Method , carried out by at least one of a navigation satellite system receiver, hereinafter abbreviated as NSS receiver, and a processing entity capable of receiving data from the NSS receiver, for estimating parameters useful to determine a position, the NSS receiver observing a NSS signal from each of a plurality of NSS satellites over multiple epochs, the method comprising: receiving (s320) input data comprising at least one of: NSS signals observed by the NSS receiver, and information derived from said NSS signals; and operating (s340) a first filter , hereinafter referred to as robustifier, and a second filter , hereinafter referred to as main estimator, wherein each of the robustifier and main estimator uses state variables, and computes the values of their respective state variables at least based on the received input data; the robustifier identifies, within the received input data, any measurement that does not match a stochastic model assigned thereto; for each measurement identified by the robustifier as not matching its stochastic model, at least one of the following applies: the robustifier rejects the measurement, and the main estimator does not use the rejected measurement; the robustifier adjusts the stochastic model assigned to the measurement, and the main estimator uses the adjusted stochastic model; and the robustifier corrects the measurement, and the main estimator uses the corrected measurement; characterised in that the robustifier uses fewer state variables than the main estimator . receiving (s320) input data comprising at least one of: NSS signals observed by the NSS receiver, and information derived from said NSS signals; and operating (s340) a first filter , hereinafter referred to as robustifier, and a second filter , hereinafter referred to as main estimator, wherein each of the robustifier and main estimator uses state variables, and computes the values of their respective state variables at least based on the received input data; the robustifier identifies, within the received input data, any measurement that does not match a stochastic model assigned thereto; for each measurement identified by the robustifier as not matching its stochastic model, at least one of the following applies: the robustifier rejects the measurement, and the main estimator does not use the rejected measurement; the robustifier adjusts the stochastic model assigned to the measurement, and the main estimator uses the adjusted stochastic model; and the robustifier corrects the measurement, and the main estimator uses the corrected measurement; each of the robustifier and main estimator uses state variables, and computes the values of their respective state variables at least based on the received input data; the robustifier identifies, within the received input data, any measurement that does not match a stochastic model assigned thereto; for each measurement identified by the robustifier as not matching its stochastic model, at least one of the following applies: the robustifier rejects the measurement, and the main estimator does not use the rejected measurement; the robustifier adjusts the stochastic model assigned to the measurement, and the main estimator uses the adjusted stochastic model; and the robustifier corrects the measurement, and the main estimator uses the corrected measurement; the robustifier rejects the measurement, and the main estimator does not use the rejected measurement; the robustifier adjusts the stochastic model assigned to the measurement, and the main estimator uses the adjusted stochastic model; and the robustifier corrects the measurement, and the main estimator uses the corrected measurement; characterised in that the robustifier uses fewer state variables than the main estimator . <EOS>