Multi-algorithm UWB-based localization method for mixed LOS/NLOS environments

This paper considers the global localization of a pedestrian via an ultra-wideband (UWB) ranging aided inertial navigation system (INS) and aims to address the challenges involved in proper processing of UWB range measurements. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS). This drop in accuracy is due to a significant unknown positive bias in the NLOS range measurements. Therefore, the measurement models used in UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. Previous work on bias compensation for UWB ranging that is used to aid an INS based localization assumes that the LoS and NLoS measurements are identified and distinguished from each other with absolute certainty. However, in practice, this assumption is hard to satisfy, and identifiers that determine the type of UWB range measurements deliver their results with only some level of certainty. To take into account the probabilistic nature of the NLoS identifiers, in this paper, we propose an adaptive localization based on the first-order generalized pseudo Bayesian (GPB) method to seamlessly handle the measurement model switching between LoS and NLoS UWB range measurements. The effectiveness of our proposed method is demonstrated via an experiment for pedestrian geolocation using a shoe-mounted INS system aided by UWB range measurements with respect to beacons with known locations.

At present, location technology divides into outdoor location technology and indoor location technology. Comparing with other indoor location methods, ultra-wide band (UWB)indoor location technology has the advantages of strong anti-multipath interference ability and signal penetration ability. But, when this method faces large obstacles or metal obstacles, non line of sight (NLOS) will still occur. Indoor location system is a part of our project Home Care System for The Elderly. The NLOS of Indoor location system reduces the reliability of Home Care System for The Elderly. In order to improve the accuracy of location and efficiency of operation, an NLOS detection method based on k-nearest neighbors (KNN) algorithm is proposed. This algorithm modeled the training samples to find the K value with the highest recognition accuracy. The experimental results show that The NLOS detection accuracy of this method is 78% in the simulated apartment scene. With the same number of samples, the accuracy of the widely used Convolutional Neural Networks (CNN) algorithm is 67%. The experimental results show that KNN algorithm is more suitable for Home Care System for The Elderly for it can keep high accuracy with less requirement for the number of samples than CNN.

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.

Relative positioning accuracy between two devices is dependent on the precise range measurements. Ultra-wideband (UWB) technology is one of the popular and widely used technologies to achieve centimeter-level accuracy in range measurement. Nevertheless, harsh indoor environments, multipath issues, reflections, and bias due to antenna delay degrade the range measurement performance in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. This article proposes an efficient and robust method to mitigate range measurement error in LOS and NLOS conditions by combining the latest artificial intelligence technology. A GP-enhanced non-linear function is proposed to mitigate the range bias in LOS scenarios. Moreover, NLOS identification based on the sliding window and Bayesian Conv-BLSTM method is utilized to mitigate range error due to the non-line-of-sight conditions. A novel spatial–temporal attention module is proposed to improve the performance of the proposed model. The epistemic and aleatoric uncertainty estimation method is also introduced to determine the robustness of the proposed model for environment variance. Furthermore, moving average and min-max removing methods are utilized to minimize the standard deviation in the range measurements in both scenarios. Extensive experimentation with different settings and configurations has proven the effectiveness of our methodology and demonstrated the feasibility of our robust UWB range error mitigation for LOS and NLOS scenarios.

Previous work shows that Average Bit Error Probability (ABEP) of SM systems under Non Line of Sight (NLOS) scenario is lower than that under Line of Sight (LOS) scenario because of a lower spatial correlation with a given Signal to Noise Ratio (SNR). However, when transmit power is constrained as a constant, the SNR under LOS scenario is larger than that under NLOS scenario benefiting from the strong LOS ray. Spatial correlation and SNR are two contradictory factors which both impact the performance of SM systems. In order to analyze performances of SM systems with a constant power under LOS and NLOS indoor scenarios, 15150 complex MIMO channel matrices are measured in a typical building of Shenzhen Graduate School, Harbin Institute of Technology. Based on measured data and motivated by the Minimum Distance Lower Bound (MDLB) approach, Euclid distances between received symbols of SM systems, as a monotonic function of ABEP, is analyzed. It is found that the effect of the SNR is much more important than the spatial correlation in the indoor environment so that SM system under LOS scenario shows a better performance than NLOS when the transmitting power is a constant. Furthermore, it is observed that bottlenecks of ABEP under LOS and NLOS scenarios are different. Under the LOS scenario, the bottleneck of ABEP is the antenna index error. On the contrary, under the NLOS scenario, the bottleneck of ABEP is the joint error. Using two extreme cases, such phenomena is analytically explained in the paper.

The error compensation and suppression effects of traditional ultrawideband (UWB) ranging in non Line of Sight (NLOS) environments are limited. The contribution of specific occlusions to UWB spatial perception in NLOS is ignored. To achieve comprehensive sensing of spatial information by UWB, we initially analyze the channel impulse response (CIR) and the underlying parameters of registers during UWB communication. By comparing the difference between NLOS and line-of-sight (LOS) environments for each parameter on a continuous time series, a fast discriminative method for UWB environment conversion is proposed. Further, combining the ensemble learning XGBoost classifier, an efficient NLOS occlusion recognition method is proposed. At the same time, an algorithm optimization based on a discrete degree threshold is designed. It is based on loss function probability matrix-weighted predictive labeling. The prediction matrix of the loss function of the XGBoost algorithm is used as label weights. The UWB prediction labels of continuous time series are weighted, which mitigates the effect of low-probability data on the overall prediction results. Finally, UWB spatial sensing experiments are carried out to verify the reliability of the proposed method. The experimental results show that the mutation in the parameter profile can effectively perceive the LOS/NLOS transition. The recognition accuracy of the proposed occlusion recognition method in conditions of human, metal, and wall occlusion is 94.44%, 92.00%, and 95.87%, respectively. In contrast to the origin method, the suggested algorithm’s average recognition accuracy has increased by 16.71%. Its precise recognition accuracy makes UWB spatial sensing more effective.

