Ultra-wideband Systems Research Articles

A Low-Cost Foot-Placed UWB and IMU Fusion-Based Indoor Pedestrian Tracking System for IoT Applications

Among existing wireless and wearable indoor pedestrian tracking solutions, the ultra-wideband (UWB) and inertial measurement unit (IMU) sensors are the popular options due to their accurate and globally referenced positioning, and low-cost and compact size, respectively. However, the UWB position accuracy is compromised by the indoor non-line of sight (NLOS) and the IMU estimation suffers from orientation drift as well as requiring position initialization. To overcome these limitations, this paper proposes a low-cost foot-placed UWB and IMU fusion-based indoor pedestrian tracking system. Our data fusion model is an improved loosely coupled Kalman filter with the inclusion of valid UWB observation detection. In this manner, the proposed system not only adjusts the consumer-grade IMU's accumulated drift but also filters out any NLOS instances in the UWB observation. We validated the performance of the proposed system with two experimental scenarios in a complex indoor environment. The root mean square (RMS) positioning accuracy of our data fusion model is enhanced by 60%, 53%, and 27% compared to that of the IMU-based pedestrian dead reckoning, raw UWB position, and conventional fusion model, respectively, in the single-lap NLOS scenario, and by 70%, 34%, and 12%, respectively, in the multi-lap LOS+NLOS scenario.

MIMO Antennas: Design Approaches, Techniques and Applications.

The excessive use of digital platforms with rapidly increasing users in the wireless domain enforces communication systems to provide information with high data rates, high reliability and strong transmission connection quality. Wireless systems with single antenna elements are not able to accomplish the desired needs. Therefore, multiple-input multiple-output (MIMO) antennas are getting more attention in modern high-speed communication systems and play an essential part in the current generation of wireless technology. However, along with their ability to significantly increase channel capacity, it is a challenge to achieve an optimal isolation in a compact size for fifth-generation (5G) terminals. Portable devices, automobiles, handheld gadgets, smart phones, wireless sensors, radio frequency identification and other applications use MIMO antenna systems. In this review paper, the fundamentals of MIMO antennas, the performance parameters of MIMO antennas, and different design approaches and methodologies are discussed to realize the three most commonly used MIMO antennas, i.e., ultra-wideband (UWB), dual-band and circularly polarized antennas. The recent MIMO antenna design approaches with UWB, dual band and circularly polarized characteristics are compared in terms of their isolation techniques, gain, efficiency, envelope correlation coefficient (ECC) and channel capacity loss (CCL). This paper is very helpful to design suitable MIMO antennas applicable in UWB systems, satellite communication systems, GSM, Bluetooth, WiMAX, WLAN and many more. The issues with MIMO antenna systems in the indoor environment along with possible solutions to improve their performance are discussed. The paper also focuses on the applications of MIMO characteristics for future sixth-generation (6G) technology.

Experimental Benchmarking of Next-Gen Indoor Positioning Technologies (Unmodulated) Visible Light Positioning and Ultra-Wideband

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 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile 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.

Phase Center Offset Calibration and Multipoint Time Latency Determination for UWB Location

The ultrawideband (UWB) system generates the precise Time of Arrival (TOA) to realize precise indoor positioning for various Internet of Things (IoT) applications. However, less attention has been paid to mitigating systematic errors in the UWB positioning algorithm, which could be a significant factor that reduces the positioning accuracy. The systematic errors are introduced mainly from two aspects. First, the offsets between the antenna reference point (ARP) and the real incidence point are not carefully eliminated from the ranging observations. Second, the between-antenna time latencies used for cable-linked synchronization usually contain systematic errors at the calibration point. In this article, we present two techniques, including the antenna phase center offset (PCO) calibration and the multipoint time latency determination (MTLD), to reduce the impact of systematic errors on the UWB positioning accuracy. The experiments are carried out in an indoor parking lot. The results show that the established PCO calibration model can correct the PCO errors by 70%. The accuracy of time latency determination is improved by 10% with MTLD and further by 44% with both MTLD and PCO calibration. The static and kinematic positioning tests indicate that the horizontal accuracy is improved from 31 to 10 cm and 20 to 10 cm, respectively.

A two-port UWB MIMO antenna with an EBG structure for WLAN/ISM applications

A two-port disc-shaped multiple-input-multiple-output (MIMO) antenna is investigated for ultra-wideband (UWB) systems. The inter-element isolation is improved by inserting an electromagnetic band-gap (EBG) structure between the monopole radiators. The MIMO antenna with EBG offers a wide impedance bandwidth (S11 ≤ −10 dB) of 120 % (3–12 GHz), isolation of 25 dB, and peak gain >5 dBi over the UWB range. The low-profile MIMO antenna is printed on the FR-4 substrate and has a size of 30 × 60 × 1.6 mm3.

