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Present methods being used for the evaluation of the peak-to-peak signal and RMS noise are perhaps satisfactory at high signal-to-noise levels, but at low signal-to-noise levels, where many low light level tubes operate, these methods are inadequate. Even at large light levels, signals which can ultimately be detected by the human eye (which integrates for periods of up to 1/5 second) will be buried in the noise of the video waveform (which is usually determined by the large bandwidth of about 10 MHz in the video channel). Any routine image tube measurement, such as spectral response or spatial resolution, will therefore need an averaging technique of one kind or another to eliminate the video noise and permit an accurate measurement over the entire useful range of signal levels. In addition, certain applications also require a knowledge of the noise itself. For instance, the amplitude distribution of the noise is very important in determining the "false alarm rate" of a system designed to detect a specific target. At the limiting resolution of the system, a large noise pulse would be indistinguishable from the target itself. That noise pulse would be a "false alarm". The false alarm rate of a system can only be predicted if the amplitude distribution of the noise is known. In the analysis of photoelectronic imaging devices, the present methods for measuring signal and noise (Ref. 1) include those where a time exposure of an oscilloscope trace is taken, allowing the film to average out the noise. The width of the "grass" displayed (assumed to indicate the ± 3a value of a Gaussian distribution) is divided by six and used as an estimate of the RMS noise value. Peak-to-peak signal is determined by measuring the positive extremes of the noise envelope. An improvement on this, while still subjective in nature, involves using a split screen presentation on the oscilloscope with the unknown signal on one side and a known RMS value on the other. By varying the known waveform amplitude until a match between the two sections is achieved, and then reading the value from the known source, the RMS value can be determined.
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