A computer implemented method for estimating interferers of a radiofrequency system embarked in a moving vehicle having a known trajectory at each time n, with a non overlapping condition between the interferers considered as K independent active sources of interference having respective positions θ = [ θ 1,.., θ k,.., θ K], the method comprising: - Obtaining observations Z n corresponding to interference measured by the radiofrequency system embarked in the moving vehicle from time instant 1 to N, and building an observation vector Z = [ Z 1,..,Z n,..,Z N], - Defining a latent variable V n indicating which source is activated at moment n, to build a vector of latent variables V = [ V 1,.., V n,.., V N], and - Implementing a Dirichlet process involving a Gibbs sampling with a Markov chain defined by vector V = [ V 1,.., V n,.., V N], the sampling as follows, being repeated until convergence: ∘ For n = 1,...,N, - If the observation Z n is already associated to a source, remove observation Z n from its current associated source corresponding to latent variable V n, and retrieve a position posterior of this source as the observation Z n is no longer belonging to this source, - Draw a new value of latent variable V n, based on a conditional probability P ( V n = k |V - n,Z - n,Z n) depending on whether a source k pre-existed or not, - Associate the observation Z n to the source corresponding to latent variable V n, and update the posterior distribution of the position for the source corresponding to latent variable V n, - and, upon convergence of the algorithm, operating thereby: * a separation of the interfering sources into K independent measurement sets related respectively to the K interfering sources, and * an estimation of each source position with the sources thus separated. - Obtaining observations Z n corresponding to interference measured by the radiofrequency system embarked in the moving vehicle from time instant 1 to N, and building an observation vector Z = [ Z 1,..,Z n,..,Z N], - Defining a latent variable V n indicating which source is activated at moment n, to build a vector of latent variables V = [ V 1,.., V n,.., V N], and - Implementing a Dirichlet process involving a Gibbs sampling with a Markov chain defined by vector V = [ V 1,.., V n,.., V N], the sampling as follows, being repeated until convergence: ∘ For n = 1,...,N, - If the observation Z n is already associated to a source, remove observation Z n from its current associated source corresponding to latent variable V n, and retrieve a position posterior of this source as the observation Z n is no longer belonging to this source, - Draw a new value of latent variable V n, based on a conditional probability P ( V n = k |V - n,Z - n,Z n) depending on whether a source k pre-existed or not, - Associate the observation Z n to the source corresponding to latent variable V n, and update the posterior distribution of the position for the source corresponding to latent variable V n, ∘ For n = 1,...,N, - If the observation Z n is already associated to a source, remove observation Z n from its current associated source corresponding to latent variable V n, and retrieve a position posterior of this source as the observation Z n is no longer belonging to this source, - Draw a new value of latent variable V n, based on a conditional probability P ( V n = k |V - n,Z - n,Z n) depending on whether a source k pre-existed or not, - Associate the observation Z n to the source corresponding to latent variable V n, and update the posterior distribution of the position for the source corresponding to latent variable V n, - If the observation Z n is already associated to a source, remove observation Z n from its current associated source corresponding to latent variable V n, and retrieve a position posterior of this source as the observation Z n is no longer belonging to this source, - Draw a new value of latent variable V n, based on a conditional probability P ( V n = k |V - n,Z - n,Z n) depending on whether a source k pre-existed or not, - Associate the observation Z n to the source corresponding to latent variable V n, and update the posterior distribution of the position for the source corresponding to latent variable V n, - and, upon convergence of the algorithm, operating thereby: * a separation of the interfering sources into K independent measurement sets related respectively to the K interfering sources, and * an estimation of each source position with the sources thus separated. * a separation of the interfering sources into K independent measurement sets related respectively to the K interfering sources, and * an estimation of each source position with the sources thus separated.