Method for estimating jamming in a global navigation satellite system receiver

A method for training a machine learning model by a computing device for estimating jamming in a global navigation satellite system (GNSS) receiver, the method comprising:

• receiving by the receiver GNSS signals at a plurality of radio frequency bands and processing by the receiver the received signals at a plurality of respective intermediate frequency bands, wherein the received GNSS signals comprise GNSS signals being jammed and GNSS signals not being jammed;
• logging by the receiver a set of parameters corresponding to the GNSS signals being jammed and the GNSS signals not being jammed at the respective intermediate frequency bands;
• and
• training by the computing device the machine learning model based on the set of logged parameters and the corresponding GNSS signals at the respective intermediate frequency bands.

A device comprising a global navigation satellite system (GNSS) receiving unit and a processing unit, the device being configured to:

• receive GNSS signals at a radio frequency band and process the received signals at an intermediate frequency band;
• collect a set of parameters at the device;
• and
• obtain a likelihood value using a machine learning model trained for the device, wherein the set of parameters are inputs to the machine learning model, the likelihood value is an output of the machine learning model and corresponds to a likelihood of the device being jammed in the intermediate frequency band.