Artificial Neural Network Approach to Guarantee the Positioning Accuracy of Moving Robots by Using the Integration of IMU/UWB with Motion Capture System Data Fusion.

This study presents an effective artificial neural network (ANN) approach to combine measurements from inertial measurement units (IMUs) and time-of-flight (TOF) measurements from an ultra-wideband (UWB) system with OptiTrack Motion Capture System (OptiT-MCS) data to guarantee the positioning accuracy of motion tracking in indoor environments. The proposed fusion approach unifies the following advantages of both technologies: high data rates from the MCS, and global translational precision from the inertial measurement unit (IMU)/UWB localization system. Consequently, it leads to accurate position estimates when compared with data from the IMU/UWB system relative to the OptiT-MCS reference system. The calibrations of the positioning IMU/UWB and MCS systems are utilized in real-time movement with a diverse set of motion recordings using a mobile robot. The proposed neural network (NN) approach experimentally revealed accurate position estimates, giving an enhancement average mean absolute percentage error (MAPE) of 17.56% and 7.48% in the X and Y coordinates, respectively, and the coefficient of correlation R greater than 99%. Moreover, the experimental results prove that the proposed NN fusion is capable of maintaining high accuracy in position estimates while preventing drift errors from increasing in an unbounded manner, implying that the proposed approach is more effective than the compared approaches.

The REACH radiometer for detecting the 21-cm hydrogen signal from redshift z ≈ 7.5–28

Observations of the 21-cm line from primordial hydrogen promise to be one of the best tools to study the early epochs of the Universe: the Dark Ages, the Cosmic Dawn, and the subsequent Epoch of Reionization. In 2018, the EDGES experiment caught the attention of the cosmology community with a potential detection of an absorption feature in the sky-averaged radio spectrum centred at 78 MHz. The feature is deeper than expected, and, if confirmed, would call for new physics. However, different groups have re-analyzed the EDGES data and questioned the reliability of the signal. The Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) is a sky-averaged 21-cm experiment aiming at improving the current observations by tackling the issues faced by current instruments related to residual systematic signals in the data. The novel experimental approach focuses on detecting and jointly explaining these systematics together with the foregrounds and the cosmological signal using Bayesian statistics. To achieve this, REACH features simultaneous observations with two different antennas, an ultra wideband system (redshift range 7.5 to 28), and a receiver calibrator based on in-field measurements. Simulated observations forecast percent-level constraints on astrophysical parameters, potentially opening up a new window to the infant Universe.

Generalized Receiver Employment in Ultra-Wideband Systems: Performance Analysis

In the present paper, a performance analysis of an ultra-wideband (UWB) system based on the ge-neralized approach to signal processing in noise is discussed. The UWB system utilizes a new pulse design that made the performance analysis possible, since the new not only has a short duration to reduce collision, but is also spectrally compliant to the standards on UWB systems. We present the performance analysis of the UWB system constructed based on the generalized receiver processing different pulse shapes and with various num-bers of users. New simulation results on multiuser performance of the impulse radio are also presented.

A Survey of Robot Swarms' Relative Localization Method.

For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite System) signals are weak due to the crowd being in a swarm or blocked by a forest, mountains, and high buildings in the environment. Thus, relative localization is an indispensable way to provide position information for the swarm. In this paper, we review the status and development of relative localization. It is first assessed that relative localization to obtain spatio-temporal relationships between individuals is necessary to achieve the stable operation of the group. After analyzing typical relative localization systems and algorithms from the perspective of functionality and practicality, this paper concludes that the UWB-based (ultra wideband) system is suitable for the relative localization of robots in large-scale applications. Finally, after analyzing the current challenges in the field of fully distributed localization for robotic swarms, a complete mechanism encompassing the relative localization process and the relationship between local and global localization that can be a possible direction for future research is proposed.

