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.

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.

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.

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.