Ultra-Wideband (UWB) radar positioning plays an important role in non-cooperative personnel positioning or device-free positioning. Recent work has reformulated the Time-of-Flight (ToF) estimation task as a two-dimensional image processing problem using Residual Convolutional Neural Networks (RCNNs), avoiding intricate procedures of traditional methods. Despite its benefits, the RCNN struggles with fully exploiting feature reutilization, resulting in significant errors in ToF estimation. Although Particle Filters (PFs) can alleviate this problem, the constant parameters in weight estimation will affect the positioning accuracy. Therefore, we first adopt a Dense Convolutional Network (DenseNet) to replace the RCNN to enhance the feature reutilization and improve the accuracy of ToF estimation. Additionally, we design a method for outlier and anomaly cluster elimination in the ToF time series based on DBSCAN clustering, effectively suppressing observation noises. Finally, to address the insufficient adaptability of parameters in PF weight estimation, we improve the loss function in the DenseNet, thereby enabling it to dynamically output the variance of ToF. We verify the effectiveness and generalizability of our proposed method through an open-source dataset collected with low-cost UWB devices. Compared with a classic method, the average Root Mean Square Error (RMSE) of the proposed method within the positioning area decreases by 37.1%. Furthermore, through repeated experiments across three distinct scenarios, our method demonstrates RMSE reductions of 20.1%, 36.5%, and 28.5%, respectively, compared to an existing RCNN-based approach.

This paper explores the use of a commercial-off-the-shelf impulse-radio ultra-wideband transceiver with 0.5 GHz bandwidth for ranging and respiration rate monitoring of a moving target. Respiration causes the chest to change in shape, which alters the target’s radar cross section, which causes periodical changes in the channel impulse response. A median filter, Viterbi algorithm and a particle filter are used to determine the range of the target, band-pass filtering and fourier transforming are used for respiration rate estimation. The results demonstrate that accurate localization and respiration rate monitoring is possible under controlled settings, but not in a realistic scenario.

Indoor localization based on Bluetooth Low Energy (BLE) is traditionally implemented by matching Received Signal Strength (RSS) fingerprints of nearby BLE nodes. Depending on the node density, pure RSS-based BLE localization only provides room-level or zone-level accuracies. For pedestrian tracking, BLE-based RSS fingerprinting is often fused with Pedestrian Dead Reckoning (PDR) using Inertial Measurement Units (IMU), which can provide up to $1-2 \mathrm{~m}$ accuracy. Recently, a phase-based BLE ranging method has been developed, which can accurately measure the distance between two BLE devices. Initial experiments on a moving platform showed promising results for accurate localization. In this work, the feasibility of this technology for pedestrian tracking is evaluated. On-body phase-based BLE and IMU measurements are performed in an industrial lab environment with four BLE anchors. A hybrid Particle Filter (PF) algorithm is designed, which fuses phase-based BLE ranging, PDR, and a range correction algorithm. Our proposed PF algorithm achieves a median and p75 error of $0.70 \mathbf{m}$ and $1.03 \mathbf{~ m}$ respectively, which outperforms traditional RSS-based (hybrid) BLE localization algoritms.

The Non-Line-of-Sight (NLOS) phenomenon can hinder signal propagation and significantly reduce the accuracy of UWB for indoor positioning and navigation. The Channel Impulse Response (CIR) sequence generated during UWB ranging is widely used for channel identification. However, existing deep learning algorithms struggle to balance the local and global features of the CIR sequence effectively. To address this, this paper constructs a Bidirectional Encoder Representations from Transformers (BERT) channel identification model using the self-attention mechanism to improve the NLOS identification. The identification Accuracy, LOS recall, and F2 scores in multiple scenarios are 96.65%, 97.13%, and 0.9703 respectively. Comparing to state-of-art algorithms such as LS-SVM, CNN, and LSTM, our algorithm outperformed by 17.9%, 11.86%, and 10.80% respectively. For NLOS ranging errors, a fine-grained classification model is constructed with error correction model based on BERT. In multiple scenarios, the average NLOS ranging error is reduced by 41.97% and outperforms LS-SVM, CNN, and LSTM by 34.04%, 31.99%, and 16.81% respectively. In the overall positioning experiment, our algorithm achieves better performance than the existing algorithms by 32.13%.

