When we examine in real time an APT signal from a weather satellite, the impression is that we have about twenty peaks next to each other, the amplitude of which varies in the same way. So about twenty copies of the same information.
With digital signal processing, could we take advantage of this redundancy to improve the signal-to-noise ratio?
I draw a parallel with an image processing soft, used by amateur astronomers to photograph a celestial object such as a planet for example. By means of a camera, we take a series of a few dozen images. Each of these images contains a disturbance caused by atmospheric turbulence. Noise. But this disturbance related to noise is different in each image. On the other hand, the image of the planet is identical in each image.
By superimposing several images, the software makes it possible to attenuate the noise and thus to improve the signal-to-noise ratio and to obtain a more mette and more contrasted image.
If each peak of the APT signal transmitted by the weather satellite contains the same information, could we imagine in order to improve the signal-to-noise ratio, a signal processing program which would cut the received spectrum into slices each corresponding to a peak and then add them all together to average the noise and thus improve the signal/noise ratio?