The radio system (10) comprises a waveform generator (1) alternately generating an FMCW wave representing a linearly frequency-modulated continuous wave for radar imaging and a CW wave representing a wave kept at a given frequency for measuring a velocity vector, an amplification chain (2), a set (4) of transmit antennas (41, 42, 43), a set (5) of receive antennas (51, 52, 531, 532), a set (7) of receivers (71-2, 731, 732), and a signal processor (9) implementing processing operations on FMCW signals received from the one or more lateral antennas (51, 52) of the set (5) of receive antennas (51, 52, 531, 532) and spectrally analysing CW signals received from the one or more lateral antennas (51, 52) and from the one or more ventral antennas (531, 532) of the set (5) of receive antennas (51, 52, 531, 532) so as to supply SAR images and components of the velocity vector of said airborne vehicle (20).

The radio system (10) comprises a waveform generator (1) alternately generating an FMCW wave representing a linearly frequency-modulated continuous wave for radar imaging and a CW wave representing a wave kept at a given frequency for measuring a velocity vector, an amplification chain (2), a set (4) of transmit antennas (41, 42, 43), a set (5) of receive antennas (51, 52, 531, 532), a set (7) of receivers (71-2, 731, 732), and a signal processor (9) implementing processing operations on FMCW signals received from the one or more lateral antennas (51, 52) of the set (5) of receive antennas (51, 52, 531, 532) and spectrally analysing CW signals received from the one or more lateral antennas (51, 52) and from the one or more ventral antennas (531, 532) of the set (5) of receive antennas (51, 52, 531, 532) so as to supply SAR images and components of the velocity vector of said airborne vehicle (20).

The present technology relates to an image processing method and a data structure of metadata that allow for image processing on a plurality of captured images obtained by operation of a formation flight. A satellite cluster management device and an image analysis server as image processing apparatuses perform predetermined image processing on the basis of satellite specification information for specifying an artificial satellite associated as metadata with a captured image captured by the artificial satellite. The present technology can be applied to, for example, artificial satellites that perform satellite remote sensing by a formation flight.

A synthetic-aperture-radar-signal processing device includes a range bin selection unit which selects, from an observed image, range bins including a signal from an isolated reflection point, a phase evaluation unit which evaluates phases in an azimuth direction in the range bins, a window function multiplication unit which designs a window function on the basis of results of the evaluation by the phase evaluation unit and multiply the range bins by the window function, and a phase error correction unit which corrects the observed image by estimating a phase error from the range bins multiplied by the window function.

A synthetic-aperture-radar-signal processing device includes a range bin selection unit which selects, from an observed image, range bins including a signal from an isolated reflection point, a phase evaluation unit which evaluates phases in an azimuth direction in the range bins, a window function multiplication unit which designs a window function on the basis of results of the evaluation by the phase evaluation unit and multiply the range bins by the window function, and a phase error correction unit which corrects the observed image by estimating a phase error from the range bins multiplied by the window function.

A maritime radar system is provided, comprising a transmitter, a receiver, and one or more processors arranged to provide range and azimuth discrimination of a detection area by performing a delay/Doppler analysis of the echo of a single beam transmitted by the transmitter and received by the receiver.

A method for detecting objects and labeling the objects with distances in an image includes steps of: obtaining a thermal image from a thermal camera, an RGB image from an RGB camera, and radar information from an mmWave radar; adjusting the thermal image based on the RGB image to generate an adjusted thermal image, and generating a fused image based on the RGB image and the adjusted thermal image; generating a second fused image based on the fused image and the radar information; detecting objects in the images, and generating, based on the fused image, another fused image including bounding boxes marking the objects; and determining motion parameters of the objects.

Techniques are described herein that enable advanced gaming and virtual reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or submillimeter scale, for user control actions even when those actions are optically occluded or obscured.

Techniques are described herein that enable advanced gaming and virtual reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or submillimeter scale, for user control actions even when those actions are optically occluded or obscured.

A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.