A contextual visual-based synthetic-aperture radar (SAR) target detection method and apparatus, and a storage medium, belonging to the field of target detection is described. The method includes: obtaining an SAR image; and inputting the SAR image into a target detection model, and positioning and recognizing a target in the SAR image by using the target detection model, to obtain a detection result. In the present disclosure, a two-way multi-scale connection operation is enhanced through top-down and bottom-up attention, to guide learning of dynamic attention matrices and enhance feature interaction under different resolutions. The model can extract the multi-scale target feature information with higher accuracy, for bounding box regression and classification, to suppress interfering background information, thereby enhancing the visual expressiveness. After the attention enhancement module is added, the detection performance can be greatly improved with almost no increase in the parameter amount and calculation amount of the whole neck.

A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The one or more controllers execute instructions to determine a signal to noise ratio (SNR) distance ratio for the input detection points generated by the plurality of radar sensors, where a value of the SNR distance ratio is indicative of an object being a ghost vehicle. The one or more controllers also determine an effective particle number indicating a degree of particle degradation for the importance sampling for each variable that is part of the state variable. In response to determining the effective particle number is equal to or less than a predetermined threshold, the one or more controllers estimate a ghost position for the ghost vehicle.

A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The controller executes instructions to determine time-matched clusters that represent objects located in an environment surrounding the autonomous vehicle based on the input detection points from the plurality of radar sensors. The controller determines an adjusted signal to noise (SNR) measure for a specific time-matched cluster by dividing an SNR of the specific time-matched cluster by a range measurement of the specific time-matched cluster. The controller determines a velocity-ratio measure of the time-matched cluster by dividing a motion-based velocity by a Doppler-frequency velocity, and identifies the time-matched cluster as either a ghost object or a real object.

The present disclosure provides a synthetic aperture radar (SAR) image target detection method. The present disclosure takes the anchor-free target detection algorithm YOLOX as the basic framework, reconstructs the backbone feature extraction network from the lightweight perspective, and replaces the depthwise separable convolution in MobilenetV2 with one ordinary convolution and one depthwise separable convolution. The number of channels in the feature map is reduced by half through the ordinary convolution, features input from the ordinary convolution are further extracted by the depthwise separable convolution, and the convolutional results from the two convolutions are spliced. The present disclosure highlights the unique strong scattering characteristic of the SAR target through the attention enhancement pyramid attention network (CSEMPAN) by integrating channels and spatial attention mechanisms. In view of the multiple scales and strong sparseness of the SAR target, the present disclosure uses an ESPHead.

A flying body includes an observation data generation unit that generates observation data on the basis of radio waves received by a radar, an image generation unit that generates an image representing a monitoring space on the basis of the observation data generated by the observation data generation unit, and a detection unit that detects a detection target on the basis of the image generated by the image generation unit.

A method for integrating a vision-based navigation system into an aircraft navigation algorithm includes the step of obtaining a previous aircraft location as a latitude-longitude grid coordinate; implementing vision odometry based location determination with several steps. The method includes the step of using one or more digital cameras to obtain a set of digital images of the landscape beneath the aircraft, identifying a set of key points in the landscape using a specified feature point detection algorithm. The method includes the step of detecting a movement in the set of key points between two frames in a time interval. The method includes the step of, based on the movement of the set of key points between the two frames to infer the motion attributes of the aircraft. The method includes the step of locating aircraft imagery by matching it against a georeferenced satellite imagery database. The method includes the step of combining visual odometry and satellite imagery matching methods to obtain aircraft location.

A synthetic aperture radar (SAR) system is disclosed. The SAR comprises a memory, a convolutional neural network (CNN), a machine-readable medium on the memory, and a machine-readable medium on the memory. The machine-readable medium storing instructions that, when executed by the CNN, cause the SAR system to perform operations. The operation comprises: receiving range profile data associated with observed views of a scene; concatenating the range profile data with a template range profile data of the scene; and estimating registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.

Disclosed are a method and apparatus for SAR image data enhancement, and a storage medium. The method includes: processing an SAR target image by electromagnetic simulation to acquire an SAR electromagnetic simulation image; and processing the SAR electromagnetic simulation image and the SAR target image by a generative adversarial network to obtain a set of virtual samples of the SAR target image.

Various embodiments of the present technology generally relate to systems, methods, and computer-readable media for simulating synthetic aperture radar (SAR) images to be captured by a radar-based imaging system. SAR technology can be used to capture large areas on Earth, from a satellite in space for example, over a single pass. A further pass over the target area can help identify changes in the landscape, scenery, and/or infrastructure providing insight on change detection, temporal analysis, or other measures; however, repeat passes over the target area may have been made from differing angles resulting in artifacts in one or both of the processed images from the two passes. In various embodiments, information about the topology of the target area, and information about the SAR platform's flight path are used to simulate the slant range distortion effects that are to be expected in the SAR image of for that pass.

A method for selective boundary detection includes identifying a plurality of boundaries for a plurality of subregions in a region of interest utilizing one or more multispectral images for the region of interest. The method further includes analyzing a plurality of adjacent fields to a first field in a first subregion out of the plurality of subregions utilizing a region identification criterion based on a plurality of attributes for the first field and the plurality of adjacent fields. The method further includes determining, based on the analyzing, the first region with the first field requires further analysis of multitemporal remote sensed data over a defined period of time.