Most spectrum surveys conducted worldwide demonstrate that the radio-electric spectrum in use at any given location and instant of time is below 25%. Current spectrum management policies and spectrum utilization inefficiency is becoming unsustainable for future development of radio technologies and services. In this context, dynamic spectrum access is a promising technique for improving spectrum utilization efficiency. A key scientific gap is identifying inaccurate spectrum data from hidden nodes that is not homogeneously distributed in the spatial domain and dynamically vary in time and frequency. For bridging this gap, our paper presents the research results of a spectrum feature extraction algorithm based on multi-correlation and Random Forest. Our algorithm is capable of estimating the spectrum utilization pattern in the spatial and frequency domain with a minimum reliability of 92% for a real heterogeneous networking scenario.

in Ultra Wideband (UWB), large ranging errors occur under Non-Line-of-Sight (NLoS) conditions, which significantly degrades positioning accuracy. Human body shadowing (HBS) is a specific case of NLoS, which is a prominent error source for on-body UWB positioning. This work presents a tracking algorithm based on a Particle Filter (PF), designed to mitigate HBS-induced positioning errors by using an orientation-adaptive measurement model, consisting of a bank of Gaussian Mixture Models. The relative orientation is derived from Inertial Measurement Unit (IMU) data, and predicted positions from the tracking algorithm itself. We propose a second tracking algorithm in order to train the adaptive measurement model in a semi-unsupervised way, eliminating the need for accurate ground truth. The proposed algorithm outperforms a state of the art algorithm by an average of 11% (unsupervised) to 39% (supervised) in an experimental evaluation.

A Visible Light Positioning (VLP) network planner holds tremendous economic potential in that it permits designing a roll-out within given cost, illuminance and accuracy bounds. In this manuscript, the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm is applied to simultaneously optimise a roll-out's maintained illuminance and positioning error, by varying the placement of the VLP-enabled LED transmitters. With simulations that differ in positioning and/or environment parameters, the important illuminance-positioning trade-off is revealed. The corresponding Pareto fronts and LED arrangements are studied. Guidelines regarding where to place the LEDs and which LEDs to select for positioning are provided.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve decimeter-level accuracy. However, the performance can degrade significantly under Non-Line-of-Sight (NLoS) conditions. Detection and mitigation of NLoS conditions is a complex problem, and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is an important cause of NLoS. In this paper, we propose an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors by attaching an inertial measurement unit to the UWB tag. Two algorithms are designed and implemented, of which the second algorithm is designed for robustness against errors in the IMU's estimated heading. The proposed algorithms are validated by UWB Two Way Ranging (TWR) measurements, performed in two environments. Two more algorithms are implemented as a benchmark, of which one is based on the estimated first path power, and the other is based on range residuals. The proposed algorithm outperforms the other algorithms in the higher error statistics, achieving a 49% reduction of the p90 error depending on the environment.

Smartphone-based WiFi ranging positioning based on fine time measurement (FTM) always collapses in real-life scenarios. In this work, a novel map-aided particle filter (PF)-based WiFi FTM fingerprinting approach is proposed to address the poor performance of the WiFi FTM ranging positioning. Different from manually collecting fingerprints, this approach utilizes the theoretical received signal strength and geometric distances between the access points and reference points as the fingerprints, which means less labour-intensive work. For accurate WiFi position estimation, a map-aided PF is designed to find the optimal position. Extensive experiments are carried out in the non-line-of-sight (NLoS) and mixed line-of-sight/non-line-of-sight (LoS/NLoS) environments, and the testing results show that the accuracy and stability of FTM fingerprinting are improved by using the mixed RSS and ranging data fingerprints. The minimal mean location errors (MEs) of the PF-based WiFi FTM fingerprinting in NLoS and mixed LoSINLoS conditions are 1.70 m and 1.85 m, respectively. Compared to the classic weighted least square method, the MEs are reduced by 54.91 % and 45.43 %, respectively. The testing results demonstrate that the PF-based FTM fingerprinting is an effective approach that provides satisfactory localization results in real-life indoor environments.

