Related papers: Data Imputation for Sparse Radio Maps in Indoor Po…
Acoustic ranging based indoor positioning solutions have the advantage of higher ranging accuracy and better compatibility with commercial-off-the-self consumer devices. However, similar to other time-domain based approaches using…
RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We…
Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO)…
Joint, radio-based communication, localization and sensing is a rapidly emerging research field with various application potentials. Greatly benefiting from these capabilities, smart city, mobility, and logistic concepts are key components…
The MUSIC algorithm, with its extension for imaging sparse {\em extended} objects, is analyzed by compressed sensing (CS) techniques. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant…
In this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user…
The rapid advancement of the next generation of communications and internet of things (IoT) technologies has made the provision of location-based services for diverse devices an increasingly pressing necessity. Localizing devices…
Radio map estimation from sparse measurements is fundamental to wireless network planning, optimization, and localized map updating. Most recent learning-based approaches formulate the problem as dense map completion over a predefined grid,…
The fields of compressed sensing (CS) and matrix completion have shown that high-dimensional signals with sparse or low-rank structure can be effectively projected into a low-dimensional space (for efficient acquisition or processing) when…
Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a…
Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and…
In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool…
Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of…
In this paper, we propose rWiFiSLAM, an indoor localisation system based on WiFi ranging measurements. Indoor localisation techniques play an important role in mobile robots when they cannot access good quality GPS signals in indoor…
Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency. However, fast and reliable radio map prediction can be very challenging…
Optimal sensor placement is a central challenge in the design, prediction, estimation, and control of high-dimensional systems. High-dimensional states can often leverage a latent low-dimensional representation, and this inherent…
This paper presents a novel radio frequency (RF) beam training algorithm for sparse multiple input multiple output (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithm leverages…
The problem of super-resolution compressive sensing (SR-CS) is crucial for various wireless sensing and communication applications. Existing methods often suffer from limited resolution capabilities and sensitivity to hyper-parameters,…
Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by…
Data loss in wireless sensor networks (WSNs) is quite prevalent. Since sensor nodes are employed for various critical applications, accurate recovery of missing data is important. Researchers have exploited different characteristics of WSN…