Non-line-of-sight Research Articles

QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Ensuring that the quality-of-service (QoS) requirements are satisfied in wireless communications systems with high user density is challenging due to the limitations on the transmit power budget and the number of resource blocks. In this paper, we propose a QoS-aware joint user scheduling and power allocation technique to enhance the number of served users in the downlink of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) with minimum QoS requirements guarantee. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints.We propose a generalized non-stationary multi-state channel model based on spherical wave propagation assuming that users under line-of-sight (LoS) and non-LoS (NLoS) transmission coexist in the same communication cell. This is done to accurately evaluate the proposed technique in realistic XL-MIMO scenarios. Numerical results reveal that the proposed CBS algorithm provides fair coverage over the whole cell area, achieving remarkable numbers of scheduled users with satisfied QoS requirements when users under the LoS and NLoS channel states coexist in the communication cell.

Pathloss and Airframe Shadowing Loss of Air-to-Ground UAV Channel in the Airport Area at UHF- and L-Band

In air-to-ground unmanned aerial vehicle (UAV) communications, an accurate channel propagation model is very important for the evaluation and design of communication systems. For this purpose, we have conducted several channel measurement campaigns in the airport area at UHF- and L-band (around 433 MHz and 1.518 GHz). In the line-of-sight (LoS) case, we give the distance-dependent pathloss models for both frequenciy bands. Meanwhile, it is found that the measured pathloss in the LoS scenario has a good agreement with the two-ray propagation model at short link distances. This is because of the strong ground reflection path. In the non-LoS (NLoS) case, we propose the elevation-dependent pathloss model at UHF-band based on the field measurement results. On the other hand, considering that the airframe of the UAV can block the wave propagation during the 3-D rotation in the air and the taking off and landing procedures, we specifically analyze the statistical characteristics of the airframe shadowing loss for the two cases and provide the loss distribution models for the fixed-wing, small-sized UAVs made of wood structure and carbon fiber skins.

A Simple Efficient Lightweight CNN Method for LOS/NLOS Identification in Wireless Communication Systems

Line-of-sight (LOS)/Non-line-of-sight (NLOS) identification in wireless communication systems is crucial for positioning, mobile communication, and wireless sensing. Conventional LOS/NLOS identification approaches generally employ channel features, such as the Rice factor, kurtosis of the received power, etc. However, these approaches have limited identification accuracy and cannot function efficiently in a dynamic environment. The deep learning approach can show better performance; however, it has high computational complexity issues. In this study, we first demonstrate that the channel impulse response (CIR) of the 5G channel outperforms channel frequency response employing convolutional neural networks (CNNs) for LOS/NLOS identification. Then, we propose a simple efficient lightweight CNN-based LOS/NLOS identification approach. The proposed one-dimensional CNN network can effectively extract CIR features and has cross-scenario adaptability. Additionally, for the first time, 5G experimental data were employed for performance verification. The experimental results demonstrate that the proposed approach can achieve an identification accuracy of 93.31% at a computational cost of 1.35 M FLOPs and has higher identification accuracy and speed than existing MWT-CNN deep learning approaches. The code is available at https://github.com/boa2004plaust/SEL-CNN.

Sub-6 GHz to mmWave for 5G-Advanced and Beyond: Channel Measurements, Characteristics and Impact on System Performance

In the 5G-Advanced and beyond systems, multi-frequency cooperative networking will become an inevitable development trend. However, the channels have not been fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the sub-6 GHz to millimeter-wave (mmWave) bands. In this paper, we carry out channel measurements at four frequency bands (3.3, 6.5, 15, and 28 GHz) in two scenarios including Urban Micro (UMi) and Outdoor-to-Indoor (O2I) with the same channel sounder. The channel characteristics are extracted and modeled, including path loss (PL), shadow fading, frequency dependence of cluster features, root mean square (RMS) delay spread (DS), Ricean K-factor, and the correlation properties. We mainly focus on the analysis of large-scale parameters and more consideration of the link budget coverage problem. We present the frequency dependence model of the channel characteristics. Among them, in the non-line-of-sight (NLoS) condition, it is found that except for theoretical value brought by higher frequency, additional path loss increment will be generated. Based on these channel characteristics, the impact on performance of the wireless system is analyzed including cell coverage radius, data rate and bit error rate (BER). The results can give an insight into the spectrum selection and optimization in 5G-Advanced and beyond multi-frequency communication systems.

