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 and apparatus are provided for processing a plurality of GNSS measurements to infer state information. An example method includes obtaining a plurality of quality indicators associated with each GNSS measurement. A space of joint values of the plurality of quality indicators is divided into at least a first region and a second region. Neither the first region nor the second region is box-shaped. The method determines whether the plurality of quality indicators fall within the first region or the second region. If the plurality of quality indicators fall within the first region, the GNSS measurement in question is included in the processing to infer the state information. Also provided is a method of estimating residual error models for GNSS measurements, based on training data.

Abstract: A method and apparatus are disclosed for processing GNSS measurements. The GNSS measurements comprise carrier phase measurements. A state vector is defined, comprising state variables. A posterior probability density for the state vector is obtained, which is based on non-Gaussian residual error models for the GNSS measurements. A systematic search of the posterior probability density is performed, to identify a set of modes of the probability density.

Abstract: A method and apparatus are provided for processing GNSS measurements to infer state information. An example method includes obtaining one or more residual error models for the plurality of GNSS measurements. The one or more residual error models describe a probability distribution of errors in each of the GNSS measurements. The method further includes inferring the state information based on the one or more residual error models. The GNSS measurements include at least one carrier phase measurement. The residual error model for the at least one carrier phase measurement is cyclic, such that errors in carrier phase that are separated by an integer number of cycles are regarded as equivalent. The probability distribution for the at least one carrier phase measurement comprises a function having a continuous first derivative, for example, a continuous first derivative at a phase boundary between successive cycles.

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 camera lens assembly comprising a lens assembly housing and a compound lens and a motion sensor arrangement within the lens assembly housing, wherein the motion sensor arrangement comprises: angular rotation sensors configured to detect angular rotation about three orthogonal axes of rotation; and linear acceleration sensors configured to detect linear acceleration in three orthogonal directions.

Abstract: A camera lens assembly comprising a lens assembly housing and a compound lens and a motion sensor arrangement within the lens assembly housing, wherein the motion sensor arrangement comprises: angular rotation sensors configured to detect angular rotation about three orthogonal axes of rotation; and linear acceleration sensors configured to detect linear acceleration in three orthogonal directions.