1. A method of keying, in a space presenting two spatial dimensions and one temporal dimension, a signal S measured in positions U subject to an uncertainty, from a set of N signals measured in determined positions, the N+1 signals having their temporal origin in a same plane, said method comprising the steps of re-sampling the N+1 signals in order to place them all in an identical sampling range; filtering the signal S in order to place it in a range of frequencies that is identical to that of the N signals; defining for each position U associated with the measurements of the signal S a same neighbourhood of places V in the spatio-temporal space centred on the position U; producing a layered neural network RN v for each location V in the neighbourhood of U, each network having an entry vector of dimension N associated with the measurements of the N signals and a scalar exit associated with a measurement of the signal S; for each neural network RN v , defining a learning set such that the entries are the collection of all the vectors of measurements of the N signals situated at the locations V and the exits are the collection of the values of the signal S at the positions U for all the positions U; fixing a predetermined number of iterations Nit for all the neural networks and launching the learning phases of all the networks; for each neural network RN v , calculating the value of the integral Σ v of the function giving the error committed by the network at each iteration, from iteration 1 to iteration Nit; for each surface spatial position V k of the neighbourhood with coordinates (x k , y k , t 0 ), selecting in the time dimension the pair of locations V 1 k (x k , y k , t 1 ), V 2 k (x k , y k , t 2 ), of the neighbourhood which correspond to the two smallest local minima of the two integrals (Σ v1 k , Σ v2 k ); for each surface spatial position V k of the neighbourhood, retaining from among the two positions V 1 k (x k , y k , t 1 ), V 2 k (x k , y k , t 2 ) the position V m , for which the signal estimated by the respective neural networks RN v1 k and RN v2 k presents a maximum variance; and choosing from among the positions V m the position V cal for which the integral Σ v m is minimum.