Complex Indoor Environments Research Articles

Accurate DOA Estimation With Adjacent Angle Power Difference for Indoor Localization

Indoor localization with high accuracy plays a key role in the field of Internet of Things in 5th-Generation (5G) era. With the introduction of Multiple Input Multiple Output (MIMO) technology in 5G, the direction-of-arrival (DOA) method is highly feasible in indoor localization. However, the direction of arrival is susceptible to complex indoor environment. To improve the accuracy and stability of DOA estimation, an adjacent angle power difference (AAPD) method is proposed based on Orthogonal Matching Pursuit (OMP). This method uses OMP to obtain an initial estimate of the direction, and then, adjusts the estimation by calculating the difference power of adjacent points at initial value point to get the fractional DOA. In the scenario of continuous movement, beamforming is further applied, which reduces the amount of calculation. Both simulation and experimental results show that the proposed method can achieve high accuracy and eliminate the error jitter. Compared with the classical Multiple Signal Classification (MUSIC) method for DOA estimation, the proposed method can increase accuracy by 46% under the condition of low SNR (Signal-to-Noise Ratio). The probability that the measurement error does not exceed 5° in the actual movement tests is 97.5%.

DL-RNN: An Accurate Indoor Localization Method via Double RNNs

Wireless fingerprinting localization method learns a mapping function from a fingerprint measurement to the estimated location, which is more suitable for complex indoor environments than the propagation model-based methods. However, most traditional methods only consider the location matching at single time or space points, but ignore the fact that correlations in the measurement sequences may improve the accuracy and robustness of mobile positioning. Recently, recurrent neural network (RNN) has been readily applied in many fields such as speech, language, and video processing, and it has flexible and powerful processing capabilities for nonlinear and high-dimensional sequence input. In this paper, we propose DL-RNN, a real-time wireless localization model, which consists of double RNNs: the first RNN estimates the location based on the historical observation signal, and the second RNN filters the location based on the historical estimated location, which further improves the localization performance. Both the simulation and measured experiments show the high accuracy and robustness of the proposed algorithm.

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning.

This work presents an integrated Indoor Positioning System which makes use of WiFi signals and RGB cameras, such as surveillance cameras, to track and identify people navigating in complex indoor environments. Previous works have often been based on WiFi, but accuracy is limited. Other works use computer vision, but the problem of identifying concrete persons relies on such techniques as face recognition, which are not useful if there are many unknown people, or where the robustness decreases when individuals are seen from different points of view. The solution presented in this paper is based on an accurate combination of smartphones along with RGB cameras, such as those used in surveillance infrastructures. WiFi signals from smartphones allow the persons present in the environment to be identified uniquely, while the data coming from the cameras allow the precision of location to be improved. The system is nonintrusive, and biometric data about subjects is not required. In this paper, the proposed method is fully described and experiments performed to test the system are detailed along with the results obtained.

Hybrid Computational Techniques: Electromagnetic Propagation Analysis in Complex Indoor Environments

In this article, we compare deterministic methodologies for characterizing channel behavior in heterogeneous and composite scenarios. These techniques include one that combines a 3D ray launching (RL) approach based on geometrical optics (GO), a second based on GO and the uniform theory of diffraction (UTD), and another that includes a diffusion equation (DE) method based on the equation of transfer. A new methodology based on the GO and DE is presented and shown to achieve accurate results when compared with real measurements. The proposed technique provides a computational time reduction of up to 90% compared to the conventional approach using GO with the UTD and DE.

Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

Improved Distance Metrics for Histogram-Based Device-Free Localization

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. The existing HD-DFL methods use either Kullback–Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.

Mobile localization in complex indoor environment based on ZigBee wireless network

An algorithm for mobile robot localization in complex indoor environment is introduced in this paper. The robot is designed as a special node of ZigBee network, so the robot localization is transformed into the location of ZigBee mobile node. For reducing error, the localization method adopts and improves the RSSI ranging technology. Further, the distance is corrected by the least square method, and the three sides’ measurement is used to calculate the coordinates of the robot. The experiments were carried out in two environments: narrow and complex laboratory, and spacious hall. The results show that the system can realize the function of data acquisition, transmission, processing and display. It can satisfy the localization requirement in indoor environment. Compared with the traditional algorithm, the maximum positioning error is reduced.

Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.

Novel Fast User-Placement Ushering Algorithms and Performance Analysis for LTE Femtocell Networks

Nowadays, the sufficient quality-of-service (QoS) provision for multimedia mobile applications remains a major challenge to any wireless network. Conventional sufficient QoS provision techniques using resource allocation, data scheduling, and cross-layer optimization have been proposed to tackle this problem. Nevertheless, due to the unpredictable nature of wireless channel conditions, the QoS improvements resulting from the aforementioned approaches are often unsatisfactory. In this paper, we would like to further exploit our previous concept, namely user-placement ushering (UPU). A mobile user can be relocated to an optimal, or at least a better place to boost the QoS for a given application. The QoS is measured by the packet-success rate (PSR), which can be further computed from the code-block error-rate (CBER). Novel fast algorithms are devised here to usher (guide) a mobile user to an appropriate spot with sufficient QoS, which is nearby. We address the UPU problem to accommodate the realistic complex indoor environments, where obstacles are present. Our simulation results have demonstrated that our new fast UPU algorithms are capable of finding new appropriate locations satisfying the required QoSs for different wireless network applications.The preliminary results are presented in [1] .

