigated until recently. In line with this specific motivation, [39] has disseminated a preliminary study regarding an eigenvaluebased multi-antenna RFI detection. Following this lead, we make the following contributions.…”

Section: B Motivationmentioning

confidence: 99%

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Eigenvalue-Based RF Interference Detector for Multi-Antenna Wireless Communications

Getu¹,

Ajib²,

Landry³

2020

Preprint

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<div>Abstract</div><div><br></div><div>Radio frequency interference (RFI) is occurring in both satellite and terrestrial communication systems. In order to mitigate RFI efficiently, it has to be detected robustly. Toward this end, through the computation of an eigenvalue-based test statistic, an eigenvalue-based blind RFI detector is proposed for single-input multiple-output systems that may suffer from RFI. For medium to large interference-tonoise ratio (INR) regimes and under sample starved settings, Monte-Carlo simulations corroborate that the proposed blind detector manifests a comparable detection performance with a generalized likelihood ratio test (GLRT) detector fed with the knowledge of the signal of interest (SOI) channel, and a matched subspace detector fed with the knowledge of the SOI and RFI channels. Such performance signifies the applicability of the proposed RFI detector for real-time applications. <br></div>

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Section: B Motivationmentioning

confidence: 99%

“…Based on the lead of [39], this article presents a full-fledged investigation on eigenvalue-based RFI detector and its performance assessment. Being consequences of the conducted investigation, the contributions of this paper are itemized beneath.…”

Section: Contributionsmentioning

confidence: 99%

Eigenvalue-Based RF Interference Detector for Multi-Antenna Wireless Communications

Getu¹,

Ajib²,

Landry³

2020

Preprint

Self Cite

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“…These techniques can also be classified according to their domain of operation: time domain, frequency domain, statistical domain, polarimetry domain, or space domain (e.g., direction of arrival followed by beamforming or null-steering antennas). The performance of each technique is highly dependent on the RFI scenario, and the RFI D/M algorithms may combine techniques from several domains to be more effective [5], e.g., time-statistical domains [6], time-frequency domains [7], frequency-statistical domains [8], time-scale domains and Wavelet Packet Decomposition (WPD) [9], signal sub-spaces decomposition (Karhunen-Loève Transform or KLT) [10], Principal Component Analysis or PCA [11], Independent Component Analysis or ICA [12], multi-lag correlations [13], and time-space domains (e.g., adaptive beamforming/null-steering) [14,15].…”

Section: Introductionmentioning

confidence: 99%

RFI Detection and Mitigation for Advanced Correlators in Interferometric Radiometers

et al. 2022

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This work presents the first RFI detection and mitigation algorithm for the interferometric radiometers that will be implemented in its correlator unit. The algorithm operates in the time and frequency domains, applying polarimetric and statistical tests in both domains, and exhibiting a tunable and arbitrary low probability of false alarm. It is scalable to a configurable number of receivers, and it is optimized in terms of quantization bits and the implementation of the cross-correlations in the time or frequency domains for hardware resource saving. New features of this algorithm are the computation of the Stokes parameters per frequency bin in the Short-Time Fourier Transform and a new parameter called Polarimetric Kurtosis. If RFI is detected in one domain or in both, it is removed using the calculated blanking masks. The optimum algorithm parameters are computed, such as length of the FFTs, the threshold selection for a given probability of false alarm, and the selection of the blanking masks. Last, an important result refers to the application of Parseval’s theorem for the computation of the cross-correlations in the frequency domain, instead of in the time domain, which is more efficient and leads to smaller errors even when using moderate quantization levels. The algorithm has been developed in the framework of the ESA’s technology preparation for a potential L-band radiometer mission beyond SMOS. However, it is also applicable to (polarimetric) real aperture radiometers, and its performance would improve if more than one bit is used in the signal quantization.

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“…Signal subspaces, such as Karhunen-Loève transform (KLT) [17] and principal component analysis (PCA) [18]; • Time-space, such as adaptive beamforming and null-steering, space-and-time adaptive processing (STAP), precoding [19], and independent component analysis (ICA) [20].…”

mentioning

confidence: 99%

A Review of RFI Mitigation Techniques in Microwave Radiometry

2019

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Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.

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An Eigenvalue-Based Multi-Antenna RFI Detection Algorithm

Cited by 3 publications

References 27 publications

Eigenvalue-Based RF Interference Detector for Multi-Antenna Wireless Communications

Eigenvalue-Based RF Interference Detector for Multi-Antenna Wireless Communications

RFI Detection and Mitigation for Advanced Correlators in Interferometric Radiometers

A Review of RFI Mitigation Techniques in Microwave Radiometry

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