Within the context of the Internet of Things (IoT), many applications require high-quality positioning services. As opposed to traditional technologies, the two most recent positioning solutions: 1) ultra-wideband (UWB) and 2) (unmodulated) visible light positioning [(u)VLP] are well suited to economically supply centimeter-to-decimeter level accuracy. This manuscript benchmarks the 2-D positioning performance of an 8-anchor asymmetric double-sided two-way ranging (aSDS-TWR) UWB system and a 15-LED frequency-division multiple access (FDMA) received signal strength (RSS) (u)VLP system in terms of feasibility and accuracy. With extensive experimental data, collected at two heights in a 8 m by 6 m open zone equipped with a precise ground-truth system, it is demonstrated that both visible light positioning (VLP) and UWB already attain median and 90thpercentile positioning errors in the order of 5 and 10 cm in line-of-sight (LOS) conditions. An approximately 20-cm median accuracy can be obtained with uVLP, whose main benefit is it being infrastructureless and thus very inexpensive. The accuracy degradation effects of non-LOS (NLOS) on UWB/(u)VLP are highlighted with four scenarios, each consisting of a different configuration of metallic closets. For the considered setup, in 2-D and with minimal tilt of the object to be tracked, VLP outscores UWB in NLOS conditions, while for LOS scenarios similar results are obtained.

This paper presents a fast, memory-efficient, and worldwide map matching algorithm based on raw geographic coordinates and enriched open map data with support for trajectories on foot, by bike, and motorized vehicles. The proposed algorithm combines the Markovian behavior and the shortest path aspect while taking into account the type and direction of all road segments, information about one-way traffic, maximum allowed speed per road segment, and driving behavior. Furthermore, a self-adapting lane detection algorithm based solely on accelerometer readings is added on top of the map matching algorithm. An experimental validation consisting of 30 trajectories on foot, by bike, and by car, showed the efficiency and accuracy of the proposed algorithms, with an average F1-score and median error of 99.5% and 1.89 m for the map matching algorithm and an average F1-score of 86.7% for the lane detection algorithm, which resulted in the correctly estimated lane 93.0% of the time. Moreover, the proposed technique outperforms existing state of the art techniques with accuracy improvements up to 45.2%.

The next-generation automated inventory management solution revolves around unmanned aerial drones that operate without any human intervention, including during (wireless) charging. As the inductive power transfer system’s efficiency depends on its coil alignment, a precision landing system is required that is lightweight, accurate, and of low-cost. This manuscript demonstrates the potential of received signal strength (RSS) Visible Light Positioning (VLP) with a single photodiode (PD), which is inherently lightweight. A landing system is proposed, in which a PD-equipped drone self-localises with respect to light-emitting diodes (LEDs) that are integrated into the landing zone. The LEDs are furthermore sequentially strung together to cut costs, thereby forming a LED strip. Automated measurements characterise the particular (propagation) challenges and the positioning performance of 3D VLP on a small scale, when mimicking drone landing on a flat surface or in a commercial funnel. For the flat surface, a 50 Hz update rate and a vertical range up to 1 meter, the target of a third quartile VLP-only positioning error bounded by 3.5 cm is attained with multilateration and a 6-element LED strip. The presence of the reflections-inducing drone funnel degrades the positioning performance significantly. However, a sufficient accuracy can still be reached. Both configurations exhibit larger errors close to the landing zone.

We present a new floor number detection algorithm for use in smartphone-based indoor localisation systems. It is designed to complement any pedestrian dead reckoning (PDR) algorithm able to detect steps and estimate a 2D trajectory from data of the smartphone’s inertial measurement unit.Our proposed method is based on the Viterbi algorithm, fusing data from an off-the-shelf smartphone’s accelerometer, barometer and wifi received signal strength (RSS) measurements. The accelerometer is used to detect accelerating elevators, while the barometer is used to detect stair climbing. This is combined with model-based wifi RSS fingerprinting, enabling accurate floor number detection. Our system is tested in an office environment with 7 41 m x 27 m floors, each of which has 2 pre-existing wifi access points. The algorithm is evaluated with a total of 116 minutes of recorded data, in which the floor number changed 76 times and a distance of 4.8 km was travelled. Since the Viterbi algorithm allows to easily correct past states (i.e. floor numbers) based on new information, it is evaluated in real-time and batch mode. Our proposed algorithm achieves a floor number detection accuracy of 99.1% (real-time) and 99.7% (batch), while using only RSS measurements resulted in 91% accuracy.

