SETI Detection Strategies for Single Dish Radio Telescopes

Radio Searches for Extra Terrestrial Intelligence aim at detecting artificial transmissions from extra terrestrial communicative civilizations. The lack of prior knowledge concerning these potential transmissions increase the search parameter space. Ground-based single dish radio telescopes offer high sensitivity, but standard data products are limited to power spectral density estimates. To overcome important classical energy detector limitations, two detection strategies based on asynchronous ON and OFF astronomical target observations are proposed. Statistical models are described to enable threshold selection and detection performance assessment

Spatial filtering experiment with the Murchison Widefield Array

Spatial Radio Frequency Interference (RFI) filtering offers both RFI rejection and potential signal-of-interest recovery. It is as such an attractive RFI mitigation technique for radio interferometry. This paper describes an experiment of spatial filtering of an amateur radio transmission originating from the International Space Station corrupting the Murchison Widefield Array low-frequency radio telescope

A Cross-Correlation based Spectral Kurtosis RFI Detector

Accurate flagging of Radio Frequency Interference (RFI) is necessary to recover instrumental efficiency and avoid false astronomical detections. Spectral Kurtosis ((SK)̂) is a popular operator in RFI flagging for radio astronomy due to its detection sensitivity to non-Gaussian emissions and its competitive computational cost. Most (SK)̂ detection pipelines are applied to single antennas or autocorrelations products. This paper investigates the application of the (SK)̂ to antennas cross-correlations, and demonstrates an improved detection performance compared to the auto-correlation-based approaches

Cyclic Imaging for All-Sky Interference Forecasting with Array Radio Telescopes

Radio Frequency Interference (RFI) is threatening modern radio astronomy. A classic approach to mitigate its impact on astronomical data involves discarding the corrupted time and frequency data samples through a process called flagging and blanking. We propose the exploitation of the cyclostationary properties of the RFI signals to reliably detect and predict their locations within an array radio telescope field-of-view, and dynamically schedule the astronomical observations such as to minimize the probability of RFI data corruption

A Cross-Correlation based Spectral Kurtosis RFI Detector

Accurate flagging of Radio Frequency Interference (RFI) is necessary to recover instrumental efficiency and avoid false astronomical detections. Spectral Kurtosis ((SK)̂) is a popular operator in RFI flagging for radio astronomy due to its detection sensitivity to non-Gaussian emissions and its competitive computational cost. Most (SK)̂ detection pipelines are applied to single antennas or autocorrelations products. This paper investigates the application of the (SK)̂ to antennas cross-correlations, and demonstrates an improved detection performance compared to the auto-correlation-based approaches

No bursts detected from FRB121102 in two 5-hour observing campaigns with the Robert C. Byrd Green Bank Telescope

Here, we report non-detection of radio bursts from Fast Radio Burst FRB 121102 during two 5-hour observation sessions on the Robert C. Byrd 100-m Green Bank Telescope in West Virginia, USA, on December 11, 2017, and January 12, 2018. In addition, we report non-detection during an abutting 10-hour observation with the Kunming 40-m telescope in China, which commenced UTC 10:00 January 12, 2018. These are among the longest published contiguous observations of FRB 121102, and support the notion that FRB 121102 bursts are episodic. These observations were part of a simultaneous optical and radio monitoring campaign with the the Caltech HIgh- speed Multi-color CamERA (CHIMERA) instrument on the Hale 5.1-m telescope.Comment: 1 table, Submitted to RN of AA

Performance analysis of the Karhunen–Loève Transform for artificial and astrophysical transmissions: denoizing and detection

In this work, we propose a new method of computing the Karhunen–Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo (MC) simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a remarkable improvement in computation time and MC simulations show significant reconstruction results for signal-to-noise ratio (SNR) down to −10 dB and comparable results with standard signal detection techniques. The application to artificial signals, such as the Voyager 1 data, shows a notable gain in SNR after the KLT

Performance analysis of the Karhunen–Loève Transform for artificial and astrophysical transmissions: denoizing and detection

In this work, we propose a new method of computing the Karhunen–Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo (MC) simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a remarkable improvement in computation time and MC simulations show significant reconstruction results for signal-to-noise ratio (SNR) down to −10 dB and comparable results with standard signal detection techniques. The application to artificial signals, such as the Voyager 1 data, shows a notable gain in SNR after the KLT