An improved NLOS detection scheme using stochastic characteristics for indoor localization

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.

5G NR mm Wave promises accurate positioning down to the centimeter level. However, mmWave signals endure prismatic propagation, making them prone to signal blockages and non-line of sight (NLoS) communications. To achieve a precise positioning solution, it is rather essential to filter out NLoS gNBs as they yield erroneous pose estimation of IoT vehicular applications. Previous works have attempted to address this issue, however, they are either based on impractical or invalid assumptions about the operation scenario. In this paper, a novel, yet, simple and realistic NLoS detection algorithm is developed. The proposed method measures the discrepancy between Received Signal Strength (RSS)-based and time-based ranges as means to detect NLoS operation. To validate the proposed NLoS detection method, it was incorporated into a Kalman Filter (KF) that fuses the angle of departure (AoD) and round trip time (RTT) measurements from multiple gNBs in a loosely coupled fashion. The proposed method was evaluated on quasi-real measurements acquired from a highly validated 5G simulation tool that simulates the cores of downtown Toronto. The proposed method demonstrates superior results, as it sustains a sub-1m level of accuracy for around 95% of the time, as compared to merely 29% of the time without the NLoS detection.

Modern indoor positioning systems are used to track machines or navigate people. Besides localization accuracy, one emerging key requirement is the robustness of a system with regard to changes in the environment. One mechanism to improve this robustness is the detection of Non-line-of-sight (NLOS) situations. In this paper, a novel, predictive NLOS detection concept is introduced. Moving objects induce changes in the Channel Impulse Response. While close-range obstacles are identified by a pulsing first path peak amplitude, objects further away can be detected using a Gabor filter bank. Both features for detection have been investigated in ideal and real-world environments with the results showing clear evidence that our novel method is delivering good-confidence NLOS detection.

Collecting location information of persons or things in sensor networks enables a variety of services and applications. For such location information, time of arrival (TOA) or angle of arrival (AOA) of radio waves is often used. Moreover, a hybrid use of them, called hybrid-TOA/AOA (HTA), has also been proposed for performance improvement. However, when the direct wave is blocked by obstacles in the non-line-of-sight (NLOS) environment, the estimation accuracy is severely deteriorated. To coordinate this problem, an iterative minimum residual (IMR) scheme has been proposed for TOA-based estimation. In the IMR scheme, the NLOS nodes are sequentially detected and eliminated, and the estimation performance is improved. However, the IMR scheme for HTA measurements has not been considered. Therefore, in this paper, we propose a new NLOS detection scheme based on IMR scheme for HTA localization and evaluate its improved performances by computer simulations.

As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.

The main drawback to improve the precision in cellular location systems based on time delays is the presence of non-line-of-sight (NLOS) propagation, introducing significant and hardly predictable errors in the measurements used in location. In this paper we propose a technique to previously detect and estimate the NLOS ratio present in the measurements. A comprehensive research on the Time of Arrival (TOA) dependence on the NLOS ratio existing in the measurements leads to estimate that ratio from the deviation calculated directly from the available measurements. NLOS detection and ratio estimate is very useful to classify and weight the measurements available in order to perform the following location estimate with the most accurate ones. Several simulations have been conducted to show the precision of ratio estimate and the measurements rating based on it.

