A method of operating a radar sensor system includes: frequency down-converting a reception signal that is chirp-modulated with a sequence of chirp ramps to an intermediate frequency signal; and high-pass filtering the intermediate frequency signal to produce a high-pass filtered signal. High-pass filtering includes: first high-pass filtering, with a first corner frequency, the intermediate frequency signal at each chirp in the chirp modulation of the reception signal; and replacing the first high-pass filtering with a second high-pass filtering with a second corner frequency, the first corner frequency being higher than the second corner frequency.

A method of operating a radar sensor system includes: frequency down-converting a reception signal that is chirp-modulated with a sequence of chirp ramps to an intermediate frequency signal; and high-pass filtering the intermediate frequency signal to produce a high-pass filtered signal. High-pass filtering includes: first high-pass filtering, with a first corner frequency, the intermediate frequency signal at each chirp in the chirp modulation of the reception signal; and replacing the first high-pass filtering with a second high-pass filtering with a second corner frequency, the first corner frequency being higher than the second corner frequency.

A radio detection and ranging (radar) operating apparatus includes: radar sensors configured to receive signals reflected from an object; and a processor configured to generate Doppler maps for the radar sensors based on the reflected signals and estimate a time difference between the radar sensors based on the generated Doppler maps.

A radio detection and ranging (radar) operating apparatus includes: radar sensors configured to receive signals reflected from an object; and a processor configured to generate Doppler maps for the radar sensors based on the reflected signals and estimate a time difference between the radar sensors based on the generated Doppler maps.

A method for training a model for use in monitoring a health parameter in a person is disclosed. The method involves receiving control data that corresponds to a control element, wherein the control data corresponds to a health parameter of a person, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from the control element, wherein the stepped frequency scanning data includes frequency and corresponding amplitude and phase data over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a health parameter of a person.

A method for training a model for use in monitoring a health parameter in a person is disclosed. The method involves receiving control data that corresponds to a control element, wherein the control data corresponds to a health parameter of a person, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from the control element, wherein the stepped frequency scanning data includes frequency and corresponding amplitude and phase data over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a health parameter of a person.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system's frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system's frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

A microstrip mixer of a microwave-doppler detecting module has a shared port, a local oscillator signal input port and a mixing output port and includes two mixer tubes. The local oscillator signal input port is electrically connected with the shared port and is electrically connected with an end of one of the mixer tubes through the shared port. The local oscillator signal input port is also electrically connected with the mixing output port and is electrically connected with an end of the other mixer tube through the mixing output port. The other ends of the two mixer tubes are respectively grounded. Accordingly, the microstrip mixer is configured to have an open structure having three ports, which is more flexible and enhances the applicability thereof while reducing the size of the microwave-doppler detecting module utilizing the microstrip mixer.

A system for monitoring a health parameter in a person is disclosed. The system includes a frequency synthesizer configured to generate radio waves across a range of stepped frequencies, at least one transmit antenna configured to transmit the radio waves below the skin surface of a person, a two-dimensional array of receive antennas configured to receive radio waves, the received radio waves including a reflected portion of the transmitted radio waves, processing circuits configured to generate data that includes amplitude and phase data in response to the received radio waves, and means for determining a value that corresponds to a health parameter of the person in response to the amplitude and phase data.