In this paper, we present a time delay analysis for indoor localization based on time difference of arrival (TDOA) algorithm, where we have performed both simulative and practical experiment together. Besides, we introduce the different channel parameter effects for line of sight (LOS) and non-line of sight (NLOS) scenario in indoor environment. This time delay analysis can be useful to understand the behavior of indoor channels and to choose the perfect technology for indoor localization. Results show remarkable effects of delay in TDOA localization in such environment. In addition, these results give enough information for improving localization techniques.

The high time resolution characteristic of ultra-wideband (UWB) signals enables the potential of accurate ranging in line-of-sight (LOS) propagation. However, Non-line-of-sight (NLOS) propagation can cause a large error in source geolocation. In order to mitigate NLOS errors this paper proposes and investigates a noble approach which makes a hybrid combination of fingerprinting (FP) positioning and an iterative Time of Arrival (TOA) real time positioning method. Moreover, to reduce the computational complexities in FP method, we introduce a unique idea for the arrangement of reference tags to create fingerprinting database. Each arrangement of reference tags resembles a shape of polygon, which size dominates the computational complexities of FP as well as proposed hybrid method. By analyzing the tradeoff between positioning accuracies and computational complexities in proposed method we determine the appropriate size of polygon to reduce the computational complexity while maintaining a higher positioning accuracy. Finally, in comparison with FP only positioning method the proposed hybrid method yields better performance and is much more robust in NLOS condition with improved positioning accuracy in indoor environment.

Nowadays indoor positioning applications have received great attentions among short range wireless communication systems. In particular, Wireless Sensor Network (WSN) is the most widely used technology for indoor localization as it is cheap and easy to deploy. However, location accuracy is limited by the harsh propagation of the Radio Frequency (RF) signals in indoor environments, for instance, caused by the presence of obstacles between wireless nodes, multi-paths, interference, etc. To overcome these effects and improve positioning accuracy, Radio Frequency Identification (RFID) technology is employed to assist the WSN based positioning. This paper proposes two hybrid positioning approaches based both on WSN and RFID. One solution uses an Extended Kalman Filter (EKF) algorithm while the other one uses a Particle Filter (PF) approach. Both methods are compared through Matlab simulations which demonstrate that in general the WSN/RFID hybrid approach is feasible and provides good positioning estimation capabilities even under harsh indoor conditions.

Multipath propagation effects encountered in mobile wireless channels provide additional degrees of freedom [Ganesan et al., 2000] that can be exploited via appropriate signaling and reception. In this work, based on a set of measurements conducted at Eurecom, we consider a novel approach of analyzing an Ultra Wide Band (UWB) indoor radio propagation channel by performing an eigen-decomposition and observing the scaling of the number of significant eigenvalues with the channel bandwidth by using the Akaike information criterion (AIC) and Minimum Description Length (MDL). These criterion are applied to estimate the number of degrees of freedom (DoF) of an UWB in an indoor environment. We evaluate our approach under both scenarios, line-of-sight (LOS) and non-line-of-sight (NLOS). Based on AIC and the MDL criterion, we find that this number is large; this has important consequences on the receiver design. We show also, that in opposition to the accepted idea in the literature, the number of Degrees of Freedom (DoF) does not increase linearly with the channel bandwidth. Hence, the results of the estimation of UWB channel entropy confirm that the number of DoF increases sub-linearly with the channel bandwidth.

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

For the mass demands of wireless communication services, the mobile location technologies have drawn much attention around the world. In wireless communication, one of the main problems with accurate location is nonline of sight (NLoS) propagation. To solve the problem, we present a new location algorithm with clustering technology by utilizing the geometrical feature of cell layout, time of arrival range measurements, and three base stations. The mobile location is estimated by solving the optimal solution of the objective junction based on the high density cluster. Simulations study was conducted to evaluate the performance of the algorithm for different NLoS error distributions and various upper bound of NLoS error. The results of our experiments demonstrate that the proposed algorithm is significantly more effective in location accuracy than range scaling algorithm, linear lines of position algorithm, and Taylor series algorithm, and also satisfies the location accuracy demand of E-911.

With the rise of the Internet of Things and smart cities, indoor positioning technology becomes more and more important. Among the existing network technologies, ultra-wide band (UWB) has the advantages of high transmission rate, and strong penetration ability. However, its signal propagation is susceptible to N on- Line-of-Sight (NLOS) effects. Therefore, this paper focuses on indoor positioning in NLOS environments, and studies the Chan-Taylor co-weighted positioning algorithm improved by machine learning. The main research contents include: 1. A machine learning method is proposed to identify and classify NLOS signals. Using a sliding window to dynamically calculate the variance and mean of multiple groups of TDOA measurements in a short period of time as the input to the recognition algorithm, and classify them into three categories: Line-of-Sight (LOS), low NLOS error, and high NLOS error. 2. Based on the Chan- Taylor co-location algorithm, the algorithm introduces the concept of weighting coefficient, optimizes the observation data, and finally obtains the target position coordinates. The simulation results show that the positioning accuracy and system stability of the algorithm are improved compared with the Chan algorithm and the Chan-Taylor co-location algorithm.