Indoor Environment Learning via RF-Mapping

Intelligent integrated sensing and communication is one of key aspects of future wireless networks in which sensing can be leveraged to enhance communications and vice-versa. In this paper, we propose a novel sensing solution that can be used to represent an RF-environment. The proposed solution accounts for practical challenges such as limited time resolution due to limited bandwidth with no angle measurements while providing robustness to wireless propagation phenomena such as diffraction. Our proposed method leverages offline data collection during RF-mapping, and finds the location of virtual anchors (VAs), i.e., mirror images of a physical anchor w.r.t reflectors, through an iterative process called successive tap removal (STR). Afterwards, machine learning (ML) models are trained to predict dominant multipath components of the received wireless channel at a given location. Found VAs and their associated ML models stand for intermediate entities that represent an RF-environment. As an application, we use the developed models in the context of multipath assisted positioning to improve positioning accuracy in challenging indoor environments with heavy non-line-of-sight (NLoS) conditions. Finally, we extend our ideas to systems with multi-antenna transmitters and show that VA detection accuracy can be improved, bringing higher accuracy to the downstream positioning applications.

Deep Learning-Based Multipath DoAs Estimation Method for mmWave Massive MIMO Systems in Low SNR

To realize the direction-of-arrivals (DoAs) estimation without the prior information about the multipath number, a novel method using the deep learning is introduced for the millimeter (mmWave) massive multiple-input and multiple-output (MIMO) systems. In particular, the DoAs estimation is decomposed into three sub-problems, which are solved by the corresponding convolutional neural network (CNN), respectively. The estimation of multipath number is achieved by a multi-label classification model using the proposed CNN-I. Then, the proposed CNN-II is introduced for the DoA estimation of the line-of-sight (LOS) path. Based on the predicted multipath number obtained by the CNN-I, a regression model using the proposed CNN-III is applied for the DoAs estimation of non-line-of-sight (NLOS) paths. The proposed DoAs estimation method is implemented by learning the non-linear relationship between the sample covariance matrix of the received signal and the angles, thus reconstructing the mapping model. A series of results validate the superiority of the proposed DoAs estimation method in low signal-to-noise-ratio (SNR) regimes. The CNN-I is capable of achieving the good multipath number estimation performance across a range of SNRs. The classification model based on the proposed CNN-II and the regression model using the proposed CNN-III achieve the lower DoAs estimation root mean square error (RMSE) compared with some deep learning-based methods, estimation of signal parameters via rotational invariance techniques (ESPRIT) and Root-MUltiple SIgnal Classification (Root-MUSIC) methods. Remarkably, the proposed method using the CNN-II exhibits the good robustness in the DoA estimation of the LOS path. Furthermore, the low DoAs estimation RMSE is also achieved in the uniform planar array.

Compensating the NLoS Occlusion Errors of UWB for Pedestrian Localization With MIMU

The Ultra-Wideband (UWB) technology has become a competitive choice for indoor pedestrian localization due to its advantages in accuracy, compact size, reliability, power efficiency, and easy setup. However, the None-Line-of-Sight (NLoS) occlusion caused by the human body parts and obstacles in its propagation path may introduce significant location errors that limit its performance and field of application. To handle the NLoS occlusion errors and promote the localization accuracy for complex indoor environments, a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (MIMU)-assisted UWB localization system with NLoS occlusion error compensation techniques is provided. Firstly, an NLoS occlusion detection method using the built-in channel information of the UWB transceivers and the variation pattern of anchor-node distance is proposed. Secondly, an Extended Kalman Filter (EKF)-based attitude computation and a Zero-Velocity Update (ZUPT)-based continuous gait segmentation are introduced for inertial navigation during occlusion intervals. Then, the UWB and IMU parameters are integrated with an Unscented Kalman Filter (UKF) framework to compensate for the occlusion error and improve the accuracy and robustness. Finally, a battery-powered miniature wearable device is developed and the proposed techniques are verified with experimental studies. The results demonstrate the feasibility and effectiveness of the presented techniques, which provides a good reference for related Internet of Things (IoT) applications.

Near-Field Channel Estimation in Mixed LoS/NLoS Environments for Extremely Large-Scale MIMO Systems

Accurate channel model and channel estimation are essential to empower extremely large-scale MIMO (XL-MIMO) in 6G networks with ultra-high spectral efficiency. With the sharp increase in the antenna array aperture of the XL-MIMO scenario, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, most of the existing XL-MIMO channel models fail to describe mixed line-of-sight (LoS) and non-line-of-sight (NLoS) path components simultaneously. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to match the practical near-field XL-MIMO scenario, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) and MIMO advanced RD (MIMO-ARD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Moreover, the Cramér-Rao lower bound (CRLB) of the proposed algorithm is derived in this paper. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods in both the theoretical channel model and the QuaDRiGa channel emulation platform.

