[Illustration: (a) (b)]
[Illustration: (c) (d)]
[Illustration: (e)
Figure 3—Adaption of the memory at-6 db SNR: (a) Blank initial memory;
(b) Memory after first dump; (c) Memory after 12 dumps; (d) Memory
after 40 dumps; (e) Perfect “checkerboard” memory for comparison]
As the machine memory adapts to this noisy input signal, it progresses
as shown in Figure 3. The sign of 10^{3} memory components are
displayed in a raster pattern in this figure. Figure 3a shows the
memory in its blank initial state at the start of the adaption process.
Figure 3b shows the memory after the first adaption of the memory. This
first “dump” occurred after the threshold had decayed to the point
where an energy measurement produced an acceptance decision. Figure
3c and 3d show the memory after 12 and 40 adaptions, respectively.
These dumps, of course, are based on both energy and cross-correlation
measurements. As can be seen, the adapted memory after 40 dumps is
already quite close to the perfect memory shown by the “checkerboard”
pattern of Figure 3c.
The detailed analysis of the performance of this type of machine
vs. signal-to-noise ratio, average signal repetition rate, signal
duration, and machine parameters is extremely complex. Therefore, it
is not appropriate here to detail the results of the analytical and
experimental work on the performance of this machine. However, several
conclusions of a general nature can be stated.
(a) Because the machine memory is always adapting, there
is a relatively high penalty for “false alarms.”
False alarms can destroy a perfect memory. Hence,
the threshold level needs to be set appropriately
high for the memory adaption. If one wishes to
detect signal occurrences with more tolerance to
false alarms, a separate comparator and threshold
level should be used.
(b) The present machine structure, which allows for
slowly varying changes in the signal waveshape,
exhibits a marked threshold effect in steady-state
performance at an input signal-to-noise ratio
(peak signal power-to-average noise power ratio)
of about -12 db. Below this signal level, the time
required for convergence increases very rapidly with
decreasing signal level. At higher SNR, convergence
to noise-like signals, having good auto-correlation
properties, occurs at a satisfactory rate.
A more detailed discussion of performance has been published in the
report cited in footnote reference 1.