Hybrid Range Based Indoor Wireless Localization using Unconstrained Nonlinear Optimization for UWB Applications

Visible light communication (VLC) is a subset of OWC. VLC deliberates line of sight (LOS) connections as well as different reflections from different paths. In a practical condition, LOS links could be affected due to the existence of the obstruction between LED transmitter and PD receiver. However, it is possible to create the wireless link in a non-line of sight (NLOS) scenario when the reflected light paths are present. Nevertheless, in terms of received signal strength indication (RSS), there is still a probability of demolishing the system performance. This paper presents a new and unique indoor visible light communication-based power maximization of received signal under the condition of NLOS. Based on a white light emitting diode, a photo-detector and evolutionary algorithm, genetic algorithm (GA) is a one type of evolutionary algorithm. In this paper, we have used GA which performs the maximization of the received power. Simulation result reflects the feasibility of the proposed methodology. The maximization of VLC-based power in NLOS condition can benefit smarter indoor communication in near-future VLC domain.KeywordsGenetic algorithmNon-line of sightOptical communicationVisible light communication

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.

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.

In this paper, first, path loss models are developed for infrared optical wireless transmission inside an aircraft cabin. Second, a cellular network in the aircraft is considered and signal-to-interference ratio (SIR) maps are determined via simulation. For this purpose, a Monte Carlo ray-tracing (MCRT) simulation is performed in a geometric computer-aided design (CAD) cabin model with defined position, azimuth (AZ), elevation (EL) and field of view (FOV) properties of transmitters and receivers. Mathematical models are developed for line-of-sight (LOS) and non-line-of-sight (NLOS) path losses along particular paths, including estimation of the path loss exponent and the shadowing component. The shadowing is modeled according to a log-normal distribution with zero mean and standard deviation sigma. The validity of this model is confirmed in the paper. It is shown that irradiance distribution under LOS conditions experiences an attenuation with a path loss exponent of 1.92 and a shadowing standard deviation of 0.81 dB. In NLOS conditions, however, the path loss exponent varies, depending on the nature of the NLOS cases considered. The presented NLOS scenarios yield path loss exponent values of 2.26 and 1.28, and shadowing standard deviation values of 1.27 dB and 0.7 dB, respectively. Finally, the cabin is divided into cells and SIR maps are presented for different frequency reuse factors. It is shown that at the edges of the circular cells with diameter of 2.8 m, a SIR of -5.5 dB is achieved in a horizontal cross section of the cabin for frequency reuse of 1, and -2 dB and 3 dB for frequency reuse factors of 2 and 3, respectively. This means that in an aircraft cabin, for reuse factors less than three, viable communication at the cell edges is not feasible without additional interference avoidance or interference mitigation techniques.

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

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.

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.

Indoor localization system based on received signal strength (RSS) often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors. To identify non-line-of-sight status and line-of-sight (LOS) status and improve the accuracy of indoor localization, a D-S evidence theory based NLOS identification algorithm was proposed. The algorithm is divided into two parts. In the first part, respectively extract a variety of RSS features in NLOS and LOS status and choose the appropriate filter for data processing; in the second part, RSS features fused by D-S evidence theory are used to identify NLOS and LOS status. In an actual test environment, the algorithm was applied to fingerprinting localization, then analyzed the advantages of fusing RSS features and the influence of RSS features selection on experimental results. Finally, compared with several commonly used NLOS and LOS identification algorithm, it shows the proposed algorithm can deal with the indoor localization environment with obstacles with a high localization accuracy and good stability.

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.

Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

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.

Non-line-of-sight (NLOS) propagation can severely degrade the reliability of communication and localisation accuracy in indoor ultra-wideband (UWB) ‘location-aware’ networks. Link adaptation and NLOS bias mitigation techniques have respectively been proposed to alleviate these effects, but implicitly rely on the ability to accurately distinguish between LOS and NLOS propagation scenarios. A statistical NLOS identification technique based on the hypothesis-testing of received signal parameters in UWB propagation channels is discussed. In contrast to narrowband and wideband signals, UWB signals possess higher temporal resolution and robustness to multipath fading. We show that these characteristics result in differences in the statistics of (a) the time-of-arrival (TOA), (b) the received signal strength (RSS) and (c) the root-mean-squared delay spread (RDS) of the received signals, between LOS and NLOS propagation scenarios, which can be exploited for accurate channel identification. We statistically characterise the ability of TOA, RSS and RDS estimates to distinguish between LOS and NLOS propagation based on an extensive indoor measurement campaign. Our measurement results suggest that the RDS of UWB signals can, even in isolation and without complete statistical information, serve as a robust and computationally efficient indicator of the LOS/NLOS nature of propagation. Finally, we demonstrate the efficacy of the discussed NLOS identification method in a location-tracking application based on indoor UWB measurements.

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.

The existence of non-line-of-sight (NLOS) errors can significantly degrade the localization performance. In this letter, we address the time-of-arrival (TOA) based localization problem under NLOS conditions. To accurately localize a source, we propose a novel robust second-order cone relaxation (SOCR) method, which is not sensitive to NLOS errors. The proposed method does not require one to know any statistics of the NLOS errors, and instead, it only requires to know the upper bound on the magnitudes of the NLOS errors, which is relatively easy to obtain in practice. Simulation results show that the proposed robust SOCR method has good performance in various NLOS scenarios, and also it has a relatively lower computational complexity than the existing methods.

Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.