radar
Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge.
To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios.
The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions).
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The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information.
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Current radar fall detection techniques based on deep learning (DL) networks are often too complex for real-time detection. This paper proposes a real-time fall detection approach by reducing the complexity of the DL networks and the UWB radar hardware requirements. A multi-indoor scene behaviour dataset of 40 subjects is established using K-band UWB radar. A sliding window-based dataflow augmentation method is proposed to augment and balance the given datasets.
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This dataset contains the extracted parameter data for the deep patellar tendon reflexes of four test subjects. Each subject was tapped with a reflex hammer with soft, medium, and hard taps three times. The dataset was collected by interpreting the spectrogram images from processed radar data and motion capture data.
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Evolving from the well-known ray-tracing dataset DeepMIMO, the DeepVerse 6G dataset additionally provides multi-modal sensing data generated from various emulators. These emulators provide the wireless, radar, LiDAR, vision and position data. With a parametric generator, the DeepVerse dataset can be customized by the user for various communication and sensing applications.
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This is the dataset we collected for the article "Scalable Undersized Dataset RF Classification: Using Convolutional Multistage Training". 17 objects were collected in the laboratory and scanned using a 'cw radar' setup featuring 2x UWB antennas (1 transmit antenna, 1 receive antenna), inside anechoic chamber. There was no clutter added in the experiment.
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This paper introduces an edge-controlled autonomous robot with a gyro-stabilized active suspension system in form of a hybrid quadrupedal wheel drive mechanism, capable of detecting free pathways with an angular resolution of 1 degree and steering the robot in that direction. This features the computer-aided prototyping of the robot as a complete multisensory mechatronic system. Also, several algorithmic models were used in developing the robot’s software, which includes suspension control and pathfinding algorithms.
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This dataset is used for the interference detection in high frequency over-the-horizon (OTH) radar, especially the sky-wave mode. The range-Doppler (RD) grey images are categorized into three types, i.e. the radio frequency interference (RFI), the transient interference (TSI), and no interference (NoI). Two databases are included. The simulated database is constructed based on signal models of interference, clutter and noise. The real database is constructed based on real data from OTH radars.
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test
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