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 radar apparatus and a radar signal processing method are provided. The radar apparatus includes a plurality of transmitting antennas, a plurality of non-uniformly and linearly deployed receiving antennas, a sensor signal processor configured to calculate target range-Doppler data from signals input from a receiving antenna arrangement according to virtual antennas while sequentially driving the plurality of transmitting antennas, and a target position calculator configured to calculate position data of a target from arrangement mapped data obtained by rearranging the virtual antenna-specific range-Doppler data output from the sensor signal processor with reference to antenna configuration related information.

A method for synthetic aperture radar signal processing includes storing signal responses of a radar signal in a memory buffer, wherein the stored signal responses are represented by a two-dimensional signal in an azimuth dimension and a range dimension. The method further includes frequency filtering the two-dimensional signal in the azimuth dimension. In addition, the method includes applying a Fourier transformation to the frequency filtered signal in the range dimension. The method further includes generating a synthetic aperture radar image based on the Fourier transformed frequency filtered signal.

A method for synthetic aperture radar signal processing includes storing signal responses of a radar signal in a memory buffer, wherein the stored signal responses are represented by a two-dimensional signal in an azimuth dimension and a range dimension. The method further includes frequency filtering the two-dimensional signal in the azimuth dimension. In addition, the method includes applying a Fourier transformation to the frequency filtered signal in the range dimension. The method further includes generating a synthetic aperture radar image based on the Fourier transformed frequency filtered signal.

A high-throughput communications channel is encoded using transmit waveforms which satisfy a variety of technical constraints deemed desirable for effective radar operations and signal processing. This enables new cooperative spectrum sharing modalities for radar and communications systems.

A high-throughput communications channel is encoded using transmit waveforms which satisfy a variety of technical constraints deemed desirable for effective radar operations and signal processing. This enables new cooperative spectrum sharing modalities for radar and communications systems.

The disclosure discloses a channel combining and time-division processing circuit of a dual-plane pulse Doppler radar seeker. The circuit includes a time-division control circuit configured to receive a time-division control signal, control input of an elevation difference channel signal and an azimuth difference channel signal, combine the elevation difference channel signal and the azimuth difference channel signal and output a combined difference channel signal, and a hybrid bridge circuit configured to receive a sum channel signal, combine channels for the sum channel signal and the combined difference channel signal and output signals on a combined channel. With the circuit of the disclosure, signals received from a sum channel, an azimuth difference channel and an elevation difference channel can be combined into received signals from two channels for processing with one received signal processing channel hardware omitted.

The disclosure discloses a channel combining and time-division processing circuit of a dual-plane pulse Doppler radar seeker. The circuit includes a time-division control circuit configured to receive a time-division control signal, control input of an elevation difference channel signal and an azimuth difference channel signal, combine the elevation difference channel signal and the azimuth difference channel signal and output a combined difference channel signal, and a hybrid bridge circuit configured to receive a sum channel signal, combine channels for the sum channel signal and the combined difference channel signal and output signals on a combined channel. With the circuit of the disclosure, signals received from a sum channel, an azimuth difference channel and an elevation difference channel can be combined into received signals from two channels for processing with one received signal processing channel hardware omitted.

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.