This radio astronomy project explores the premise for different ways of communicating outside of our solar system using natural, possibly serendipitous, methods of transmitting information to planets in nearby star systems; and correspondingly to test a different strategy for receiving information from our local galactic neighborhood.

The plan for SASERÓ , Solar Amplification by Stimulated Emission Radiation, is to investigate the effect, if any, that weak, transmitted microwave signals have on the solar radio continuum with the possibility of receiving these signals back on Earth. SASERÓ will attempt to bounce or mix or amplify (or some combination) a weak 400 watt microwave signal with the solar radio continuum and receive signal products back on Earth

The data presented was captured by the Jupiter Space Station (JSS) from reflected microwave signals of radio operators transmitting to the moon on November 16, 1997 during the ARRL EME Moonbounce Contest. The JSS likewise transmitted and received three successful lunar contacts via the moon.

This presentation presents preliminary data on successfully identifying weak radio signals at the noise floor of signals reflected off the moon. This identification technique, using spectrograms and EXCEL spreadsheets, will increase the probability of deciphering weak signals from space sources, and is a cornerstone process for attempting to execute the SASERÓ research plan found at http://members.carol.net/~saser.

Introduction

We would like to introduce you to the SASER Project more fully and show you our communication strategies for sending microwave signals to the Sun and analyzing the return signal products. We introduced SASER at Greenbank (NRAO) in 1996 and had our proposal reviewed by six experts in radio astronomy. The central theme of the reviewers was the weak nature of the return signals, if they exist at all. We felt that we needed to address the rear end first... thus the impetus for these DSP experiments.

First, you need to know why we are interested in this DSP so intently. We are putting together a Solar Project which can be summarized as:

A software and hardware vehicle known as the Jupiter Space Station (JSS) is the principal facility for conducting this project and it is fully computerized, has a steerable 12' dish, and is presently being upgraded to a larger dish. The JSS system was originally worked in conjunction with receiving gear (ICOM R71A) to monitor Jupiter at the decametric frequencies. The JSS station was converted to operation at 1296 MHz in 1997.

The data presented in this paper was captured by the Jupiter Space Station (JSS) from reflected microwave signals of radio operators transmitting to the moon on November 16, 1997 during the ARRL EME Moonbounce Contest. The JSS likewise transmitted and received three successful lunar contacts via the moon.

This presentation presents preliminary data on successfully identifying weak radio signals at the noise floor of signals reflected off the moon. This identification technique, using spectrograms and EXCEL spreadsheets, will increase the probability of deciphering weak signals from space sources, and is a cornerstone process for attempting to execute the SASERÓ research plan found at http://members.carol.net/~saser.

Methodology

Diagram 1 is a block diagram of the Jupiter Space Station transmitting and receiving equipment located in Pendleton, South Carolina. While collecting data on November 15-16, 1998 we pointed this system at the moon and both transmitted and received reflected signals off the lunar surface. These signals are characterized as being very weak and are at the noise floor (from 1-3 dB above the noise floor).

All the received signals are digitally recorded and an attempt is made while receiving the signal to respond to the transmission; we successfully made three lunar contacts. Later, after the "live" deciphering, we reviewed these 26 weak signals by human ear to decode them. In doing this we selected two or three candidate signals for computer analysis. The candidate signals are first examined with Spectral Plus and then reduced to an EXCEL spreadsheet. Spectra Plus is a signal analysis software package which portrays the signal characteristics of wav files of digitally recorded signals... and portrays these signals as a spectrum analyzer would portray them.

A spectrum analyzer is an instrument used to convert a signal from the time domain (amplitude vs time) to the frequency domain (amplitude vs frequency). Those familiar with an ordinary oscilloscope would know what a time domain display looks like. A frequency domain display is known as a spectrum. Unless you are measuring a single tone, an oscilloscope provides little in the way of frequency information; however, a spectrum analyzer clearly reveals this information. An Audio Spectrum Analyzer, by definition, is limited to processing signals in the audio band. The specific frequency limit is determined by the capabilities of your computer’s sound card.