Ultra-Wideband Localization and Deep-Learning-Based Plant Monitoring Using Micro Air Vehicles

Micro air vehicles (MAVs) are ideal for indoor precision farming due to their agility and minimal impact on surrounding objects. To achieve autonomous operations, these vehicles hardly rely on the commonly used Global Positioning System for indoor navigation and monitor plants with severely limited payloads. This paper proposes an autonomous MAV system that incorporates an ultra-wideband (UWB) system and a deep learning method to perform indoor navigation and localize unhealthy plants. The position of the MAV is estimated using an extended Kalman filter based on the trilateration results of the ranging measurements obtained from the UWB system. A multiple bounding box prediction strategy is used to analyze all the leaves of the plants and quickly identify their health conditions. Several deep learning models were selected and trained to detect unhealthy plants using field condition images. The model that achieved the desired combination of the accuracy and efficiency was fine-tuned with various image resolutions and sizes of negative samples to further improve its detection accuracy. Several real-world flight tests were performed successfully using the UWB system, and the plants were classified correctly with the selected model.

Measurement of Orientation and Distance Change Using Circularly Polarized UWB Signals

The article proposes methodology to use circularly polarized (CP) ultra-wideband (UWB) signals for simultaneous measurement of orientation and distance changes between transmitter and receiver. The proposed technique uses the rotational Doppler effect on CP pulsed communication. The amplitude of a CP signal is immune to polarization misalignment in the presence of rotation; however, the phase is subjected to a frequency-invariant shift proportional to the rotation angle. This significantly distorts the pulse shape in the time domain and can be used for the measurement of the rotated angle. By combining the technique with the well-known localization capability of UWB systems, one can precisely measure not only the distance but also the orientation. This is demonstrated by both numerical and experimental studies presented in this article.

ANALYSIS OF THE PERFORMANCE OF AN UWB-BASED COOPERATIVE POSITIONING FOR DIFFERENT CAR PLATOON CONFIGURATIONS

Abstract. The increasing interest in autonomous vehicles motivates the researches aiming at developing reliable positioning system also in conditions challenging for the Global Navigation Satellite Systems (GNSS), such as in urban canyons, tunnels, under quite dense vegetation. The uso of Ultra Wide-Band (UWB) systems is among the quite well known methods for providing reasonable positioning results without exploiting GNSS. UWB systems are typically used indoors, however their use can be of interest also outdoors, in particular when the need is to ensure good positioning results over a quite small area. This paper investigates the use of UWB systems for positioning in the case of terrestrial vehicles, and, more specifically, it focuses on checking the influence of car platoon configurations on the performance of an UWB cooperative positioning system. In the considered tests, where a high percentage of UWB communications was successful, the obtained results show that the car configuration can have a quite remarkable impact on the positioning performance, doubling the obtained median error.

IMPLEMENTATION AND IMPROVEMENT OF INDOOR WEARABLE UWB/INS INTEGRATION POSITIONING METHOD

Abstract. Aiming at the problem that the Ultra-Wide-Band (UWB) positioning accuracy is reduced in the Non-Line-of-Sight (NLOS) environment, a UWB positioning accuracy evaluation mechanism is introduced. This paper analyzes the geometric distribution of UWB equipment theoretically to evaluate the positioning accuracy of the UWB system. Furthermore, it optimizes the geometric distribution of UWB and optimizes the UWB positioning algorithm model to improve the positioning accuracy. A set of UWB positioning accuracy estimation process is proposed. Through theoretical analysis and simulation, a model of the influence of the geometric distribution of base stations on the UWB positioning precision is established. The obtained model provides a reference for setting and adjusting the measurement noise in the Kalman filter for UWB/Inertial Navigation System (INS) integration positioning, which improves the combined positioning accuracy.

RFI Mitigation for One-Bit UWB Radar Systems

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultrawideband (UWB) radar systems because RFI can severely degrade the radar imaging capability and target detection performance. In this article, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the continuous time binary value (CTBV) technology. This sampling strategy compares the signal to a known threshold that varies with slow-time and can be used to achieve a high sampling rate and quantization resolution with simple and affordable hardware. This article establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We use an extended majorization-minimization-based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method for the extended 1bMMRELAX algorithm to improve its computational efficiency. Moreover, a sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.

Thin Circularly Polarized Slot Array Antenna for High-Band UWB Applications

This paper presents a thin 2 × 2 circularly polarized slot array antenna that operates in the high-band ultra-wideband (UWB) system. The proposed antenna is composed of three parts: a top metal part that consists of an L-shaped slot structure, a dielectric substrate, and a bottom floating metal layer. The design model and characteristics of the antenna element and array antenna are investigated through simulation and measurement. Experimental results show that the maximum gain of the prototype array antenna is 6.94 dBi at 9.02 GHz. The antenna size is 29.2 mm × 31.7 mm, and its thickness is 1.636 mm. By using a slot antenna structure, a thin circularly polarized antenna (thickness: 1.636 mm) with high gain was demonstrated. The antenna characteristics qualify the antenna to be used in high-band UWB applications.