Urban environments present major challenges to the global navigation satellite system (GNSS) positioning to estimate the position of a self-driving vehicle. GNSS signals are often obstructed by buildings and other obstacles, leading to reflected and diffracted signals, which cause major positioning errors and known as line-of-sight (LOS) and non-line-of-sight (NLOS) multipath errors. Particularly, real-time kinematic (RTK)-GNSS, which can estimate the position in the centimeter-level accuracy, cannot estimate the precise position when there exist NLOS multipath errors. If we use NLOS GNSS code and carrier phase multipath measurements into the RTK-GNSS algorithm, carrier phase integer ambiguity cannot be solved, or wrong integer ambiguities are estimated. As a result, integer ambiguity resolved to fix solutions cannot be estimated, or wrong fix solutions which have large position error are estimated. That is a major barrier to using RTK-GNSS in autonomous driving. In this paper, we propose a novel NLOS detection and exclusion technique to enhance existing RTK-GNSS positioning in urban areas using only GNSS measurements alone. The key idea behind the proposed technique is that the NLOS pseudorange error will always be a positive value. NLOS pseudorange is longer than the true range, which is the distance between the GNSS antenna and satellite, due to the time delay caused by reflection and diffraction. The distribution of the NLOS error is not along with a Gaussian distribution. We use this phenomenon to detect the NLOS signals. We propose a technique to detect and eliminate the NLOS multipath using double-differenced pseudorange residuals (DD-PR). In the RTK-GNSS, DD GNSS measurement between the rover and base station is typically used. Here, we compute the DD-PR to detect the NLOS signals. In order to compute the DD-PR, the true position of the vehicle must be determined in advance. To solve this contradiction, we use a particle filter. The particle filter generates the position hypotheses, which is called as “particle”. These particles are used as the position candidate for estimating the DD-PR. When the DD-PR can be calculated at each particle position, we detect the large positive errors in DD-PR as NLOS signals. Then we compute the integer ambiguity of only the LOS DD carrier phase to estimate the precise vehicle position. The flow of the proposed technique is as follows: (1) The hypothetical user positions are created as particles, where the state vectors of these particles are 3D positions, x, y, and z. The initial particles are distributed based on the solution of the normal GNSS point positioning and its variance. (2) In the prediction step of the particle filter, the particle state in the next time step is estimated using the velocity of each particle computed from GNSS Doppler measurements. (3) Next, we compute the DD-PR at each particle location. The receiver clock bias, satellite clock bias, ionosphere error, and troposphere error in GNSS pseudorange are canceled through DD computation. As a result, DD geometrical range between receiver and satellite and DD multipath errors remain in the DD measurement. We compute DD-PR using the particle position. As a result, the only multipath error remains if the particle is close to the true position. NLOS multipath error is always positive value; we generate the subset of the LOS measurement hypothesis at each particle using a simple threshold test. (4) Next, we compute the RTK-GNSS position solution using the generated subset of the LOS measurement hypothesis at each particle location. It is expected that the RTK-GNSS fix solution can be calculated if the particle location is close to the true position because the NLOS signal has been eliminated. By contrast, if the particle is far from true position, the integer ambiguity cannot be solved because of using the incorrect subset of LOS measurements. The likelihood of each particle is computed from a distance between the particle location and the RTK-GNSS fix solution. The particle that is closest to the true position has a higher likelihood than the other particles. The probabilistic weight of each particle is then updated based on its likelihood. (5) Finally, all the particles are resampled based on their new weights. Particles that are sufficiently close to the true position survive this resampling step, and the average position of the remaining particles are employed as the final estimated user position. To confirm the effectiveness of the proposed technique, a kinematic positioning test was performed in Shinjuku, Tokyo, Japan. From the several positioning tests, the proposed technique could dramatically improve the availability and accuracy of RTK-GNSS fix solutions in the dense urban canyon where there exist a lot of NLOS multipath signals.

The present paper considers how to improve estimation accuracy in a time of arrival (TOA) localization system using UWB in a non-line-of-sight (NLOS) environment for medical network. This method consists of step-by-step compensation on the basis of two approaches considering a reference position that is estimated from data affected by NLOS delay. The first consideration consists of determining NLOS delays for each node, performing compensation to alleviate the effect on line of sight (LOS) nodes through a step-by-step compensation for the NLOS delay. The second consideration consists of compensating the effect of NLOS delay on the position determined on the basis of node distribution and geometrical relations of the estimated positions. Using these considerations, we show that the proposed method outperforms the conventional NEWTON method in terms of estimation accuracy. Under certain conditions, it also exhibits better characteristics than the maximum likelihood estimation method, where environmental parameters such as NLOS delays are known.

Due to the wide deployment of wireless local area networks (WLANs) and the easy acquirement of received signal strength (RSS), indoor localization based on RSS has attracted considerable attention in both academia and industry. In this paper, we extract the scatter factor from RSS and then propose a novel indoor localization scheme based on location fingerprinting and curve fitting techniques. The scheme is set up in two phases. In the offline phase, we create a fingerprint and apply the curve fitting technique to construct fitted RSS-distance functions in LOS (Line Of Sight) and NLOS (None Line Of Sight) condition, respectively. In online positioning phase, the proposed optimized fingerprinting-based localization algorithm (OFPL) uses trilateration technique to assist the fingerprinting localization in choosing the proper location of the receiver. Through conducting field and extensive experiments, we can draw the conclusion that our proposed algorithm can obtain some improvement in localization accuracy compared with the classical fingerprinting-based localization.

