The present invention relates a remote sensing system, or particularly a satellite-formation-based remote sensing system, wherein comprising: a master satellite provided with an SAR system as a payload thereof, a first concomitant satellite, and a second concomitant satellite, wherein the first concomitant satellite and the second concomitant satellite fly around the master satellite, and the master satellite is located on major axes of motion trajectories of the first concomitant satellite and the second concomitant satellite, so as to define a first spatial baseline and a second spatial baseline that have an identical cross-track baseline component. The present invention enables high-precision, wide-range, three-dimensional imaging based on the satellite-formation, while acquires spatiotemporal features of variation of a ground region according to the synchronization in terms of time, frequency, and space.

A method for using Synthetic Aperture Radar (SAR) to perform a maneuver in a land vehicle is provided. The method includes: receiving digitized radar return data from a radar transmission from a SAR onboard the vehicle; accumulating a plurality of frames of the digitized radar return data; applying a RADON transform to the accumulated plurality of frames of the digitized radar return data and odometry data from the vehicle to generate transformed frames of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent integration for each three-dimensional point, project a radar trajectory onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generating a two-dimensional map of an area covered by the radar transmission from the SAR based on the transformed frames of data for each three-dimensional point; and performing a maneuver with the land vehicle by applying the generated two-dimensional map.

Various embodiments utilize application-based processing parameters to dynamically configure a radar-based detection system based upon an operating context of an associated device. A first application with execution priority on a device dynamically configures the radar-based detection system to emit a radar field suitable for a first operating context associated with the first application. The first application can also dynamically configure processing parameters of the radar-based detection system, such as digital signal processing parameters and machine-learning parameters. In some cases, a second application assumes execution priority over the first application, and dynamically reconfigures the radar-based detection system to emit a radar field suitable to a second operating context associated with the second application. Alternately or additionally, the second application can dynamically reconfigure the processing parameters of the radar-based detection system based upon the second operating context of the second application.

The present invention concerns a method for performing SAR acquisitions, which comprises performing SAR acquisitions in Spotlight/Stripmap mode of areas/swaths of earth's surface by means of a SAR system carried by an air or space platform along a flight direction, whereby: an azimuth direction is defined by a ground track of the flight direction on the earth's surface, a nadir direction is defined that is orthogonal to the earth's surface, to the flight direction and to the azimuth direction, an across-track direction is defined that lies on the earth's surface and is orthogonal to the azimuth direction and to the nadir direction, and, for each acquired area/swath of the earth's surface, a respective range direction is defined that extends from the synthetic aperture radar system to said acquired area/swath. Performing SAR acquisitions in Spotlight/Stripmap mode of areas/swaths of earth's surface includes contemporaneously acquiring P areas or portions of P swaths in a pulse repetition interval having a predefined time length, P being an integer greater than one. Said P areas/swaths are separated along the across-track direction and are spaced apart from each other along the across-track direction and from the SAR system along the respective range direction by predefined distances. Said predefined time length and said predefined distances are such that to enable contemporaneous acquisition of said P areas or of portions of said P swaths in said pulse repetition interval.

The signal processing device includes an interference processing unit which generates an interferogram from a plurality of SAR images, a coherence calculation unit which calculates coherence of the SAR images, a singular point processing unit which performs an operation for resolving singular points in the interferogram, a phase unwrapping unit which executes a phase unwrapping process using operation result of the singular point processing unit, and an SBAS analysis unit which performs displacement analysis by SBAS, using processing result of the phase unwrapping unit.

A radar system for generating a radar image of a scene includes an input interface to accept radar measurements of a scene collected from a set of antennas with clock ambiguities, wherein the radar measurements are measurements of reflections of a radar pulse transmitted to the scene, a hardware processor configured to solve a convex sparse recovery problem to produce a radar image of the scene, wherein the convex sparse recovery problem matches a time shift of the radar measurements with a signal generated by propagation of the radar pulse through a radar propagation function of the scene, wherein the time shift of the radar measurements is represented as a convolution of the radar measurements with a shift kernel that is one-sparse in time, and an output interface configured to render the radar image.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

This document describes techniques for fine-motion virtual-reality or augmented-reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or sub-millimeter scale, for user control actions even when those actions are small, fast, or obscured due to darkness or varying light. Further, these techniques enable fine resolution and real-time control, unlike conventional RF-tracking or optical-tracking techniques.

Various embodiments dynamically learn user-customizable input gestures. A user can transition a radar-based gesture detection system into a gesture-learning mode. In turn, the radar-based gesture detection system emits a radar field configured to detect a gesture new to the radar-based gesture detection system. The radar-based gesture detection system receives incoming radio frequency (RF) signals generated by the outgoing RF signal reflecting off the gesture, and analyzes the incoming RF signals to learn one or more identifying characteristics about the gesture. Upon learning the identifying characteristics, the radar-based gesture detection system reconfigures a corresponding input identification system to detect the gesture when the one or more identifying characteristics are next identified, and transitions out of the gesture-learning mode.