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era.

The Internet-of-Things (IoT) has been used with greater frequency to track peoples' daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

Indoor Visible-Light 3D Positioning System Based on GRU Neural Network

With the continuous development of artificial intelligence technology, visible-light positioning (VLP) based on machine learning and deep learning algorithms has become a research hotspot for indoor positioning technology. To improve the accuracy of robot positioning, we established a three-dimensional (3D) positioning system of visible-light consisting of two LED lights and three photodetectors. In this system, three photodetectors are located on the robot’s head. We considered the impact of line-of-sight (LOS) and non-line-of-sight (NLOS) links on the received signals and used gated recurrent unit (GRU) neural networks to deal with nonlinearity in the system. To address the problem of poor stability during GRU network training, we used a learning rate attenuation strategy to improve the performance of the GRU network. The simulation results showed that the average positioning error of the system was 2.69 cm in a space of 4 m × 4 m × 3 m when only LOS links were considered and 2.66 cm when both LOS and NLOS links were considered with 95% of the positioning errors within 7.88 cm. For two-dimensional (2D) positioning with a fixed positioning height, 80% of the positioning error was within 9.87 cm. This showed that the system had a high anti-interference ability, could achieve centimeter-level positioning accuracy, and met the requirements of robot indoor positioning.

Performance Analysis and Simulation of Millimeter Wave Cell-Free mMIMO Networks

Towards the 5Gnetworks and beyond, there are a lot of emerging technologies. These technologies include but not limited to; multiple input multiple output (MIMO), cell-Free networks, and millimeter wave bands. The cellular MIMO can provide a satisfied performance for users except the shadowed and the cell-edge ones. In order to overcome this disadvantage, the cell-Free networks are deployed. Through applications of distributed access points (APs), the cell-Free networks can provide a ubiquitous coverage for users as whole. Therefore, they can provide a high throughput for users. Moreover, the applications of millimeter wave bands can provide a high bandwidth and hence, a high throughput for users. In other words, the application of the cell-Free technology combined with the millimeter wave bands can extremely enlarge the users’ throughput. This is the motivation of our manuscript. The purpose of this manuscript is to provide mathematical model and simulation for the cell-Free mMIMO network, operating in the millimeter wave bands. The performance metrics can include; the spectral efficiency (SE), bit error rate (BER), and energy efficiency (EE). It is observed that the centralized cooperation among the APs can let users have a satisfied throughput even the system employs the maximal ratio combining (MRC). Furthermore, all cooperation levels, among APs, can perform well even for non-light of sight (NLOS) environment.

Unsupervised LoS/NLoS identification in mmWave communication using two-stage machine learning framework

Identification of line-of-sight (LoS)/ non-LoS (NLoS) condition in millimeter wave (mmWave) communication is important for localization and unobstructed transmission between a base station (BS) and a user. A sudden obstruction in a link between a BS and a user can result in poorly received signal strength or termination of communication. Channel features obtained by the estimation of channel state information (CSI) of a user at the BS can be used for identifying LoS/NLoS condition. With the assumption of labeled CSI, existing machine learning (ML) methods have achieved satisfactory performance for LoS/NLoS identification. However, in a real communication environment, labeled CSI is not available. In this paper, we propose a two-stage unsupervised ML based LoS/NLoS identification framework to address the lack of labeled data. We conduct experiments for the outdoor scenario by generating data from the NYUSIM simulator. We compare the performance of our method with the supervised deep neural network (SDNN) in terms of accuracy and receiver characteristic curves. The proposed framework can achieve an accuracy of 87.4% and it outperforms SDNN. Further, we compare the performance of our method with other state-of-the-art LoS/NLoS identification schemes in terms of accuracy, recall, precision, and F1-score.

