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Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent…
Millimeter wave communication systems can leverage information from sensors to reduce the overhead associated with link configuration. LIDAR (light detection and ranging) is one sensor widely used in autonomous driving for high resolution…
Radar odometry estimation has emerged as a critical technique in the field of autonomous navigation, providing robust and reliable motion estimation under various environmental conditions. Despite its potential, the complex nature of radar…
Estimating subsurface dielectric properties is essential for applications ranging from environmental surveys of soils to nondestructive evaluation of concrete in infrastructure. Conventional wave inversion methods typically assume few…
The robust estimation of the mounting angle for millimeter-wave automotive radars installed on moving vehicles is investigated. We propose a novel signal processing pipeline that combines radar and inertial measurement unit (IMU) data to…
While LiDAR and cameras are becoming ubiquitous for unmanned aerial vehicles (UAVs) but can be ineffective in challenging environments, 4D millimeter-wave (MMW) radars that can provide robust 3D ranging and Doppler velocity measurements are…
While machine learning is widely used to optimize wireless networks, training a separate model for each task in communication and localization is becoming increasingly unsustainable due to the significant costs associated with training and…
Geometric environment information aids future distributed radio infrastructures in providing services, such as ultra-reliable communication, positioning, and wireless power transfer (WPT). An a priori known environment model cannot always…
Automotive radars are increasingly susceptible to mutual interference from neighboring radar systems, which can lead to false target detections and the masking of valid targets. While current interference levels remain manageable due to the…
Achieving pixel-level segmentation with low computational cost using multimodal data remains a key challenge in crack segmentation tasks. Existing methods lack the capability for adaptive perception and efficient interactive fusion of…
This paper presents a novel microwave photonic (MWP) radar scheme that is capable of optically generating and processing broadband linear frequency-modulated (LFM) microwave signals without using any radio-frequency (RF) sources. In the…
For high resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for robust future vehicle autonomy and driver assistance in adverse weather conditions,…
The proliferation of edge devices has created an urgent need for security solutions capable of detecting malware in real time while operating under strict computational and memory constraints. Recently, Large Language Models (LLMs) have…
This paper presents an infrastructure-free approach for obstacle detection and environmental mapping using ultra-wideband (UWB) radar mounted on a mobile robotic platform. Traditional sensing modalities such as visual cameras and Light…
Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing…
Machine learning (ML) methods are ubiquitous in wireless communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and cognitive radio. However, the…
This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via…
Millimeter-wave radar provides robust perception in visually degraded environments. However, radar-inertial state estimation is inherently susceptible to drift. Because radar yields only sparse, body-frame velocity measurements, it provides…
We demonstrate a new type of analysis for the DRIFT-IId directional dark matter detector using a machine learning algorithm called a Random Forest Classifier. The analysis labels events as signal or background based on a series of selection…
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam…