RFI Detection Research Articles

Fusion Method of RFI Detection, Localization, and Suppression by Combining One-Dimensional and Two-Dimensional Synthetic Aperture Radiometers

Ocean salinity is a pivotal aspect of the ocean dynamic environment. Spaceborne L-band radiometers, like SMOS, Aquarius, and SMAP, offer a comprehensive approach to mapping out large-scale ocean salinity patterns. As China prepares for the launch of the Chinese Ocean Salinity and Soil Moisture Mission (COSM), it is essential to delve into the intricacies of radio frequency interference (RFI) detection, localization, and mitigation. The L-band, in particular, is highly susceptible to RFI. COSM will carry not one but two advanced instruments: a 2-D L-band aperture synthesis microwave radiometer (LASMR) and a 1-D L-C-K band microwave imager combined active and passive (MICAP). This article delves into the current state of RFI research, particularly in recent years, and introduces a fusion method that integrates MICAP and LASMR for more accurate RFI detection, localization, and mitigation. This fusion method relies on an algorithm that constructs localization and intensity objective functions based on the principle of least squares. By optimizing these functions, we can pinpoint the precise location and intensity of RFI. The resulting minimum mitigation residual offers a blueprint for achieving optimal RFI detection, localization, and mitigation. The experimental results, achieved in a controlled anechoic chamber, confirm that this fusion method—when weighted by variance—boosts detection accuracy, refines localization precision, and minimizes residual mitigation errors compared with standalone MICAP or LASMR techniques.

RFI Detection and Mitigation for Advanced Correlators in Interferometric Radiometers

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.

Learning to detect radio frequency interference in radio astronomy without seeing it

ABSTRACT Radio frequency interference (RFI) corrupts astronomical measurements, thus affecting the performance of radio telescopes. To address this problem, supervised-segmentation models have been proposed as candidate solutions to RFI detection. However, the unavailability of large labelled data sets, due to the prohibitive cost of annotating, makes these solutions unusable. To solve these shortcomings, we focus on the inverse problem: training models on only uncontaminated emissions, thereby learning to discriminate RFI from all known astronomical signals and system noise. We use nearest latent neighbours – an algorithm that utilizes both the reconstructions and latent distances to the nearest neighbours in the latent space of generative autoencoding models for novelty detection. The uncontaminated regions are selected using weak labels in the form of RFI flags (generated by classical RFI flagging methods) available from most radio astronomical data archives at no additional cost. We evaluate performance on two independent data sets, one simulated from the Hydrogen Epoch of Reionization Array (HERA) telescope and the other consisting of real observations from the Low-Frequency Array (LOFAR) telescope. Additionally, we provide a small expert-labelled LOFAR data set (i.e. strong labels) for evaluation of our and other methods. Performance is measured using the area under the receiver operating characteristic (AUROC), area under precision–recall curve (AUPRC), and the maximum F1-score for a fixed threshold. For the simulated HERA data set, we outperform the current state of the art across all metrics. For the LOFAR data set, our algorithm offers both a 4 per cent increase in AUROC and AUPRC at the cost of increasing the false negative rate, but without any manual labelling.

Introducing Machine Learning in the Ground RFI Detection System (GRDS)

Introducing Machine Learning in the Ground RFI Detection System (GRDS)

Development of an On-Board Wide-Band Processor for Radio Frequency Interference Detection and Filtering

The demand for microwave spectrum for commercial and industrial use has been increasing rapidly over the last decade, putting stress on the limited spectral resources for passive microwave remote sensing. Radio frequency interference from man-made sources is expected to become worse over the coming years. At 1.4 GHz, the SMAP mission has implemented and demonstrated advanced interference detection algorithms for its microwave radiometer. This scheme will not be feasible at higher microwave frequencies (above 6 GHz) due to much larger radiometer bandwidths used and the limited downlink data volume available to implement RFI filtering algorithms in the ground processing. In this paper, we present the design, development, and test of an advanced on-board interference detection and RFI filtering digital back-end that is capable of operation for a 1 GHz-radiometer bandwidth. We describe the combined RFI detection algorithms implemented in the digital backend’s firmware and the on-board RFI filtering of interference-corrupted data that will be necessary to limit downlink rate requirements for future high-frequency microwave missions.

