Real Indoor Environment Research Articles

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna.

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

Range Expansion for Wireless Power Transfer Using Joint Beamforming and Waveform Architecture: An Experimental Study in Indoor Environment

Far-field Wireless Power Transfer (WPT) has emerged as a potential power source for the Internet of Things (IoT) and Wireless Sensor Network (WSN). The expansion of the power transfer range is one of the key challenges to make the technology viable. In this letter, we experimentally study a channel-adaptive joint beamforming and waveform architecture to expand the power transfer range. WPT experiments have been conducted in a variety of wireless channels at various distances in a realistic indoor environment. The measurement data have been fitted using a simple analytical model to analyze the output DC power and achievable range improvement depending on the signal design schemes and the number of tones and antennas. The model shows a clear relationship between signal design versus output DC power and achievable range, and highlight the significant benefit of the proposed architecture to expand the power transfer range.

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

With the advancement of wireless technologies and sensing methodologies, many studies have shown that wireless signals can sense human behaviors. Human activity recognition using channel state information (CSI) in commercial WiFi devices plays an important role in many applications. In this paper, a framework for human activity recognition was constructed based on WiFi CSI signal enhancement. Firstly, the sensitivity of different antennas to human activity was studied. An antenna selection algorithm was proposed, which can make a choice of the antenna automatically based on their sensitivity in accordance with different activities. Secondly, two signal enhancement approaches, which can strengthen the active signals and weaken the inactive signals, were proposed to extract the active interval caused by human activity. Finally, an activity segmentation algorithm was proposed to detect the start and end time of activity. In order to verify and evaluate the methods, extensive experiments have been conducted in real indoor environments. The experimental results have demonstrated that our solutions can eliminate a large number of redundant information brought by insensitive and inactive signals. Our research results can be put into use to improve recognition accuracy significantly and decrease the cost of recognition time.

Physical–Chemical Coupling Model for Characterizing the Reaction of Ozone with Squalene in Realistic Indoor Environments

Squalene can react with indoor ozone to generate a series of volatile and semi-volatile organic compounds, some of which may be skin or respiratory irritants, causing adverse health effects. Better understanding of the ozone/squalene reaction and product transport characteristics is thus important. In this study, we developed a physical-chemical coupling model to describe the behavior of ozone/squalene reaction products, that is, 6-methyl-5-hepten-2-one (6-MHO) and 4-oxopentanal (4-OPA) in the gas phase and skin, by considering the chemical reaction and physical transport processes (external convection, internal diffusion, and surface uptake). Experiments without intervention were performed in a single-family house in California utilizing time- and space-resolved measurements. The key parameters in the model were extracted from 5 day data and then used to predict the behaviors in some other days. Predictions from the present model can reproduce the concentration profiles of the three compounds (ozone, 6-MHO, and 4-OPA) well (R2 = 0.82-0.89), indicating high accuracy of the model. Exposure analysis shows that the total amount of 6-MHO and 4-OPA entering the blood capillaries in 4 days can reach 14.6 and 30.1 μg, respectively. The contribution of different sinks to ozone removal in the tested realistic indoor environment was also analyzed.

Frequency-Domain SWIPT and Modulation Classification: Design and Experimental Validation

Simultaneous wireless information and power transfer (SWIPT) constitutes an emerging paradigm that prolongs the lifetime of energy-constrained devices, such as wireless sensors and Internet-of-Things (IoT) nodes. Its frequency-domain (FD) variant enables energy harvesting (EH) by using (low-power) local oscillators/mixers. In this paper, a novel FD-SWIPT waveform design that minimizes the multitone interference induced to the information signal by the energy signal, thus eliminating the need for using receive filters to this end, is described. The inherent interference suppression of the proposed strategy allows also for applying modulation classification (MC). This functionality is highly desirable in contemporary networks, where the access points utilize adaptive transmission. In this context, we analytically derive the average error probability of the proposed waveform over a Rayleigh fading channel for various modulation schemes under non-zero interference and frequency synchronization errors. Furthermore, we optimize the power of the energy tones, such that the signal-to-interference-ratio at the information signal is maximized subject to the EH and transmission power constraints. In addition, we investigate the coexistence of SWIPT with blind MC under this framework. Numerical simulation results reveal that the proposed approach substantially increases both the data rate and the harvested power in comparison to the conventional power-splitting method at the cost of a negligibly higher error probability. Also, they indicate that the employed MC scheme achieves a high success rate even in the low signal-to-noise-ratio regime. The proposed concept is validated experimentally in a realistic indoor environment by using a testbed based on software-defined radio units.