Transfer learning-based UWB indoor localization using MHT-MDC and clusterization-based sparse fingerprinting

Indoor localization systems have become more and more popular. Several technologies are intensively studied with application to high precision object localization in such environments. Ultra-wideband (UWB) is one of the most promising, as it combines relatively low cost and high localization accuracy, especially compared to Beacon or WiFi. Nevertheless, we noticed that the leading UWB systems’ accuracy is far below the values declared in the documentation. To achieve high localization accuracy, low fingerprinting complexity, and tolerance to significant random errors, we propose a Multiple Hypothesis Tracker with Direction Motion Constraint (MHT-MDC) algorithm followed by a transfer learning approach. We perform fingerprinting using a dense grid in a controlled environment to train the neural network. Thanks to transfer learning, full fingerprinting is not necessary to obtain high localization accuracy when the UWB system is deployed in a new localization. We demonstrate that thanks to transfer learning, high localization accuracy can be maintained when only 7% of fingerprinting samples from a new localization are used to update the neural network, which is very important in practical applications. It is also worth noticing that our approach can be easily extended to other localization technologies. This is an extended version of the conference paper published in [1].

Optimized Gaussian Pulse Design for UWB System Using Particle Swarm Optimization Based-on Generalized Bessel Polynomials

The ultrawideband system operates a very short pulse with enormous bandwidth to provide high data rates for data transmission. To design the UWB pulse, considering the pulse shape is very necessary, and a spectral emission mask of the designed pulse should meet the FCC spectral mask requirement between frequency range 3.1 GHz to 10.6 GHz. The traditional UWB pulse design is based on the Gaussian derivative. However, the frequency spectrum is not satisfied the FCC spectral mask requirement. In this study, the Gaussian pulse can be designed from the mathematical characteristic of the generalized Bessel polynomial. The spectral efficiency of the proposed pulse can be improved by the combination of the derivative of Gaussian pulse with a weight coefficient optimization with particle swarm optimization (PSO). PSO is a population-based optimization algorithm inspired by animal behavior. PSO is applied with generalized Bessel polynomial transfer function to gain the best weight coefficient, we proposed to optimize its weight vector to design a pulse that exceeds to FCC spectral mask. The results were found in MATLAB software show that generalized Bessel polynomials can approximate the proposed pulse with combination method and PSO. The spectral efficiency is improved to 89.30% and the spectrum is greater close to the FCC spectral mask requirement. To confirm an improved spectral efficiency compared to the previous works.

Estimating TOA Reliability With Variational Autoencoders

Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in the time of flight (TOF) estimation and hence to a degraded localization accuracy. A major topic in high-precision RF-based localization is the identification of such anomalous signals that negatively affect the localization performance, and to mitigate the errors introduced by them. As such signal and error characteristics depend on the environment, data-driven approaches have shown to be promising. However, there is a trade-off to a bad generalization and a need for an extensive and time-consuming recording of training data associated with it. We propose to use generative deep learning models for out-of-distribution detection based on channel impulse responses (CIRs). We use a Variational Autoencoder (VAE) to predict an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our experiments show that a VAE trained only on line-of-sight (LOS) training data generalizes well to new environments and detects non-line-of-sight CIRs with an accuracy of 85%. We also show that integrating our anomaly score into a TOF-based extended Kalman filter (EKF) improves tracking performance by over 25%.

A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems

Automated valet parking (AVP) has attracted much attention as the entry point to autonomous driving. In an indoor environment, high-precision positioning systems are essential for AVP. Ultra-wideband (UWB) is one of the most widely adopted techniques. However, the base station placement significantly influences the system’s positioning accuracy, especially for the irregular architecture of underground parking lots. This article proposes a three-stage practical and economical layout planning approach for UWB base stations, including determining the deployment strategy and layout parameters and comprehensive adjustment and scheme verification. The approach considers regional differentiation accuracy requirements for AVP, such as ramp area, surface fluctuation area, and narrow area. The adopted positioning method of a UWB system is the time difference of arrival (TDOA), and the evaluation index of positioning accuracy is the horizontal dilution of precision (HDOP). Through experimental tests in an actual parking lot, the proposed approach is confirmed to ensure stability and economy with fewer UWB base stations and can meet the positioning accuracy requirements of AVP.

Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System

In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.