US 2026/0093045 A1 · METHODS AND SYSTEMS FOR ENHANCED RANSAC SELECTION OF GNSS SIGNALS

Abstract

Machine learning techniques are used to perform RANSAC like processing in a GNSS receiver. A model (e.g., one or more neural networks) is trained to perform this processing to generate a selection of a subset of GNSS SVs. In one embodiment, the trained model is used during inferencing in a GNSS receiver. A method in a GNSS receiver can include the following operations: receiving GNSS signals from a plurality of SVs; extracting a set of features from the received GNSS signals, the set of features being predetermined based on a trained model in the GNSS receiver, the trained model having been trained to select a subset of GNSS SVs based on the set of features including RANSAC (random sample consensus) residuals; applying the set of features as an input to the trained model; generating, by the trained model, a selection of a subset of GNSS SVs based in part on RANSAC residuals and based on other features in the set of features; and computing a position solution using GNSS signals received from the subset of GNSS SVs.

Claims18

Dependent claims

A non-transitory machine readable medium storing executable program instructions which when executed by a data processing system cause the data processing system to perform a method of operating a global navigation satellite system (GNSS) receiver, the method comprising:

receiving GNSS signals from a set of GNSS satellites (GNSS SVs);
extracting a set of features from the received GNSS signals, the set of features being predetermined based on a trained model in the GNSS receiver, the trained model having been trained to select a subset of GNSS SVs based on the set of features including RANSAC (random sample consensus) residuals, wherein the trained model has been trained by computing an error for each training subset in a plurality of training subsets and each training subset comprises four GNSS SVs;
applying the set of features as an input to the trained model;
generating, by the trained model, a selection of a subset of GNSS SVs based in part on RANSAC residuals and based on other features in the set of features;
and
computing a velocity solution using GNSS signals received from the subset of GNSS SVs.

3 more elements

A global navigation satellite system (GNSS) receiver comprising:

an antenna to receive GNSS signals from GNSS satellites (GNSS SVs);
an RF front end coupled to the antenna to amplify the GNSS signals;
an analog to digital converter (ADC) coupled to the RF front end to generate a digital representation of received GNSS signals;
a baseband memory coupled to the ADC to store the digital representation;
a GNSS processing system coupled to the baseband memory to process the received GNSS signals, the GNSS processing system including a set of correlators that provide outputs that include correlation vectors;
wherein the GNSS processing system includes processing logic to extract a set of features from the received GNSS signals, the set of features being predetermined based on a trained model in the GNSS receiver, and the trained model having been trained to select a subset of GNSS SVs based on the set of features including RANSAC (random sample consensus) residuals, wherein the trained model has been trained by computing an error for each training subset in a plurality of training subsets and each training subset comprises four GNSS SVs;
wherein the GNSS processing system includes processing logic to apply the set of features as an input to the trained model;
and
wherein the GNSS processing system includes processing logic to generate, by the trained model, a selection of a subset of GNSS SVs based in part on RANSAC residuals and based on other features in the set of features and the GNSS processing system includes processing logic to compute a velocity solution using GNSS signals received from the selected subset of GNSS SVs.

6 more elements

Dependencies are inferred from the claim text.

Methods and systems for enhanced ransac selection of gnss signals

Life of this patent

Methods and systems for enhanced ransac selection of gnss signals

Life of this patent

Abstract

Claims18