This work assesses the applicability of the well-known SAGE algorithm for time-of-arrival estimation on ultra-wideband (UWB) measurements taken with cheap COTS hardware. Performance is comparable with a simple leading-edge detection (LDE) algorithm, establishing a general precision of approximately 30 cm/60 cm. SAGE performance is slightly worse in general (33 cm/71 cm), but is more stable in non-line-of-sight (NLOS) caused by human body presence. A more detailed breakdown of the effect of incidence angle on one-dimensional ranging accuracy is studied in relationship to human body shadowing effects. Within a cone of 135 degrees in front of the UWB device (pointing away from the body), the azimuthal incidence angle has no influence on the ranging performance of either algorithm.

In this paper, we investigate the coexistence of cognitive radio networks on TV white spaces for rural and suburban connectivity. Although experimental models and laboratory measurements defined the maximum interference threshold for TV white space technologies for general use cases, our research found that in real wireless rural and suburban scenarios, severe interference to the broadcasting services might occur. This is particularly relevant when the traffic load of the telecom base stations (BSs) exceeds 80% of their maximum capacity. We propose a dynamic management algorithm for minimizing the interference, based on a centralized access control architecture for cognitive radio wireless networks. In an experimental emulation for assessing the impact of cognitive radio interference on the broadcasting service’s QoE, our method reduced the perceived video distortion by the broadcasting users by at least 50% and 27.5% in a rural and suburban scenario, respectively, while the spectrum usage is increased by just 8%.

Fingerprinting indoor localization provides high positioning accuracy with low cost and easy deployment. Considering the unsatisfying precision of received signal strength (RSS)-based fingerprinting, hybrid metrics including angle-of-arrival (AoA) and time-of-flight (ToF), are incorporated to the RSS fingerprinting system. To evaluate the positioning performance of hybrid metrics, the closed-form Cramér-Rao lower bound (CRLB) is derived in this paper. The existence conditions of CRLBs, as well as the relationship of the CRLBs between single and hybrid metrics is revealed. Numerical results based on an office building scenario show that hybrid metrics greatly improve the positioning performance and the robustness to measured standard deviations compared to the single metric's case. Furthermore, hybrid schemes of the AoA/RSS/ToF metrics are also investigated, and simulations reveal that the scheme of AoA/ToF-supporting access points (AP) enhanced with single RSS-supporting APs achieves the best positioning accuracy among all hybrid schemes.

This paper assesses the accuracy of a three-dimensional Visible Light Positioning (VLP) algorithm for two different Light Emitting Diode (LED) configurations using the same four LEDs, but mounted at different locations on the ceiling. The two configurations are both simulated and measured at 22801 test points. It is observed that a classic square LED configuration results in position ambiguities, causing errors up to several meters. Alternatively, a star-shaped LED configuration is able to uniquely reconstruct the photodiode's location. For LEDs at a height of approximately 3 m above the receiver, median errors of 12.7 cm and maximal errors of 21.1 cm are experimentally obtained, showcasing the applicability of 3D VLP for drone navigation.

This paper is the design of a Radio Environment Map (REM) with a real-time tool to sense the radiofrequency spectrum and optimally places with Surrogate Modelling and Sequential Experimental Design tools a total of 72 SDR sensors in the selected area, using LoRa and/or NB-IoT technologies for networking. It permits the regulatory body to check the correct use of the assigned spectrum and constitutes a communication alternative in case of a catastrophic event, such as a hurricane or an earthquake, where radio and TV broadcasting play an important role in keeping people informed after such meteorological event. The radiobroadcast services use large antennas and high towers, making them vulnerable to such events. Regardless of the chosen technology, the IoT monitoring network will be more robust, since it uses small antennas and lower towers, and often a given area is covered by multiple base stations. The tool can be used to deploy new services in the nonserved area (e.g., 4G in the 700 MHz band at a lower cost or using TVWS techniques to provide communications and internet connection) and optimal interference management.

The precise (location) tracking of automated guided vehicles will be key in enlarging the productivity, efficiency and safety in the connected warehouse or production infrastructure. Combining the modest price tag, the adequate coverage and the potential centimetre accuracy makes Visible Light Positioning (VLP) systems appealing as replacements for the current, high-cost, tracking systems. Model-fingerprinting-based received signal strength (RSS) VLP enables the required accuracy. It requires an elaborate optical channel model fingerprinted in a fine-grained, and predefined positioning grid. Depending on the grid's granularity, constructing the fingerprint database demands a significant computation and storage effort. This paper employs response adaptive or sequential experimental design to form sparse channel models, vastly reducing the storage and computation. It is shown that model-fingerprinting-based RSS only requires modelling less than 1 percent of the grid points, in an elementary positioning cell. The sparse model can be re-evaluated as a way to cope with environment changeover. Model recomputation as a way of compensating for LED ageing is also studied.

