Real-Time RFI Processor for the Next Generation Satellite Radiometers

Lahtinen, J.; Kovanen, Arhippa; Lehtinen, K. E. J.; Kristensen, Steen Savstrup; Sobjrerg, Sten Schmidl; Skou, N.; D’Addio, Salvatore

doi:10.1109/microrad.2018.8430725

Cited by 4 publications

(1 citation statement)

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“…Some examples include: statistical detection methods (where the statistics of the received signal are estimated and normality is tested, [4][5][6][7][8][9][10]), polarimetric methods (where the cross-polarization components may indicate the presence of RFI, [11,12]), spatial filtering techniques (where the RFI is blanked using the differences in the Direction of Arrival, DOA [13,14]), parametric techniques (where some RFI properties are known a priori, and therefore it can be estimated and subtracted [15]), and time and/or frequency analysis [4,[16][17][18][19]. Some of these techniques can also be combined for improved effectiveness, as in some recent on-board implementations [20][21][22]. In this work, only time/frequency analysis is taken into consideration, as in general, RFI type and properties are not known beforehand.…”

Section: Introductionmentioning

confidence: 99%

Impact of Signal Quantization on the Performance of RFI Mitigation Algorithms

Díez-García

Camps

2019

Remote Sensing

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Radio Frequency Interference (RFI) is currently a major problem in Communications and Earth Observation, but it is even more dramatic in Microwave Radiometry because of the low power levels of the received signals. Its impact has been attested in several Earth Observation missions. On-board mitigation systems are becoming a requirement to detect and remove affected measurements, increasing thus radiometric accuracy and spatial coverage. However, RFI mitigation methods have not been tested yet in the context of some particular radiometer topologies, which rely on the use of coarsely quantized streams of data. In this study, the impact of quantization and sampling in the performance of several known RFI mitigation algorithms is studied under different conditions. It will be demonstrated that in the presence of clipping, quantization changes fundamentally the time-frequency properties of the contaminated signal, strongly impairing the performance of most mitigation methods. Important design considerations are derived from this analysis that must be taken into account when defining the architecture of future instruments. In particular, the use of Automatic Gain Control (AGC) systems is proposed, and its limitations are discussed.

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