A radar system and method of processing one or more return signals obtained by a receive section of a radar system resulting from transmitting one or more signals involve a transmit section to transmit the one or more signals, and a receive section to receive the one or more return signals resulting from reflection of the one or more signals by a target. The system also includes a processor to process the one or more return signals using a two-stage fast Fourier transform (FFT) to obtain a range-Doppler map indicating energy levels at each of a set of range values and a set of Doppler values, to filter the range-Doppler map using a kernel sized according to an estimate of a number of the set of range values over which the energy levels above a threshold value are spread, and to perform target detection based on a result of filtering.

Various technologies pertaining to motion compensation for radar systems using FM waveforms are described herein. A waveform generator outputs a parametrized, pulsed FM waveform to a radar antenna, whereupon the radar antenna emits pulsed electromagnetic (EM) radiation into a target area based upon the parametrized FM waveform. The parametrized FM waveform compensates for motion of a radar platform that includes the antenna. The parametrized FM waveform compensates for inter-pulse Doppler effects by introducing a time delay to a reference FM waveform, and compensates for intra-pulse Doppler effects by time dilating, or frequency-scaling, pulses of the reference waveform. The parametrized FM waveform can be generated by modifying the reference waveform based on first and second parameters, where the parameters are based upon motion of the radar platform and changes in echo return delay times from one pulse to another.

A system adaptively filters out a representation of an object from a radar frame captured by a radar device, where a maximum signal strength at zero velocity is obtained in a range bin comprising a detection of the object in range Doppler representations of a set of radar frames captured during a time period before the radar frame. A motion vector is obtained representing a determined magnitude and direction of motion of the radar device at the time when the radar frame was captured. The motion of the radar device is due to an oscillatory movement of the radar device. A range Doppler representation of the radar frame is produced and a direction vector representing a direction from the radar device to the object is determined. A radial relative velocity between the object and the radar device is determined based on the obtained motion vector and the determined direction vector.

A device includes: a millimeter-wave radar sensor circuit configured to generate N virtual channels of sensed data, where N is an integer number greater than one; and a processor configured to: generate a 2D radar image of a surface in a field of view of the millimeter-wave radar sensor circuit based on sensed data from the N virtual channels of sensed data, where the 2D radar image includes azimuth and range information, generate a multi-dimensional data structure based on the 2D radar image using a transform function, compare the multi-dimensional data structure with a reference multi-dimensional data structure, and determine whether liquid is present in the field of view of the millimeter-wave radar sensor circuit based on comparing the multi-dimensional data structure with the reference multi-dimensional data structure.

A system is disclosed. The system may include a receiver or transmitter node. The receiver or transmitter node may include a communications interface with an antenna element and a controller. The controller may include one or more processors and have information of own node velocity and own node orientation relative to a common reference frame. The receiver or transmitter node may be time synchronized to apply Doppler corrections to signals, the Doppler corrections associated with the receiver or transmitter node's own motions relative to the common reference frame, the Doppler corrections applied using Doppler null steering along Null directions. The receiver node is configured to determine a parameter of the signals and an authenticity of the signals based on the parameter.

A circuit comprises a receive processing window formation subsystem, a matched filter subsystem, a keystone interpolation subsystem, a phase modulation subsystem, and an image forming subsystem. The receive processing window formation subsystem forms, for each radar return from a scene, a receive processing window containing the radar return as an unbroken radar return. The matched filter subsystem creates a motion model for a reference point target disposed at a predetermined location within the scene, based on a set of motion compensation parameters for range and range rate, to compensate for at least some effects of fast time Doppler on the reference point target. The keystone interpolation subsystem rescales slow time information from the matched filter subsystem. A phase modulation subsystem applies phase modulations to a keystone-interpolated 2-D output array of information associated with the scene, to ensure proper registration in a range-Doppler map output of the scene.

A system adaptively filters out a representation of an object from a radar frame captured by a radar device, where a maximum signal strength at zero velocity is obtained in a range bin comprising a detection of the object in range Doppler representations of a set of radar frames captured during a time period before the radar frame. A motion vector is obtained representing a determined magnitude and direction of motion of the radar device at the time when the radar frame was captured. The motion of the radar device is due to an oscillatory movement of the radar device. A range Doppler representation of the radar frame is produced and a direction vector representing a direction from the radar device to the object is determined. A radial relative velocity between the object and the radar device is determined based on the obtained motion vector and the determined direction vector.

A method of filtering dynamic objects in radar-based ego-motion estimation includes converting measurement value at current time, measured by radar sensor, into point cloud, classifying the point cloud into points of a first object predicted as static object and points of a second object predicted as dynamic object, based on position value of dynamic object tracked at previous time, classifying the points of the first object into the points of the static object predicted as normal value and the points of the dynamic object predicted as outlier, based on outlier filtering algorithm, classifying the points of the second object into points of a candidate static object and points of a candidate dynamic object, based on velocity model of the static object, and tracking a position value of the dynamic object at current time, based on the points of the dynamic object and the points of the candidate dynamic object.

Methods, apparatus, systems and articles of manufacture are disclosed for distributed, multi-node, low frequency radar systems for degraded visual environments. An example system includes a transmitter to transmit a radar signal. The example system includes a distributed network of radar receivers to receive the radar signal at each receiver. The example system includes a processor to determine a first range and a first angular position of a background point based on return time, wherein the first range and the first angular position are included in first data; determine a second range and a second angular position of the background point based on doppler shift, wherein the second range and the second angular position are included in second data; determine a refined range and a refined angular position, wherein the refined range and refined angular position are included in third data, and generate a radar map based on third data.

A system for signal processing is provided that obviates the use of prior-knowledge, such as synthetic aperture radar (SAR) imagery, in time compressed signal processing (i.e. it can be knowledge unaided). The knowledge-unaided power centroid (PC.sub.KU) is found by evaluating a covariance matrix R.sub.SCM for its moments m.sub.i. Because R.sub.SCM uses a sample signal, rather than SAR data, the power centroid PC.sub.KU may be found without needing SAR data.