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

In this paper, we present a simple-structure Pedestrian Dead Reckon (PDR) system based on commercial IMU sensor and UWB ranging system. In PDR system, the accuracy of step and heading angle estimation completely decide the precision of location result. In order to select proper zero-velocity intervals for step and heading estimations in the foot stance/still stage, a modified zero-velocity detection way called Heuristic Step Detection (HSD) has been designed based on zero-velocity update algorithm (ZUPT). Based on the modified zero-velocity detection algorithm, i.e., HSD, an Kalman-type filter is used to get rough heading angle by fusing zero-velocity information and single UWB anchor ranging results. After that, a constrained sigma point based filter is used to further constrain heading angle range. Moreover, the range measures, provided by a UWB localization system with only one reference anchor, are used to correct the pedestrian step length. Trough UWB ranging measures analysis between two consecutive steps, the step length and pedestrian heading direction correcting processes are related to each other, and as a result, a more stable positioning result can be gotten. The corresponding practical experiments are conducted in real indoor environment over 2000 meters, and the results show that, compared with only INS-aided PDR, our scheme can reduce the average position error by more than 80%, and it can achieve long-term high accuracy and robust localization results.

A Prototype Orthogonal Vertical Beamforming for Indoor Wireless Communications

The concept of Orthogonal Vertical Beamforming (OVB) is proposed in this paper to eliminate interference when users remain at the same angle in the horizontal plane but are positioned at different distances. This orthogonal property helps systems avoid interference in the vertical direction of the mainbeam. A fully constructed prototype tested in a real indoor environment validates the proposed concept, revealing that the proposed OVB provides higher Signal Interference plus Noise Ratio (SINR) and Packet Error Rate (PER) in the vertical plane than conventional vertical beamforming and Orthogonal Beamforming.

Exploring Fast Fingerprint Construction Algorithm for Unmodulated Visible Light Indoor Localization.

The study of visible light indoor position has received considerable attention. The visible light indoor position has problems such as deployment difficulty and high cost. In our system, we propose a new fingerprint construction algorithm to simplify visible light indoor position. This method can realize the rapid construction of a visible fingerprint database and prove that the fingerprint database can be used repeatedly in different environments. We proved the theoretical feasibility of this method through theoretical derivation. We carried out extensive experiments in two classic real indoor environments. Experimental results show that reverse fingerprinting can be achieved. In 95% of cases, the positioning accuracy can be guaranteed to be less than 10 cm.

An accurate and easy deployment array gain-phase error calibration method for DoA estimation in Wi-Fi network

DoA estimation using channel state information (CSI) is an important technology for localization in Wi-Fi network. Existing direction of arrival (DoA) estimation algorithms such as multiple signal classification (MUSIC) algorithm may provide feasible DoA estimation accuracy, if the array gain-phase error can be well calibrated in commodity Wi-Fi devices. However, for multipath propagation environment, classical subspace-based calibration methods cannot be directly deployed, due to the coupling effect between gain-phase error parameters and DoA parameters. Therefore, most works deploy wired calibration methods with specialized devices, which also require professional and trivial deployments. This paper offers an iterative pairwise antenna gain-phase error calibration method, which is easy deployment and low cost. Furthermore, we combine spatial smoothing technique with pairwise antenna calibration schemes to guarantee calibration accuracy and parameters decoupling, respectively. Experimental results in realistic indoor environments to verify the effectiveness of the proposed calibration method.