This paper experimentally compares the positioning accuracy of TDoA-based and RSS-based localization in a public outdoor LoRa network in the Netherlands. The performance of different Received Signal Strength (RSS)-based approaches (proximity, centroid, map matching,...) is compared with Time-Difference-of-Arrival (TDoA) performance. The number of RSS and TDoA location updates and the positioning accuracy per spreading factor (SF) is assessed, allowing to select the optimal SF choice for the network. A road mapping filter is applied to the raw location estimates for the best algorithms and SFs. RSS-based approaches have median and maximal errors that are limited to 1000 m and 2000 m respectively, using a road mapping filter. Using the same filter, TDoA-based approaches deliver median and maximal errors in the order of 150 m and 350 m respectively. However, the number of location updates per time unit using SF7 is around 10 times higher for RSS algorithms than for the TDoA algorithm.

We develop and benchmark four RSS localisation algorithms where different a priori knowledge is required. The selection of the best algorithm depends on the availability of additional information on path loss exponent and/or transmit power. We compare our algorithms with centroid localization and show that the algorithms provide better results for shadowing on the values not exceeding 6dB. We perform experiments and simulations with Bluetooth Low Energy and LoRaWAN technologies and select the best technology and algorithm for localisation in large open industrial environments.

Current (theoretical) Visible Light Positioning (VLP) systems assume the transmission of ideal waveforms. Existing VLP LED transmitters, however, transmit far from perfect signals, causing a plunge in the positioning accuracy. The impact of transmitter nonidealities, such as overshoot and rise time, remains unclear. The lack of quality (industry-ready) LED drivers presents a major hurdle in large-scale VLP adoption. The extent to which transmitter nonidealities are tolerable, is valuable information for LED driver designers. This paper provides specifications for the latter. It charts the potentially detrimental impact of transmitter nonidealities on the positioning performance. The influence of both overshoot and rise/fall time is studied.

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra‐High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT‐based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB‐IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time.

The performance of LoRa geolocation for outdoor tracking purposes has been investigated on a public LoRaWAN network. Time Difference of Arrival (TDoA) localization accuracy, update probability, and update frequency were evaluated for different trajectories (walking, cycling, and driving) and LoRa spreading factors. A median accuracy of 200 m was obtained for the raw TDoA output data. In 90% of the cases, the error was less than 480 m. Taking into account the road map and movement speed significantly improves accuracy to a median of 75 m and a 90th percentile error of less than 180 m.

A mobile application is presented for real-time testing and optimization of path-loss models and network planning, based on the execution of validation measurements in the considered environment. The application is tested in three indoor scenarios and for three path-loss models. Without optimization, average absolute prediction errors of about 5 dB are obtained, with a simple free-space model performing best. Executing a limited set of ten additional measurements suffices to improve predictions by up to more than 40%. The application is particularly useful for very quick path-loss model tests in a certain environment or for easily obtaining more accurate network deployments, as a single measurement only takes a few seconds and optimization of the path-loss model is fully automated.

Wireless systems often need to optimize multiple conflicting objectives (low delay, high reliability, and low cost), which are difficult to fulfill simultaneously. In such cases, the wireless system exhibits multiple optimal operation points, referred to as the optimal Pareto front (OPF). However, due to the large number of parameter settings to be evaluated and the time-consuming nature of performing wireless experiments, it is typically not possible to identify the OPF by exhaustively evaluating all possible settings. Instead, for many use cases, an approximation is good enough. To this end, this paper applies a multi-objective surrogate-based optimization (MOSBO) toolbox to efficiently optimize wireless systems and approximate the OPF using a limited number of iterations. Moreover, a real Wi-Fi conferencing scenario is optimized that has two conflicting objectives (exposure and audio quality) and four configurable parameters (Tx-Power, Tx-Rate, Codec Bit-Rate, and Codec Frame-Length). The benefits of using the MOSBO approach for such a network problem is demonstrated by approximating the OPF using 94 iterations instead of requiring the exploration of 6528 different parameter combinations, while still dominating 96.58% of the complete design space.