SERPent: Automated reduction and RFI-mitigation software for e-MERLIN

The Scripted E-merlin Rfi-mitigation PipelinE for iNTerferometry (SERPent) is an automated reduction and RFI-mitigation procedure utilising the SumThreshold methodology (Offringa et al. 2010b), originally developed for the LOFAR pipeline. SERPent is written in the Parseltongue language enabling interaction with the Astronomical Image Processing Software (AIPS) program. Moreover, SERPent is a simple "out of the box" Python script, which is easy to set up and is free of compilers. In addition to the flagging of RFI affected visibilities, the script also flags antenna zero-amplitude dropouts and Lovell telescope phase calibrator stationary scans inherent to the e-MERLIN system. Both the flagging and computational performances of SERPent are presented here, for e-MERLIN commissioning datasets for both L-band (1.3 - 1.8 GHz) and C-band (4 - 8 GHz) observations. RFI typically amounts to < 20 - 25% for the more problematic L-band observations and < 5% for the generally RFI quieter C-band. The level of RFI detection and flagging is more accurate and delicate than visual manual flagging, with the output immediately ready for AIPS calibration. SERPent is fully parallelised and has been tested on a range of computing systems. The current flagging rate is at 110 GB/day on a "high-end" computer (16 CPUs, 100 GB memory) which amounts to ~ 6.9 GB/CPU/day, with an expected increase in performance when e-MERLIN has completed its commissioning. The refining of automated reduction and calibration procedures is essential for the e-MERLIN legacy projects and future interferometers such as the SKA and the associated pathfinders (MeerKAT and ASKAP), where the vast data sizes (> TB) make traditional astronomer interactions unfeasible.

Detecting RFI Through Integrity Monitoring at a DGPS Reference Station

Because GPS is a radio navigation system which has a very low power level, it is vulnerable to RFI. Excessive RFI could cause receiver performance degradation, such as degradation of position accuracy, loss of lock and increased acquisition time. After GPS modernization plans introduce dual-frequency civil signals to mitigate ionospheric errors, RFI will remain as one of the dominant threats for differential GPS navigation systems. Examples of safety-critical civil aviation and military missions threatened by RFI include the Local Area Augmentation System (LAAS) and the Joint Precision Approach and Landing System (JPALS). This paper focuses on RFI mitigation through integrity monitoring for a DGPS system like LAAS or JPALS. The mitigation strategy consists of two parts. First, the paper develops a new RFI detection method, using a raw divergence statistic. Second, the paper investigates strategies for maintaining integrity in the case that RFI is detected.To validate the utility of the divergence-based RFI monitor, this paper takes an experimental approach. The experiments assess the performance of the divergence metric and compare it to existing alternatives for RFI detection, such as metrics for Automatic Gain Control (AGC) and carrier-to-noise ratio (C/N0). Generating a monitoring threshold for these statistics proves challenging, because the threshold depends both on the type of RFI threat (e.g. continuous wave, narrow band, wideband, pulsed) and on environmental conditions, such as temperature. As experiments illustrate, the divergence statistic resolves these limitations, as divergence directly estimates ranging source error, independent of the type of RFI threat or the environmental conditions.

Letter: Impact of RFI Detection in DRS Links on the Performance of Coding Schemes

AbstractThis paper deals with Data Relay Satellite systems operating in the S‐band environment and using Binary Phase Shift Keying modulation with concatenated Forward Error Correction data protection. One of the major sources of their performance degradation is radar generated Radio Frequency Interference in the same band. Transmission channel performance would certainly be improved if RFI positions (called erasures) were detected and passed to the decoders. By allowing the codes to handle about twice as many erasures as errors, this information would significantly reduce the effects of RFI deterioration.A novel technique for RFI detection is developed. It is based on a bank of simple “moving windows” associated with a detection algorithm and a method for the removal of ambiguous RFI detections. The use of this erasure information in the Viterbi decoding process is then illustrated and a new rule for updating the decoding metric is proposed.The residual correlation on the errors at the Viterbi decoder output makes the outer code useless, unless a further interleaving is introduced.Extensive simulation results highlighting the good performance of the joint RFI detection and modified Viterbi decoding schemes are presented. Simulation is also used to assess the outer code behavior.