Wireless Power Transfer With Distributed Antennas: System Design, Prototype, and Experiments

In this paper, we design and experiment a far-field wireless power transfer (WPT) architecture based on distributed antennas, so-called WPT DAS, that dynamically selects transmit antenna and frequency to increase the output dc power. Uniquely, spatial and frequency diversities are jointly exploited in the proposed WPT DAS with low complexity, low cost, and flexible deployment to combat the wireless fading channel. A numerical experiment is designed to show the benefits using antenna and frequency selections in spatially and frequency selective fading channels for single-user and multi-user cases. Accordingly, the proposed WPT DAS for single-user and two-user cases is prototyped. At the transmitter, we adopt antenna selection to exploit spatial diversity and adopt frequency selection to exploit frequency diversity. A low-complexity over-the-air limited feedback using an IEEE 802.15.4 RF interface is designed for antenna and frequency selections and reporting from the receiver to the transmitter. The proposed WPT DAS prototype is demonstrated in a real indoor environment. The measurements show that WPT DAS can boost the output dc power by up to 30 dB in single-user case and boost the sum of output dc power by up to 21.8 dB in two-user case and broaden the service coverage area in a low cost, low complexity, and flexible manner.

Possibilities of Using Kalman Filters in Indoor Localization

Kalman filters are a set of algorithms based on the idea of a filter described by Rudolf Emil Kalman in 1960. Kalman filters are used in various application domains, including localization, object tracking, and navigation. The text provides an overview and discussion of the possibilities of using Kalman filters in indoor localization. The problems of static localization and localization of dynamically moving objects are investigated, and corresponding stochastic models are created. Three algorithms for static localization and one algorithm for dynamic localization are described and demonstrated. All algorithms are implemented in the MATLAB software, and then their performance is tested on Bluetooth Low Energy data from a real indoor environment. The results show that by using Kalman filters, the mean localization error of two meters can be achieved, which is one meter less than in the case of using the standard fingerprinting technique. In general, the presented principles of Kalman filters are applicable in connection with various technologies and data of various nature.

Effects of Exogenous Putrescine on Delaying Senescence of Cut Foliage of Nephrolepis cordifolia.

Senescence is the main limitation for cut foliage display in vase. Naturally occurring polyamines such as putrescine (Put) have been considered effective anti-senescence agents. However, effect of Put on cut foliage in vase in a realistic indoor environment has not yet been revealed. In the present study, effects of Put spraying on the postharvest performance of cut foliage of Nephrolepis cordifolia L. were investigated. Cut fronds sprayed with deionized water (Put0) showed visible injuries after 10 days in vase. Meanwhile, chlorophyll (Chl), soluble protein (Sp), and proline (Pro) content were decreased by 60.15, 57.93, and 73.09% respectively, photochemical activity reflected by Chl fluorescence parameters was inhibited, whereas electrolyte leakage (EL), contents of soluble sugar (Ss), malondialdehyde (MDA), and hydrogen peroxide (H2O2) were increased (+194.29, +44.83, +34.06, and +178.01%, respectively). Put spraying extended the vase life of the cut foliage and the 2.0 mM Put had a longer vase life (21 days) than 0.2 mM (15 days). Leaf spraying of 2.0 mM Put for 10 days significantly ameliorated the losses of Chl, Sp, and Pro content (−10.72, −26.29, and −42.64%, respectively), followed by 0.2 mM Put (−27.36, −36.24, and −60.55%, respectively). Put spraying also improved photochemical capability and prevented membrane impairment as well as visible injury in comparison with Put0. In addition, 2.0 mM Put had a better mitigating ability than that of 0.2 mM. Leaf spraying of 2.0 mM Put greatly reduced the decline of the effective quantum yield of photochemical energy conversion in PSII (ΦPSII), the maximal quantum yield of PSII photochemistry measured in the dark-adapted state (Fv/Fm) and electron transport rate (ETR) (−7.89, −12.91, and −10.06%, respectively), and also inhibited the increases of EL, MDA, Ss, and H2O2 (+31.87, +6.43, +16.22, and +49.40%, respectively). Overall, Put played important roles in deterring the degradation of Chl, Ss, and Pro, detoxifying the H2O2, weakening the sugar signaling, mitigating the decline of photochemical activity, and eventually postponing the leaf senescence. The present study gives new insights into effects of Put on leaf senescence and provides a strategy for preserving post-harvest cut foliage.