Statistical channel model for 6G communication networks in Banda Aceh City

Wireless technology is expected to undergo considerable transformation because of the numerous services offered by 6G communication networks, which virtually entirely encompass every part of everyday life and use a variety of devices. Channel modeling is an essential factor in designing 6G communication networks. To meet the channel requirements of future 6G communication networks, it is crucial to measure the channel to consider path loss, multi-band, fading, blocking effect, multipath clustering, transmitter, and receiver moving speed/direction/time. The goal of this paper is to design and evaluate a 6G communication network in Banda Aceh City using a statistical channel model. The channel model is associated with environmental conditions such as rainfall and humidity. The method is then based on computer simulation using the NYUSIM simulator to complete the channel modeling using an operating frequency of 95 GHz with a bandwidth of 800 MHz. In the simulator, the designed 6G channel model is evaluated in both line-of-sight (LOS) and non-line-of-sight (NLOS) network environments. In addition, the designated network parameters, such as the coverage area, angle of arrival (AoA), angle of departure (AoD), and power delay profile (PDP), are simulated. The results, at AoA, the value of received power for LOS conditions ranges from -86 dBm to -101 dBm, while the value for NLOS conditions ranges from -91 dBm to -111 dBm. Under LOS conditions, the received power for AoD ranges from -86 dBm to -101 dBm, whereas under NLOS conditions, it ranges from -91 dBm to -111 dBm. In the omnidirectional PDP, the pathloss value for the LOS condition is 99.8 dB and the delay is 17.9 ns, while the pathloss value for the NLOS condition is 104.2 dB with a delay of 28.1 ns. For the directional PDP, the LOS condition yields a path loss of 106.4 dB and a delay of 2.9 ns, while the NLOS condition yields a path loss of 110.5 dB and a delay of 3 ns. Conclusions. The simulation indicated that the AoA, AoD, and PDP in terms of received power, pathloss, and propagation delay are in acceptable conditions for a 6G network in Banda Aceh City in the two observed environments. Therefore, it is conceivable to establish a 6G network in Banda Aceh City in the future.

LOS and NLOS identification in real indoor environment using deep learning approach

Visibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a Deep Learning (DL) model to classify LOS/NLOS condition while analyzing two Channel Impulse Response (CIR) parameters: Total Power (TP) [dBm] and First Path Power (FP) [dBm] is proposed. The experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100% and 95% in static and dynamic senarios, respectively. The proposed model improves the classification rate by 2-5% compared to other Machine Learning (ML) methods proposed in the literature.

A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.

Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning

Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB’s positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) and extended Kalman filter (EKF) would also visibly degrade UWB positioning accuracy. To overcome the impacts of NLOS errors and truncation errors, this paper introduced a robust-theory-based particle filter (RPF) into UWB positioning. In such a method, the IGG-III model and PF were adopted to limit the impacts of NLOS errors and truncation errors, respectively, by introducing a weight inflation factor and particle group. For comparison, the Bancroft, LS, EKF, unscented Kalman filter (UKF), cubature Kalman filter (CKF), PF, and RPF were also presented. Here, the influences of truncation errors were analyzed by comparing the results based on LS and EKF with those calculated by UKF, CKF, and PF. The impacts of NLOS errors were evaluated by making a comparison between the results of PF and RPF. Results based on a set of simulated UWB data and a group of experiment UWB data demonstrated that the RPF can significantly avoid the positioning errors caused by both truncation errors and NLOS errors. In general, position improvements percentages of 57.2%, 52.7%, 39.6%, 38.2%, 26.6%, and 20.4% can be obtained by RPF compared to those calculated by Bancroft, LS, EKF, UKF, CKF, and PF, respectively. As a comparison, the truncation error would lead to about 8.1%, 10.1%, and 33.2% accuracy decrease in the north, east, and vertical directions on average. Such accuracy-decrease rates caused by NLOS were 6.1%, 5.2%, and 25%.

Millimeter-Wave Dual-Band MIMO Channel Capacity Analysis Based on Climate Data: A Samsun Province Case Study

The analysis of multiple-input multiple-output (MIMO) channel capacity is important for developing and optimizing high-speed wireless communication systems that can meet the growing demand for data-intensive applications. This study aims to analyze the 4 × 4 MIMO channel capacity of outdoor urban and rural environments using the NYUSIM simulator. The channel models are designed for 28 GHz and 39 GHz frequencies for both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Realistic channel models are simulated using annual climate data collected in Samsun province, Turkey in three different environments: urban microcell (UMi), urban macrocell (UMa), and rural macrocell (RMa) areas. According to the annual average channel capacity analysis, it is observed that there is a very small capacity difference between UMi and UMa areas at 28 GHz and 39 GHz frequencies in the LOS region, while the RMa area is found to have a very low capacity compared to the UMi and UMa areas. The channel capacity for RMa is found to be approximately eight times smaller than UMi and UMa. In the NLOS region, channel capacities decrease significantly (between 312 and 3953 times) compared to the LOS region, with the UMa area having the greatest capacity and the UMi area having the lowest capacity. Compared to the UMi channel capacity, the RMa channel capacity is 1.36 times higher for 28 GHz and 1.28 times higher for 39 GHz. When the monthly changes in channel capacity are examined, it is discovered that the amount of precipitation has the greatest impact on channel capacity, and the capacity decreases as the rain rate increases. The highest correlation between channel capacity and rain rate was −0.97 for RMa, with a 28 GHz frequency and LOS conditions. Additionally, it becomes clear that channel capacities increase in the summer months as the temperature rises and humidity and pressure fall.

