Abstract: A method for generating substitute correction data for GNSS-based localization of a mobile device is disclosed. The method includes: a) recognizing that receipt of correction data from at least one correction data source is currently impaired, b) reading of correction data that has been received earlier, and c) generating substitute correction data for the current situation by using at least part of the correction data that has been received earlier.

Abstract: A method for determining a protection level of a position estimate using a single epoch of GNSS measurements, the method includes: specifying a prior probability density P(x) of a state x; specifying a system model h(x) that relates the state x to observables z of the measurements; quantifying quality metrics q associated with the measurements; specifying a non-Gaussian residual error probability density model f(r|?, q) and fitting model parameters ? using a set of experimental data; and defining a posterior probability density P(x|z, q, ?); estimating the state x; and computing the protection level by integrating the posterior probability density P(x|z, q, ?) over the state x.

Abstract: A method for determining a protection level of a position estimate using a single epoch of GNSS measurements, the method includes: specifying a prior probability density P(x) of a state x; specifying a system model h(x) that relates the state x to observables z of the measurements; quantifying quality metrics q associated with the measurements; specifying a non-Gaussian residual error probability density model ƒ(r|?, q) and fitting model parameters ? using a set of experimental data; and defining a posterior probability density P(x|z, q, ?); estimating the state x; and computing the protection level by integrating the posterior probability density P(x|z, q, ?) over the state x.

Abstract: A method for determining a protection level of a position estimate using a single epoch of GNSS measurements, the method includes: specifying a prior probability density P(x) of a state x; specifying a system model h(x) that relates the state x to observables z of the measurements; quantifying quality metrics q associated with the measurements; specifying a non-Gaussian residual error probability density model ƒ(r|?, q) and fitting model parameters ? using a set of experimental data; and defining a posterior probability density P(x|z, q, ?); estimating the state x; and computing the protection level by integrating the posterior probability density P(x|z, q, ?) over the state x.