Temporal rectification of radio frequency signals

A non-transitory, computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising

• training a machine learning network using, as input training data,
• a plurality of time series representing sampled radio frequency (RF) signals, and
• a plurality of labels, each label associated with at least one time series of the plurality of time series, the labels describing one or more characteristics of emitters of the sampled RF signals,
• wherein the machine learning network comprises, for each label of the plurality of labels, a corresponding soft dynamic time warping (soft-DTW) terminal associated with the label, the soft-DTW terminals comprising weights, wherein a value of each soft-DTW terminal is based on a soft-DTW loss function based on the weights of the soft-DTW terminal, and
• wherein training the machine learning network comprises iteratively adjusting the weights of the soft-DTW terminals so as to reduce a value of at least one loss function;
• obtaining, as a result of the training, a machine learning model configured to classify new RF signal sequences by label;
• and
• providing the machine learning model to an RF sensing device.

A system comprising:

• one or more computers;
• and
• one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• training a machine learning network using, as input training data,
• a plurality of time series representing sampled radio frequency (RF) signals, and
• a plurality of labels, each label associated with at least one time series of the plurality of time series, the labels describing one or more characteristics of emitters of the sampled RF signals,
• wherein the machine learning network comprises, for each label of the plurality of labels, a corresponding soft dynamic time warping (soft-DTW) terminal associated with the label, the soft-DTW terminals comprising weights, wherein a value of each soft-DTW terminal is based on a soft-DTW loss function based on the weights of the soft-DTW terminal, and
• wherein training the machine learning network comprises iteratively adjusting the weights of the soft-DTW terminals so as to reduce a value of at least one loss function;
• obtaining, as a result of the training, a machine learning model configured to classify new RF signal sequences by label;
• and
• providing the machine learning model to an RF sensing device.

A method comprising:

• receiving, at a sensing device, a radio-frequency (RF) signal;
• inputting a sampled time series of the RF signal into a machine learning model trained using a soft dynamic time warping (soft-DTW) loss function;
• and
• determining, based on the machine learning model, an identity of an emitter of the RF signal.