Positioning is one of the key technologies to support the application of intelligent connected vehicles (ICV). GNSS, as one of main measures of ICV positioning, cannot be available in indoor scenarios. The emergence of indoor wireless positioning technologies, such as UWB and WIFI, has solved this problem. However, they are affected by the non line of sight (NLoS) propagation of the signal, resulting in lower positioning accuracy. To improve the vehicle indoor positioning performance with NLoS state, we propose an ICV indoor positioning method called CGPC-EMPF. First, the Chan-GN algorithm is improved with permutation and combination consistency, whose result is used as the observation of particle filter. For the vehicle motion prediction without a Strapdown inertial navigation system, we propose a vehicle motion prediction model based on a priori information. Then, the NLoS correction model is established by a particle filter. Finally, the corrected measurements are substituted into the Chan-GN algorithm to obtain the final results. It realizes accuracy enhancement by introducing a vehicle motion state to correct errors coursed by NLoS. Compared with the multi-sensor fusion positioning algorithm, our method has lower complexity and cost due to a single sensor application.

Using Time of Arrival (TOA) on ultra-wideband (UWB) signal is well known as a promising indoor localization technique. However, Non Line-of-Sight (NLoS) issue resulting from complicated scenarios is considered as a key challenge to transfer this technology into mature products. In this paper, a convex hull based node selection method is proposed to improve the accuracy of NLoS identification and mitigation. In particular, two reference nodes are selected adaptively from four nodes to carry out a 2-dimension (2D) indoor localization in a convex hull based effective localization area (ELA). Performance is evaluated using measurement data collected from a Medav UWB SIMO channel sounder, benchmarked to the parametric identification method and the first peak detection method.

In this paper, we propose a new joint time-of-arrival (TOA) estimation and non-line-of-sight (NLOS) identification approach for ultra-wideband (UWB) localization systems. We use an energy-based TOA estimation (EBE) algorithm. The NLOS detection is based on the ratio between the first path power (FPP) and the total signal power (TSP) of the received signal folded version (RSFV) of the EBE algorithm. The proposed approach is evaluated by Monte Carlo simulation on the CM1 and CM2 channel models of the standard IEEE802.15.4a. The simulation demonstrates that the correct identification of channel models with the proposed approach gives better results than those based on joint likelihood ratio.

High-precision indoor localization systems (ILSs) are critical for applications such as human smartphone navigation, autonomous robotics and automated warehouse and factory design. This paper presents a novel fingerprinting-based ILS, which features a decimeter-level localization accuracy, the ability to function in a constantly changing non line-of-sight (NLoS) environment, and user privacy protection without the need for heavy computations. The proposed ILS is able to maintain its localization accuracy in a constantly changing environment and to camouflage the user’s location by leveraging multipath propagation. The method was successfully tested both by experimental verification using the ultra-wideband communication standard and a ray-tracing simulation. An average localization error of 6 cm is demonstrated for a stationary or slow-moving receiver. An average error of 30 cm is demonstrated for a receiver that is moving at a fast walking pace. The obtained localization accuracy is comparable to the accuracy of the state-of-the-art localization algorithms. At the same time, the proposed approach solves two practical challenges faced by ILSs: robustness to changing environments with moving objects and the high computation requirements of user privacy protection. The high degree of user privacy was evaluated using a set of corresponding metrics.

The present paper considers how to improve estimation accuracy in a time of arrival (TOA) localization system using UWB in a non-line-of-sight (NLOS) environment for a sensor network. This algorithm consists of step-by-step compensation on the basis of two approaches considering a reference position which is estimated from data affected by NLOS delay. The first consideration consists of determining NLOS delays for each node, performing compensation to alleviate the effect on line of sight (LOS) nodes through a step-by-step compensation for the NLOS delay. The second consideration consists of compensating the effect of NLOS delay on the position determined on the basis of node distribution and geometrical relations of the estimated positions. Using these considerations, we show that the proposed algorithm outperforms the conventional algorithm in terms of estimation accuracy.

In time of arrival (TOA) based indoor personnel positioning system, the human body mounted with positioning devices could cause non-line-of-sight (NLOS) propagation and further give rise to large ranging errors, thus reducing the accuracy of localization. A novel NLOS identification and mitigation method based on UWB signal power is proposed in this paper, which solves the problem that the performance of classical least squares (LS) algorithm severely degrades in NLOS environments. An improved self-learning LS localization algorithm is also introduced, overcoming the drawbacks of LS estimator that it requires at least three range estimates for an unambiguous solution. Furthermore, we demonstrate the proposed approach outperforms traditional LS algorithm with an increasing localization accuracy by 50% in NLOS scenarios.