Systems may include at least one processor configured to determine a predicted value of an unwrap factor using a machine learning model, wherein the machine learning model is a trained machine learning model configured to provide a predicted value of an unwrap factor for dealiasing a measurement of range rate of a target object as an output, dealiase a measurement value of range rate from a radar of an autonomous vehicle (AV) based on the predicted value of the unwrap factor to provide a true value of range rate, and control an operation of the AV in a real-time environment based on the true value of range rate. Methods, computer program products, and autonomous vehicles are also disclosed.

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.

Provided are a method and an apparatus for range ambiguity suppression based on orthogonal nonlinear frequency modulation (NLFM) waveforms. The method includes that: according to transmitted orthogonal NLFM signals, a waveform sequence of the transmitted signals corresponding to an obtained echo signal is determined; a set of range matched filters is constructed according to the waveform sequence and the orthogonal NLFM signals; range compression is performed on the echo signal by using the set of range matched filters to obtain range-compressed data; and synthetic aperture radar (SAR) imaging is performed according to the range-compressed data, to obtain an imaging result, and the imaging result is outputted. A non-transitory computer-readable storage medium is also provided.

The synthetic aperture radar image processing device includes time-series analysis unit which extracts persistent scatterers from time-series observation data for the observation direction for an observation area observed from multiple observation directions by a radar, and calculating displacement speeds of the extracted persistent scatterers, clustering unit which generates reflection point clusters by clustering extracted persistent scatterers based on their phase and position, distance calculation unit which calculates a distance between each of the persistent scatterers included in the reflection point clusters and each structure included in the observation area, representative value calculation unit which calculates each representative value for the distance between each persistent scatterer and each structure, for each reflection point cluster, and corresponding structure determination unit which associates the structure corresponding to the smallest representative value with the persistent scatterer, for each reflection point cluster.

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.

The invention concerns a monitoring method that comprises coupling in an integral manner at least one electromagnetic mirror of passive type with a given target to be monitored and monitoring the given target; wherein monitoring the given target includes: acquiring, via one or more synthetic aperture radar(s) installed on board one or more satellites and/or one or more aerial platforms, SAR images of a given area of the earth's surface where the given target is located; and determining, via a processing unit, a movement of the electromagnetic mirror on the basis of the acquired SAR images.

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.

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.

The present invention is a signal processing device that efficiently removes multiple types of interference waves mixed in with a received signal. This signal processing device is provided with: a first extraction unit that performs deconvolution with respect to a cross-correlation of a reference signal and a received signal and to an autocorrelation of the reference signal, and extracts a channel response to the reference signal; a second extraction unit that extracts a main channel response corresponding to the reference signal from the channel response; and a removal unit that restores a signal by performing convolution with respect to the reference signal and the main channel response, and removes the restored signal from the received signal.

A cloud platform-based garlic crop recognition method by coupling active and passive remote sensing images includes: firstly, obtaining an optical satellite remote sensing image based on phenological characteristics of garlic, and constructing a decision tree model for optical image recognition of the garlic by combining geographic coordinate information of the garlic, so as to obtain an optical distribution diagram of the garlic; secondly, obtaining radar image characteristics of the garlic and winter wheat based on a synthetic aperture radar satellite, and constructing a decision tree model for radar image recognition of the garlic by combining the geographic coordinate information of the garlic, so as to obtain a radar distribution diagram of the garlic; and finally, coupling the optical distribution diagram of the garlic with the radar distribution diagram of the garlic, i.e., selecting an intersection of the two distribution diagrams to complete remote sensing recognition drawing of the garlic.