Abstract: Techniques and apparatuses are described that implement height-estimation of objects using radar. In particular, a radar system, which is mounted to a moving platform, receives reflection signals that represent versions of a radar signal that are reflected off of objects. The radar system generates a range-elevation map based on raw data from the reflection signals, identifies an elevation bin and a range bin in the range-elevation map that corresponds to a selected object, and calculates a height for the selected object based on the range and elevation bins. The radar system then calculates a de-noised height for the selected object based on one or more previously calculated heights for the selected object. In this way, the radar system can determine accurate heights of objects at sufficiently long ranges for evasive action.

Abstract: This document describes techniques and systems for fuzzy labeling of low-level electromagnetic sensor data. Sensor data in the form of an energy spectrum is obtained and the points within an estimated geographic boundary of a scatterer represented by the smear is labeled with a value of one. The remaining points of the energy spectrum are labeled with values between zero and one with the values decreasing the further away each respective remaining point is from the geographic boundary. The fuzzy labeling process may harness more in-depth information available from the distribution of the energy in the energy spectrum. A model can be trained to efficiently label an energy spectrum map in this manner. This may result in lower computational costs than other labeling methods. Additionally, false detections by the sensor may be reduced resulting in more accurate detection and tracking of objects.

Abstract: This document describes systems and techniques for determining a height of an object in a surrounding of a vehicle. In a first aspect, the systems and techniques include acquiring radar data for each of a plurality of vertically distributed antenna elements of a radar antenna. In additional aspects, the systems and techniques include estimating an elevation spectrum from the acquired radar data, extracting one or more features representative of the shape of the estimated elevation spectrum, and determining the height of the object using the extracted one or more features.

Abstract: This document describes techniques and systems for machine-learning-based super resolution of radar data. A low-resolution radar image can be used as input to train a model for super resolution of radar data. A higher-resolution radar image, generated by an effective, but costly in terms of computing resources, traditional super resolution method, and the higher-resolution image can serve as ground truth for training the model. The resulting trained model may generate a high-resolution sensor image that closely approximates the image generated by the traditional method. Because this trained model needs only to be executed in feed-forward mode in the inference stage, it may be suited for real-time applications. Additionally, if low-level radar data is used as input for training the model, the model may be trained with more comprehensive information than can be obtained in detection level radar data.

Abstract: This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

Abstract: Techniques and apparatuses are described that implement height-estimation of objects using radar. In particular, a radar system, which is mounted to a moving platform, receives reflection signals that represent versions of a radar signal that are reflected off of objects. The radar system generates a range-elevation map based on raw data from the reflection signals, identifies an elevation bin and a range bin in the range-elevation map that corresponds to a selected object, and calculates a height for the selected object based on the range and elevation bins. The radar system then calculates a de-noised height for the selected object based on one or more previously calculated heights for the selected object. In this way, the radar system can determine accurate heights of objects at sufficiently long ranges for evasive action.