A Review of Techniques for 3D Reconstruction of Indoor Environments

Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.

WiSign

In this article, we propose WiSign that recognizes the continuous sentences of American Sign Language (ASL) with existing WiFi infrastructure. Instead of identifying the individual ASL words from the manually segmented ASL sentence in existing works, WiSign can automatically segment the original channel state information (CSI) based on the power spectral density (PSD) segmentation method. WiSign constructs a five-layer Deep Belief Network (DBN) to automatically extract the features of isolated fragments, and then uses the Hidden Markov Model (HMM) with Gaussian mixture and Forward-Backward algorithm to recognize sign words. In order to further improve the accuracy, WiSign also integrates the language model N-gram, which uses the grammar rules of ASL to calibrate the recognized results of sign words. We implement a prototype of WiSign with commercial WiFi devices and evaluate its performance in real indoor environments. The results show that WiSign achieves satisfactory accuracy when recognizing ASL sentences that involve the movements of the head, arms, hands, and fingers.

Decentralized adaptive indoor positioning protocol using Bluetooth Low Energy

Previous indoor positioning research has mainly been focused on using Wi-Fi and RFID. In recent years, researchers began to study using Bluetooth 4.0 and Bluetooth Low Energy (BLE) for indoor positioning purposes. In general, positioning techniques based on received signal strength indicator (RSSI), such as signal propagation and fingerprint, are commonly used in wireless/mobile networks. These techniques have certain limitations and tradeoff in terms of accuracy, ease of implementation and practical application/deployment. For example, both methods require a training process before deployment. In this paper, we present a decentralized BLE-based positioning protocol that does not require training before deployment. The training process can automatically be done on the fly by the anchor nodes. While the anchor nodes are broadcasting, they also scan for signals emitted by other anchors. This collaborative communication process exchanges location information and signal strength measurements between each anchor. This process builds a signal-to-distance reference list for the target node to estimate physical distance in a more accurate way. Experimentation in a real indoor environment shows that the proposed collaborative positioning method can achieve an error of 1.5 meters on average. This is generally applicable for most indoor positioning applications for locating people. Furthermore, its implementation is simple and practical, because it does not require training before positioning estimation and is adaptive to environmental changes.

Semantic Localization System for Robots at Large Indoor Environments Based on Environmental Stimuli.

In this paper, we present a new procedure to solve the global localization of mobile robots called Environmental Stimulus Localization (ESL). We propose that the presence of common facts on the environment around the robot can be considered as stimuli for the procedure. The robust performance of our approach is supported by two concurrent particle filters. A primary particle filter estimates and tracks the robot position, while a secondary filter is fired by environmental stimuli, helps to reduce the influence of measurement errors and allows an earlier recovery from localization failures. We have successfully used this method in a 5000 m real indoor environment using as inputs the available environment information from a Geographical Information System (GIS) map, the robot’s odometry and the output of an algorithm for the perception of facts from the environment. We present a case study and the result of different tests, showing the performance of our method under the influence of errors in real applications.

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks.

The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.

Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.

Electromagnetic Field Propagation and Indoor Exclusion Zone Analysis in a Nuclear Power Plant

This article investigates the electromagnetic field strengths and the resulting indoor exclusion zones (EZs), taking into account the actual indoor nuclear power plant (NPP) environment. To obtain the accurate indoor EZ, a real indoor environment based on the simulator room of Korea Institute of Nuclear Safety was included in the numerical simulation. The maximum distance d of the indoor EZs depending on the position and polarization of the emitter is 1 to 2.1 times longer than the distances d of the free-space EZs. To validate the simulated indoor EZs, electromagnetic field strengths along the arc line with an arbitrary distance from the emitter are measured in the NPP, which are in good agreement with the simulation results. The result demonstrates that the indoor EZ should be investigated before applying the wireless system in the NPP because the indoor EZ and free space EZ can be significantly different.

A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems.

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.