A method and apparatus for receiving signals from an unknown transmitting source and providing the location of the unknown transmitting source comprising a series of channels for receiving signals radiated by the unknown transmitting sources, generating preprocessed time domain data and generating a SAR image depicting a location of the unknown transmitting source, and a processor for processing the preprocessed time domain data to enhance a pixel value at each pixel location within the SAR image by summing signal data from each channel related to each pixel location to generate an enhanced SAR image.

A target detection and/or high resolution RF system is provided herein in which the resolution of a legacy target angle detection (direction of arrival) system is improved without any change to the existing hardware of the legacy target detection system. Rather, the target detection and/or high resolution RF system can apply virtual aperture postprocessing to reflected signals to achieve improvements in the detection of one or more targets.

Embodiments are disclosed that for synthetic aperture radar (SAR) systems and methods. Front-end circuitry transmits radar signals, receives return radar signals, and outputs digital radar data. FFT circuits process the digital radar data without zero-padding to generate FFT data corresponding to oversampled pixel range values. A processor further processes the FFT data to generate radar pixel data representing a radar image. Further, the FFT circuits can interpolate the FFT data based upon pixel ranges using a streamlined range computation process. This process pre-computes x-axis components for pixels in common rows and y-axis components for pixels in common columns within the FFT data. For one embodiment, a navigation processor is coupled to a SAR system within a vehicle, receives the radar pixel data, and causes one or more actions to occur based upon the radar pixel data, such as an advanced driver assistance system function or an autonomous driving function.

Described is a stripmap SAR system on a vehicle comprising an antenna that is fixed and directed outward from the side of the vehicle, a SAR sensor, a storage, and a computing device. The computing device comprises a memory, one or more processing units, and a machine-readable medium on the memory. The machine-readable medium stores instructions that, when executed by the one or more processing units, cause the stripmap SAR system to perform various operations. The operations comprise: receiving stripmap range profile data associated with observed views of a scene; transforming the received stripmap range profile data into partial circular range profile data; comparing the partial circular range profile data to a template range profile data of the scene; and estimating registration parameters associated with the partial circular range profile data relative to the template range profile data to determine a deviation from the template range profile data.

A subsurface interferometric synthetic aperture radar (InSAR) imaging technique for the detection and localization of underground targets in the presence of a rough ground surface comprises a two-step procedure. First, surface clutter suppression is performed with a polarimetric difference operation that does not alter the propagation phase of the target scattered signal; then a subsurface interferometric algorithm is applied to infer target depth by correlating the clutter-suppressed images obtained along two observation paths.

Example embodiments relate to underbody radar units. An example radar system may involve a set of radar units coupled to an underbody of a vehicle such that each radar unit has a field of view below a bumper line of the vehicle. The set of radar units may include a first radar unit configured to measure an environment of the vehicle in a first direction and a second radar unit configured to measure the environment of the vehicle in a second direction. The second direction differs from the first direction. In some implementations, the first radar unit is positioned proximate a front bumper of the vehicle, and the second radar unit is positioned proximate a back bumper of the vehicle. Other example configurations may involve using more or fewer radar units coupled to the underbody of a vehicle.

A system for detecting objects in space comprises an array of satellite nodes. The array of satellite nodes comprise at least one transmitter module for transmitting an electromagnetic signal, and a plurality of receiver modules for receiving diffractions from electromagnetic waves scattered from objects in space. The system comprises a control module for focussing the plurality of receiver modules to receive diffractions from a focussed virtual aperture in space.

A method and apparatus for generating a NLFM signal in real time, and a computer storage medium are disclosed, including: determining a signal parameter of a signal according to a system parameter, the signal parameter includes: a signal bandwidth, a signal pulse width and a PSLR; determining a power spectrum density function according to PSLR; calculating the power spectrum density function to obtain a group delay vector; calculating a frequency axial vector according to a system sampling rate; calculating a time axial vector according to the signal pulse width; performing linear interpolation calculation on the group delay vector to obtain an instantaneous frequency vector; integrating the instantaneous frequency vector to obtain a phase vector; determining a signal time domain discrete vector; and generating a digital signal according to the signal time domain discrete vector, and performing digital-to-analog conversion on the digital signal to obtain the NLFM signal.

Disclosed are a method and an apparatus for end-to-end SAR image recognition, and a storage medium. According to the disclosure, a generative adversarial network is used to enhance data and improve data richness of a SAR image, which is beneficial to subsequent network training; a semantic feature enhancement technology is also introduced to enhance semantic information of a SAR deep feature by a coding-decoding structure, which improves performances of SAR target recognition; and meanwhile, an end-to-end SAR image target recognition model with high integrity for big scenes like the Bay Area is constructed, which is helpful to improve a synthetic aperture radar target recognition model for big scenes like the Bay Area from local optimum to global optimum, increases the stability and generalization ability of the model, reduces the network complexity, and improves the target recognition accuracy.

A subsurface interferometric synthetic aperture radar (InSAR) imaging technique for the detection and localization of underground targets in the presence of a rough ground surface comprises a two-step procedure. First, surface clutter suppression is performed with a polarimetric difference operation that does not alter the propagation phase of the target scattered signal; then a subsurface interferometric algorithm is applied to infer target depth by correlating the clutter-suppressed images obtained along two observation paths.