相关论文: Active Sensing with Meta-Reinforcement Learning fo…
The utilization of radio frequency (RF) signals for wireless sensing has garnered increasing attention. However, the radio environment is unpredictable and often unfavorable, the sensing accuracy of traditional RF sensing methods is often…
Global Navigation Satellite System (GNSS) receivers provide ubiquitous and precise position, navigation, and time (PNT) to a wide gamut of civilian and tactical infrastructures and devices. Due to the low GNSS received signal power, even…
The rapid development of technology has led to an increase in the number of devices that rely on position, velocity, and time (PVT) information to perform their functions. As such, the Global Navigation Satellite Systems (GNSS) have been…
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat, as they compromise the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to…
Global Navigation Satellite System (GNSS) is essential for autonomous driving systems, unmanned vehicles, and various location-based technologies, as it provides the precise geospatial information necessary for navigation and situational…
A resilient and robust positioning, navigation, and timing (PNT) system is a necessity for the navigation of autonomous vehicles (AVs). Global Navigation Satelite System (GNSS) provides satellite-based PNT services. However, a spoofer can…
This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over…
Global Navigation Satellite Systems (GNSS) are fundamental in ubiquitously providing position and time to a wide gamut of systems. Jamming remains a realistic threat in many deployment settings, civilian and tactical. Specifically, in…
Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are…
We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked with acquiring information about targets of interests using…
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigation strategies…
Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a…
The accuracy and reliability of vehicle localization on roads are crucial for applications such as self-driving cars, toll systems, and digital tachographs. To achieve accurate positioning, vehicles typically use global navigation satellite…
The vulnerabilities associated with modern systems relying on Global Navigation Satellite Systems (GNSS) due to intentional and unintentional interference is an increasing threat. Since radio frequency interference (RFI) significantly…
This paper addresses an uplink localization problem in which a base station (BS) aims to locate a remote user with the help of reconfigurable intelligent surfaces (RISs). We propose a strategy in which the user transmits pilots sequentially…
In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a…
The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals.…
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions…
Radio frequency (RF)-based indoor localization offers significant promise for applications such as indoor navigation, augmented reality, and pervasive computing. While deep learning has greatly enhanced localization accuracy and robustness,…
This paper presents research findings on handling faulty measurements (i.e., outliers) of global navigation satellite systems (GNSS) for vehicle localization under adverse signal conditions in field applications, where raw GNSS data are…