Efficient Indoor Localization Based on Geomagnetism

Geomagnetism is promising for indoor localization due to its omnipresence, high stability, and availability of magnetometers in smartphones. Previous works often fuse it with pedometer via particles, which are computationally intensive and make strong user behavior assumptions. To overcome that, we propose Magil, an approach leveraging geo mag netism for i ndoor l ocalization. To our best knowledge, this is the first piece of work using geomagnetism for smartphone localization without the need of pedometer or user walking model. Magil is applicable to any open or complex indoor environment. In the offline phase, Magil collects and stores geomagnetic fingerprints while surveyors walk indoors. In the online phase, it employs a fast algorithm to match the geomagnetic variation of the target with the stored fingerprints. Given closely matched segments, Magil constructs user trajectory efficiently with a modified shortest path formulation by selecting and connecting these matched segments. To further improve accuracy and deployability, we propose MagFi, which extends Mag il by fusing it with Wi- Fi . MagFi further collects opportunistic Wi-Fi RSSI for fingerprint construction. We have implemented both Magil and MagFi and conducted extensive experiments in our campus. Results show that our schemes outperform state-of-the-art schemes by a wide margin (often cutting localization error by 30%).

Fast floor identification method based on confidence interval of Wi-Fi signals

The indoor positioning technology is based on the hotpots of location based services (LBS). However, most indoor positioning systems are two-dimensional and couldn’t meet the requirements of today’s LBS. The complex indoor structures and environment determine the floor positioning rather than the altitude positioning in the vertical direction, so the floor identification is the key to three-dimensional indoor positioning systems. There are many restrictions for the existing floor identification methods based on barometer or inertial sensor. They need to get the comparable data in advance, or detect the test data changes in a certain period of time for accurate identification. The current floor identification methods based on ordinary Wi-Fi fingerprints are less effective in the complex environment. Therefore, a new floor identification method based on confidence interval of Wi-Fi signals was developed in this paper, which was divided into the offline stage and the online stage. In the offline stage, the dynamic Wi-Fi signal sequences were collected fast. Then, the adaptive partitioning of Wi-Fi signal intervals was carried out according to RSSI distribution characteristics in the multi-floor environment. Finally, the confidence levels were calculated and the database of fingerprints was constructed. In the online stage, the matching between the test fingerprints and those in the database was applied to obtain the confidence of APs on each floor monitored by the test fingerprints. The sums of the confidence of APs on each floor were calculated, and the floor corresponding to the maximum value was judged as the target floor. To verify the performance of the proposed method, it was compared with the majority voting committees, K-means, Naive Bayes and KNN methods. The results indicate that it was better than other methods in large complex indoor scenes. Its identification accuracy rate was 92.2% and the error rate was 7.8% only one floor away. Moreover, it also could significantly reduce the size of the fingerprint database and further improve the efficiency of algorithm.

Performance Evaluation and Interference Characterization of Wireless Sensor Networks for Complex High-Node Density Scenarios.

The uncontainable future development of smart regions, as a set of smart cities’ networks assembled, is directly associated with a growing demand of full interactive and connected ubiquitous smart environments. To achieve this global connection goal, large numbers of transceivers and multiple wireless systems will be involved to provide user services and applications anytime and anyplace, regardless the devices, networks, or systems they use. Adequate, efficient and effective radio wave propagation tools, methodologies, and analyses in complex indoor and outdoor environments are crucially required to prevent communication limitations such as coverage, capacity, speed, or channel interferences due to high-node density or channel restrictions. In this work, radio wave propagation characterization in an urban indoor and outdoor wireless sensor network environment has been assessed, at ISM 2.4 GHz and 5 GHz frequency bands. The selected scenario is an auditorium placed in an open free city area surrounded by inhomogeneous vegetation. User density within the scenario, in terms of inherent transceivers density, poses challenges in overall system operation, given by multiple node operation which increases overall interference levels. By means of an in-house developed 3D ray launching (3D-RL) algorithm with hybrid code operation, the impact of variable density wireless sensor network operation is presented, providing coverage/capacity estimations, interference estimation, device level performance and precise characterization of multipath propagation components in terms of received power levels and time domain characteristics. This analysis and the proposed simulation methodology, can lead in an adequate interference characterization extensible to a wide range of scenarios, considering conventional transceivers as well as wearables, which provide suitable information for the overall network performance in crowded indoor and outdoor complex heterogeneous environments.