An Audio Spectrum Analyzer is very useful for measuring the fundamental frequency components which are contained in an audio signal. It can accurately measure the frequency of single or multiple tones and the frequency difference between them. Software spectral analysis offers a powerful alternative at a low cost. Let us pose a radio astronomy question: "Can we say in this article that there exists a direct, if not equivalent, relationship between the signal energy in the original RF signal (before the detection phase) and the signal energy represented in the audio spectrum?" Obviously there are various radio receiver IF and AF gain stages which change the absolute energy level by significant amounts; however, the spectral characteristics of the signal of interest remains the same. Receiver IF and AF filters, as well as AGC, may only shape the signal.

Another question: "Can we say that if a radio has a bandwidth of 12.5 KHz then the signal spectrum (and energy) in that audio spectrum bandwidth is a map of the spectrum about the center frequency ± 12.5 KHz?" The detected signal is essentially a tunable window of the total RF spectrum, however, the pre-amplifier, filtering and detection stages are never exactly linear so you would be more accurate to say that the audio window is an approximation of the RF window.

The scientific goal is to create an analysis methodology which permits us to decode these otherwise weak signals so that they are readable. For the SASER project it is important to have a definitive analysis which helps us characterize the dits and dahs of a weakly received signal.

The Data

First we present a 54 second transmission (Figure 1) which illustrates a common problem of signals reflected off a moving/librating target. The received signal could have as much as a 25 Hz drift in a time of 60 seconds. In Figure 1 the signal centers on 803 Hz and drifts to approximately 815 Hz in 54 seconds.

Figure 1         ARRL EME Contest - November 16, 1997

                       10 Hz Signal Drift over 54 Second EME Reception

Second, we present an energy map of the signal (Figure 2) which clearly shows that the dits and dahs are not discernible in the noise... even though the human ear can pick out a lot of the dits and dahs. The upper window shows the ending of the received signal and noise mixed together, followed by a clean transmitted dah from our station. Looking carefully in the lower window of Figure 2, we see the entire received signal (30 seconds) followed by a clean transmission in dits and dahs of the word: OH2AXH. This word OH2AXH was repeated three times in the noisy portion of the lower window.

Figure 2 A Dah in the Upper Window with Noise and Signal Mixed in the Lower Window

In Figure 3, we can take a look at a clean set of dits as seen in the letter H in the lower window (dit dit dit dit).

Figure 3     Two Dits in the Upper Window and Noise and Signal Mixed in the Lower Window

The next issue in decoding the signals is looking at a spectrogram of the frequency intensity versus time. In Figure 4 the dark black blotches indicate a higher signal level (signal strength) and the spectrogram shows a side by side comparison of the "noisy" received signal along with the "clean" transmitted signal. In this Figure 4 these are the same two dahs in the Morse code of the letter O.

Figure 4 Spectrogram Rendition of Both Noisy and Clean Signal (2 Dahs)

This left hand portion of the spectrogram (Figure 4) is beginning to show a nicely formed identifying registration. This is probably 3 dB above the noise however, and is not too challenging.

Figure 5 A Buried in the Noise Letter A (dit dah)

In the spectrogram in Figure 5 one can see on the left side at 2.17 seconds the remnants of a signal. This remnant could be either dit-dit-dah or dit-dit-dit or dit-dah. We employ an EXCEL spreadsheet here to enhance these difficult sections. Basically, the particular signal segment in Figure 4 was not yet filtered. In this next signal analysis we applied a software bandpass filter which cuts everything from 825 Hz and upwards and 775 Hz and downwards. This in essence reduces the noise level, enhancing the signal portion.

Next we dump the filtered segment to a data file which can be read into EXCEL. The element of the signal we dump is the power level maker every 2.5 milliseconds. Then we graph the power level for the remnant signal in Figure 5.

Figure 5     EXCEL Graph of Weak Signal Remnant

In this graph one can see centered at 2.50 seconds a triple hump which can be only interpreted as a long dah because there is not a significant pause between the humps. The elevated signal humps are being "squashed" on both sides by the noise. Even a poorly transmitted Morse code signal would have 50 - 100 milliseconds of "quiet" between the humps.

The EXCEL graph enhances the true signal representation of the spectrogram to conclude that this signal portion at 2.50 seconds is a dah.

Conclusions

There are four conclusions from analyzing the data in this project:

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ARRL UHF/MICROWAVE Experimenter's Manual, pp.10.1 to 10.26, The American Radio Relay

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