Radio frequency interference mitigation using deep convolutional neural networks

Akeret, Joël; Chang, C.; Lucchi, Aurélien; Réfrégier, Alexandre

doi:10.1016/j.ascom.2017.01.002

Cited by 180 publications

(140 citation statements)

References 26 publications

Supporting

Mentioning

136

Contrasting

Unclassified

Order By: Relevance

“…4b. We compare segmentation results of trained MS-D networks with those of the popular UNet architecture [5]: We use a TensorFlow implementation [28]. U-Net architectures are similar to that shown in Fig.…”

Section: Simulated Datamentioning

confidence: 99%

A mixed-scale dense convolutional neural network for image analysis

Pelt

Sethian

2017

Proc. Natl. Acad. Sci. U.S.A.

235

207

View full text Add to dashboard Cite

Abstract-Deep convolutional neural networks have been successfully applied to many image processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters is often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales, and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.

show abstract

Section: Simulated Datamentioning

confidence: 99%

A mixed-scale dense convolutional neural network for image analysis

Pelt

Sethian

2017

Proc. Natl. Acad. Sci. U.S.A.

235

207

View full text Add to dashboard Cite

show abstract

“…Also, with the development of artificial intelligence in image recognition (Gómez-Ríos et al 2019) and natural language processing (Evans et al 2019), AI-related algorithms have been invoked by various branches in astronomy, from classifications of variable stars with enhanced performance in light-curve classification benchmarks (Aguirre et al 2018), to pulsar candidate identifications (Zhu et al 2014). Also, in the radio band, efforts have been made to apply CNN (Akeret et al 2017b) and RNN (Czech et al 2018) for RFI detection.…”

Section: Rfi Detection Methodsmentioning

confidence: 99%

“…Since the signal strength of RFI is usually much stronger than that of typical astronomical signals, classical algo-rithms are based on physical characteristics of RFI. One of the notable approaches is SumThreshold, which is one of the most widely used algorithms (Akeret et al 2017b). Introduced by Offringa et al (2010a), the SumThreshold method has been proved to yield the highest accuracy among classical detection algorithms.…”

Section: Classical Methods: Sumthreshold As An Examplementioning

confidence: 99%

“…Currently, artificial intelligence (AI) technology represented by deep learning techniques has been used to detect RFI. Burd et al (2018) has applied Recurrent Neural Network (RNN) to flag RFI, while Akeret et al (2017b) has performed similar tasks with Convolutional Neural Network (CNN). Czech et al (2018) combined RNN and CNN to classify transient RFI sources.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Deep residual detection of radio frequency interference for FAST

Zhi-cheng

Xiao

et al. 2020

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Radio frequency interference (RFI) detection and excision is one of the key steps in the data processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The FAST telescope, due to its high sensitivity and large data rate, requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using Convolutional Neural Network (CNN), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as training dataset preparation for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model named RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with less training data, thus saving effort and time required to prepare the training set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimised, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.

show abstract

“…Machine learning (ML) algorithm may be superior in providing the required flexibility and efficiency. As a matter of fact, Akeret et al () and Czech et al () have recently successfully applied different models of deep neural networks (DNNs) to identify RFI in data from single‐dish radio telescopes.…”

Section: Rfi Mitigation and The Machine‐learning Approachmentioning

confidence: 99%

Detecting radio frequency interference in radio‐antenna arrays with the recurrent neural network algorithm

Burd¹,

Mannheim²,

März³

et al. 2018

Astron Nachr

View full text Add to dashboard Cite

Signal artifacts due to radio frequency interference (RFI) are a common nuisance in radio astronomy. Conventionally, the RFI‐affected data are tagged by an expert data analyst in order to warrant data quality. In view of the increasing data rates obtained with interferometric radio telescope arrays, automatic data filtering procedures are mandatory. Here, we present the results from the implementation of a RFI‐detecting recurrent neural network (RNN) employing long short‐term memory (LSTM) cells. For the training of the algorithm, a discrete model was used that distinguishes RFI and non‐RFI data, based on the amplitude information from radio interferometric observations with the Giant Metre‐Wave Radio Telescope (GMRT) at 610 MHz. The performance of the RNN is evaluated by analyzing a confusion matrix. The true positive and true negative rates of the network are ∼99.9 and ∼97.9%, respectively. However, the overall efficiency of the network is ∼30% because of the fact that a large amount non‐RFI data is classified as being contaminated by RFI. Matthews correlation coefficient is 0.42, suggesting that a still more refined training model is required.

show abstract

Radio frequency interference mitigation using deep convolutional neural networks

Cited by 180 publications

References 26 publications

A mixed-scale dense convolutional neural network for image analysis

A mixed-scale dense convolutional neural network for image analysis

Deep residual detection of radio frequency interference for FAST

Detecting radio frequency interference in radio‐antenna arrays with the recurrent neural network algorithm

Contact Info

Product

Resources

About