Signal processing in SETI

Cullers, D. K.; Linscott, I. R.; Oliver, Bernard M.

doi:10.1145/4547.4549

Cited by 22 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present work extends from SETI Institute's earlier campaign Project Phoenix (Backus 1996, Tarter 1996, Backus 1997, Backus 1998, Cullers 2000, Backus 2001, Backus et al 2004, which used large single dish telescopes to explore the radio spectrum one star at a time over a frequency range from 1.2 -3 GHz. Here we have used the interferometer capabilities of the ATA to observe 2 -3 stars at a time with SonATA's automated system in frequency ranges from 1-9 GHz.…”

Section: Introductionmentioning

confidence: 94%

“…Path sums that exceed the statistical threshold (Table 1, column 4) are deemed signals. The CW algorithm, the Doubling Accumulation Drift Detector, recursively uses partial sums to achieve performance for frequency bins and spectra (Cullers et al 1985). Triplets are sets of 3 or more pulses that occur along a line in the sparse frequency-time plane, with nearly equal time spacing between pulses.…”

Section: Signal Processingmentioning

confidence: 99%

See 1 more Smart Citation

Seti Observations of Exoplanets With the Allen Telescope Array

Harp

Richards

Tarter

et al. 2016

View full text Add to dashboard Cite

We report radio SETI observations on a large number of known exoplanets and other nearby star systems using the Allen Telescope Array (ATA). Observations were made over about 19000 hours from May 2009 to Dec 2015. This search focused on narrow-band radio signals from a set totaling 9293 stars, including 2015 exoplanet stars and Kepler objects of interest and an additional 65 whose planets may be close to their Habitable Zone. The ATA observations were made using multiple synthesized beams and an anticoincidence filter to help identify terrestrial radio interference. Stars were observed over frequencies from 1-9 GHz in multiple bands that avoid strong terrestrial communication frequencies. Data were processed in near-real time for narrow-band (0.7-100 Hz) continuous and pulsed signals, with transmitter/receiver relative accelerations from -0.3 to 0.3 m/s 2 . A total of 1.9 x 10 8 unique signals requiring immediate follow-up were detected in observations covering more than 8 x 10 6 star-MHz. We detected no persistent signals from extraterrestrial technology exceeding our frequency-dependent sensitivity threshold of 180 -310 10 -26 W m -2 .

show abstract

Section: Introductionmentioning

confidence: 94%

Section: Signal Processingmentioning

confidence: 99%

Seti Observations of Exoplanets With the Allen Telescope Array

Harp

Richards

Tarter

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Introduction. The authors first learned of the problem of detecting signals with linearly drifting frequencies in discussions with scientists associated with the Search for Extraterrestrial Intelligence (SETI) project [3] at NASA Ames Research Center (see Acknowledgments). Signals with drifting frequencies result from continuous-wave (CW) transmissions between any locations that are accelerating with respect to one another.…”

mentioning

confidence: 99%

“…Improve the accuracy of the existing method. In this paper, we will pursue options 2 and 3 in the context of an existing method described in [3] and [4] where the fast Fourier transform (FFT) is used to compute, but not accumulate, the spectra. We show that the FFT can be used in all phases of the computation.…”

mentioning

confidence: 99%

Efficient Detection of a Continuous-Wave Signal with a Linear Frequency Drift

Bailey¹,

Swarztrauber²

1995

SIAM J. Sci. Comput.

View full text Add to dashboard Cite

An efficient method is presented for the detection of a continuous-wave (CW) signal with a frequency drift that is linear in time. Signals of this type occur if the transmitter and receiver are rapidly accelerating with respect to one another, for example, as in interplanetary and space communications. We assume that both the frequency and the drift are unknown. We also assume that the signal is weak compared with the Gaussian noise. The signal is partitioned into subsequences whose discrete Fourier transforms provide a sequence of instantaneous spectra at equal time intervals. These spectra are then accumulated with a shift that is proportional to time. When the shift is equal to the frequency drift, the signal-to-noise ratio increases and detection occurs. In this paper, we show how to compute these accumulations for many shifts in an efficient manner using a variant of the fast Fourier transform (FFT). Computing time is proportional to LlogL, where L is the length of the time series. Computational results are presented.

show abstract

“…The multidimensional search space describes the unknown signal parameters such as frequency, strength, and position on the sky. This large increase in coverage is due to advances in signal processing, microelectronics, and computer science (Cullers, Linscott, and Oliver, 1985). The capabilities of the 10 million channel Multichannel Spectrum Analyzer system will stretch the state of the art in computation, achieving the equivalent power of hundreds of supercomputers through its highly parallel architecture.…”

mentioning

confidence: 99%