Robust TDOA-Based Indoor Localization Using Improved Clock-Sync-Scheme and Multilevel Constrained ARPF

The need for indoor positioning research is driven by the contradiction between the lack of satisfactory location information available indoors and the emergence of an increasing number of indoor location-based services. The ability of ultra-wideband (UWB) technology to effectively estimate the signal arrival time and thereby obtain highly accurate ranging information makes it a research hotspot for indoor positioning systems. However, the scalability, user capacity, and ranging performance degradation under non-line-of-sight (NLOS) conditions are challenges that hinder its wide application. To address these issues, this paper proposes an improved highly available indoor positioning system that uses time difference of arrival (TDOA)-based UWB. Within this system, a novel UWB wireless clock synchronization scheme was designed to reduce synchronization errors, improve ranging accuracy, reduce synchronization frequency, and allocate more channel time for positioning. To identify observations with NLOS errors and mitigate their effects on localization, this paper implements an adaptive robust particle filtering algorithm (ARPF) based on multiple robustness factors. The multi-level constrained ARPF considers information such as signal quality, historical observations, and floor map. In a real-world environment, the proposed method is tested using multiple forms of tag devices. The results show that the proposed method outperforms the general particle filter, cutting the relative error by about the half, especially in the worst badge tests.

Machine learning based GNSS signal classification and weighting scheme design in the built environment: a comparative experiment

None-Line-of-Sight (NLOS) signals denote Global Navigation Satellite System (GNSS) signals received indirectly from satellites and could result in unacceptable positioning errors. To meet the high mission-critical transportation and logistics demand, NLOS signals received in the built environment should be detected, corrected, and excluded. This paper proposes a cost-effective NLOS impact mitigation approach using only GNSS receivers. By exploiting more signal Quality Indicators (QIs), such as the standard deviation of pseudorange, Carrier-to-Noise Ratio (C/N0), elevation and azimuth angle, this paper compares machine-learning-based classification algorithms to detect and exclude NLOS signals in the pre-processing step. The probability of the presence of NLOS is predicted using regression algorithms. With a pre-defined threshold, the signals can be classified as Line-of-Sight (LOS) or NLOS. The probability of the occurrence of NLOS is also used for signal subset selection and specification of a novel weighting scheme. The novel weighting scheme consists of both C/N0 and elevation angle and NLOS probability. Experimental results show that the best LOS/NLOS classification algorithm is the random forest. The best QI set for NLOS classification is the first three QIs mentioned above and the difference of azimuth angle. The classification accuracy obtained from this proposed algorithm can reach 93.430%, with 2.810% false positives. The proposed signal classifier and weighting scheme improved the positioning accuracy by 69.000% and 40.700% in the horizontal direction, 79.361% and 75.322% in the vertical direction, and 75.963% and 67.824% in the 3D direction.

TDOA-Based Target Tracking Filter While Reducing NLOS Errors in Cluttered Environments.

We consider tracking a moving target in a wireless communication system that is based on the radio signal. Considering a bounded workspace with many unknown obstacles, we handle tracking a non-cooperative transmitter using multiple signal receivers. Here, a non-cooperative transmitter is a transmitter whose signal emission time is not known in advance. We consider a time difference of arrival (TDOA) location problem, which locates the transmitter by processing the signal measurement time at multiple receivers. In tracking a non-cooperative transmitter, non-line-of-sight (NLOS) errors occur if obstacles block the LOS line connecting the receiver and the moving transmitter. Our article addresses how to track a moving transmitter while decreasing the NLOS error in TDOA-only measurements. We propose an algorithm to localize a transmitter while decreasing the NLOS error in TDOA measurements. For tracking a moving transmitter in real time, we integrate the proposed localization algorithm and the interacting multiple model Kalman filter (IMM KF). As far as we know, our article is novel in tracking a moving transmitter based on TDOA-only measurements in an unknown mixed LOS/NLOS workspace. We show that the proposed filter considerably decreases the NLOS errors in TDOA-only measurements while running fast. Therefore, the proposed tracking scheme is suitable for tracking a moving transmitter in real time. Through MATLAB simulations, we show that the proposed filter outperforms other state-of-the-art TDOA filters, considering both time efficiency and tracking accuracy.