- PAMI2024
- mmWave
- conference
- mmWave
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Range and Velocity Measurement in ACC
Millimeter wave radar is a type of radar that operates at frequencies above 30 GHz. It can detect objects with higher resolution and speed, making it ideal for use in autonomous driving. In this blog post, we will explore how mmWave radar is used for range and velocity measurement in ACC (Adaptive Cruise Control). The range measurement capability of mmWave radar allows vehicles to accurately determine the distance between themselves and other objects on the road. The velocity measurement capability of mmWave radar allows vehicles to accurately determine the speed of objects on the road. Both capabilities are crucial for safe navigation and collision avoidance. As the technology continues to evolve, we can expect even more advanced applications of mmWave radar in autonomous driving.
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Range and Velocity Estimation
Millimeter-wave radar (MWIR) technology has gained significant attention in recent years due to its ability to detect objects at very long distances with high resolution. In this article, we will explore the concept of range and velocity estimation using MWIR radar and discuss the various techniques and algorithms used in this field. We will also provide a brief overview of the applications of MWIR radar in various industries, such as surveillance and security, autonomous vehicles, remote monitoring and diagnostics, and agricultural monitoring.
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Rainfall Estimation Techniques
Rainfall estimation techniques using millimeter-wave radar (MWIR) offer several advantages over other radar technologies. MWIR radar can detect rainfalldrops with sizes ranging from a few nanometers to tens of micrometers, making it suitable for accurate rainfall estimation. Image processing techniques such as Fourier transform algorithms and wavelet transforms are used to extract relevant information from the radar images. Machine learning algorithms such as support vector machines (SVMs), random forests (RF), or neural networks (NNs) are then used to classify the radar images into different categories based on their physical properties. Validation techniques such as cross-validation methods and metrics are used to evaluate the accuracy of rainfall estimation models. Rainfall estimation using MWIR radar has been successfully applied in various real-world scenarios, including agricultural forecasting, hydrological modeling, and climate research.
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Radar Tomography
Radar tomography is a non-invasive imaging technique that uses radar signals and computational algorithms to create high-resolution 3D images of complex objects. The technique works by emitting radar pulses and analyzing the echoes returned by the target object. By measuring the time delay between the transmitted pulse and the received echo, radar can determine the distance between the object and the receiver, which is then used to create a three-dimensional image of the object's structure. Applications of radar tomography include medical imaging, aerospace engineering, environmental monitoring, security surveillance, and art conservation. However, challenges such as limited range, noise pollution, and complex target behavior must be addressed to improve the accuracy and efficiency of the technique.
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Radar Target Classification
Radar Target Classification，A comprehensive guide to the fundamental principles, different types of radar targets and advancements in this field. The process involves analyzing the returned signal from a radar to determine the type of object being observed. Machine learning algorithms are trained on labeled data to learn the relationships between the radar returns and the corresponding object classes. Common types include synthetic aperture radar (SAR), millimeter wave radar (mmWave), mid-infrared (MIR), ultraviolet (UV) and visible-light (VLW) radar. Recent developments include Convolutional neural networks (CNNs), Generative adversarial networks (GANs), and reinforcement learning.