GNSS Signal Processing Methods and Apparatus with Scaling of Quality Measure
First Claim
1. Method to estimate parameters derived from global navigational satellite system (GNSS) signals useful to determine a position, comprisingobtaining observations of a GNSS signal from each of a plurality of GNSS satellites;
- feeding the observations to a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals, each float ambiguity constituting a real number estimate associated with an integer number of wavelengths of the GNSS signal between a receiver of the GNSS signal and the GNSS satellite from which it is received, and the filter being for estimating a float value for each float ambiguity of the state vector;
assigning integer values to at least a subgroup of the estimated float values to define a plurality of integer ambiguity candidate sets;
determining a quality measure for each of the candidate sets;
determining the best quality measure of the candidate sets;
determining an expectation value of the candidate set having the best quality measure;
determining an error measure as a ratio of the best quality measure to the expectation value;
adapting the quality measures of the candidate sets as a function of the error measure; and
forming a weighted average of a subgroup of the candidate sets on the basis of the adapted quality measures, wherein at least one of selecting the subgroup of the candidate sets and the weighting of each candidate set in the weighted average is based on the adapted quality measure.
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Accused Products
Abstract
Methods and apparatus are provided for estimating parameters, i.e. ambiguities, derived from GNSS signals. Observations of GNSS signals are obtained from each of a plurality of GNSS satellites (1120). The observations are fed to a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals (1140). The filter estimates a float value for each float ambiguity of the state vector. Integer values are assigned to at least a subgroup of the estimated float values to define a plurality of integer ambiguity candidate sets (1160). A quality measure is determined for each of the candidate sets. The best quality measure of the candidate sets is determined. An expectation value of the candidate set having the best quality measure is determined (1 170). An error measure as a ratio of the best quality measure to the expectation value is determined. The quality measures of the candidate sets is adapted as a function of the error measure (1180). A weighted average of a subgroup of the candidate sets on the basis of the adapted quality measures is formed, wherein at least one of selecting the subgroup of the candidate sets and the weighting of each candidate set in the weighted average is based on the adapted quality measure (1200). Ambiguities of the weighted average can be used in subsequent operations to aid in determining a position of the receiver or can be used to prepare data, e.g., in a network processor that can be used to augment position information of a rover.
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Citations
48 Claims
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1. Method to estimate parameters derived from global navigational satellite system (GNSS) signals useful to determine a position, comprising
obtaining observations of a GNSS signal from each of a plurality of GNSS satellites; -
feeding the observations to a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals, each float ambiguity constituting a real number estimate associated with an integer number of wavelengths of the GNSS signal between a receiver of the GNSS signal and the GNSS satellite from which it is received, and the filter being for estimating a float value for each float ambiguity of the state vector; assigning integer values to at least a subgroup of the estimated float values to define a plurality of integer ambiguity candidate sets; determining a quality measure for each of the candidate sets; determining the best quality measure of the candidate sets; determining an expectation value of the candidate set having the best quality measure; determining an error measure as a ratio of the best quality measure to the expectation value; adapting the quality measures of the candidate sets as a function of the error measure; and forming a weighted average of a subgroup of the candidate sets on the basis of the adapted quality measures, wherein at least one of selecting the subgroup of the candidate sets and the weighting of each candidate set in the weighted average is based on the adapted quality measure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 48)
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23. Apparatus to estimate parameters derived from global navigational satellite system (GNSS) signals useful to determine a position, comprising:
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a receiver adapted to obtain observations of a GNSS signal from each of a plurality of GNSS satellites; a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals, each float ambiguity constituting a real number estimate associated with an integer number of wavelengths of the GNSS signal between a receiver of the GNSS signal and the GNSS satellite from which it is received, and the filter being for estimating a float value for each float ambiguity of the state vector; a processing element adapted to assign integer values to at least a subgroup of the estimated float values to define a plurality of integer ambiguity candidate sets; determine a quality measure for each of the candidate sets; determine the best quality measure of the candidate sets; determine an expectation value of the candidate set having the best quality measure; determine an error measure as a ratio of the best quality measure to the expectation value; adapt the quality measures of the candidate sets as a function of the error measure; and form a weighted average of a subgroup of the candidate sets on the basis of the adapted quality measures, wherein at least one of selecting the subgroup of the candidate sets and the weighting of each candidate set in the weighted average is based on the adapted quality measure. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46)
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47. (canceled)
Specification