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Three-dimensional (3-D) modeling of indoor environment plays an important role in various applications such as indoor navigation, Building Information Modeling (BIM), interactive visualization, etc. While automated reconstruction of 3-D models from point clouds is receiving more and more attention. Indoor modeling remains a challenging task in terms of dealing with the complexity of indoor environment, the level of automation and restrictions of input data. To address these issues, an automatic indoor reconstruction method that quickly and effectively reconstructs indoor environment of multi-floors and multi-rooms using both point clouds and trajectories from mobile laser scanning (MLS) is proposed. The proposed automatic method of parametric structure modeling comprises three steps. First, structural elements, such as doors, windows, walls, floors, and ceilings, are extracted based on the geometric and semantic features of point clouds. Then, the point clouds are automatically segmented into adjoining rooms through a combination of visibility analysis and physical constraints of the structural elements, which ensures the integrity of the room-space partitions and yields priors for the definition of point cloud label for reconstructed model. Finally, 3-D models of individual rooms are constructed by solving an energy optimization function via multi-label graph cuts. Three benchmark datasets collected by two handheld laser scanning (HLS) and a backpack laser scanning (BLS) system were used to evaluate the proposed method. Experiments demonstrate that the recall and precision of reconstructed surface models obtained by the proposed method are mostly larger than 60%, and the average F1-score of the model is close to 5 cm.

A Modified KNN Indoor WiFi Localization Method With K-median Cluster

Because of the serious attenuation and multi-path effect of GPS signal, outdoor-positioning technology can not be applied in complex indoor environment. Through the study of K-Nearest Neighbor applied in WiFi positioning, according to the problem that the time complexity of KNN algorithm increases linearly with the quantity of samples, this paper combined clustering algorithm with KNN optimized the similarity measure in fingerprint feature space and proposed a efficient indoor target location algorithm . Experimental results showed that the algorithm improved the positioning accuracy, had strong robustness to noise and more importantly, the positioning time was effectively shortened and it can meet the requirements of practical applications.

Evaluation of the complexity of indoor air in hospital wards based on PM2.5, real-time PCR, adenosine triphosphate bioluminescence assay, microbial culture and mass spectrometry

BackgroundThe aim of this study was to establish a set of assessment methods suitable for evaluating the complex indoor environment of hospital wards and to ascertain the composition of bacteria and microbial ecology of hospital wards.MethodsColony-forming units (CFUs), PM2.5 detection, real-time PCR, and adenosine triphosphate (ATP) bioluminescence assay were employed to evaluate the complexity of indoor air in 18 wards of nine departments in a hospital and two student dormitories in a university. Subsequently, the microbial samples were quantified and identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).ResultsAlthough the studied indices were relatively independent, the PM2.5 content was correlated with bacterial CFUs determined by passive sedimentation method, bacterial and fungal counts measured by real-time PCR, and ATP bioluminescence assay. The composition of microorganisms in the air of hospital wards differed from that in the air of student dormitories. The dominant genera in hospital wards were Staphylococcus (39.4%), Micrococcus (21.9%), Corynebacterium (11.7%), Kocuria (4.4%), Bacillus (2.9%), Streptococcus (1.6%), Moraxella (1.6%), and Enterococcus (1.3%), and the microbial ecology differed between Respiration Dept. III and other hospital departments. Additionally, 11.1 and 27.3% of bacteria in hospital wards and student dormitories were not identified, respectively.ConclusionsAssessment of environmental quality of hospital wards should be based on comprehensive analysis with multiple indicators. There may be imbalances in the microbial diversity in the hospital wards, therefore, monitoring of the environmental quality of hospitals is important in the prevention of nosocomial infections.

Convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter based indoor localization with Wi-Fi

Various research works have been proposed for Wi-Fi-based indoor localization, including Received Signal Strength Indicator (RSSI)-based fingerprint algorithm, Angle of Arrival (AoA)-based algorithm and so on. However, since RSSI value cannot accurately express the spatial features of emitted wireless signal, and the interfering noise in indoor environment makes the wireless signal distortion, RSSI-based localization algorithm cannot achieve an ideal accuracy. In this paper, we utilize Channel State Information (CSI) extracted from MIMO-OFDM PHY layer as fingerprint image to express the spatial and temporal features of Wi-Fi signal. At the same time, an indoor localization algorithm is also proposed, which is based on convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter, to achieve accurate position estimate with time-varying measurement noise and process noise in complex indoor environment. According to the simulation results, compared with existing methods, our proposed algorithm improves the positioning accuracy up to 51.8%. In the real indoor environment, our proposed algorithm improves the positioning accuracy up to 22% in LoS scenario, and 9.8% in NLoS scenario, respectively.

Multilevel Humanlike Motion Planning for Mobile Robots in Complex Indoor Environments

In this paper, a multilevel humanlike motion planning approach is proposed for indoor mobile robots. Compared with existing approaches, the novelty of this paper is twofold: 1) the proposed path planning framework is multilevel and humanlike to ensure both foreseeability and flexibility , wherein functions of human brain, eyes, and legs are corresponding to global path planning, sensor-level path planning, and action-level path planning, respectively, and 2) along the planned path, a new velocity-adjustable trajectory planning algorithm is put forward which is provably complete and time optimal considering multiple constraints from both the robot and the environment. Experimental results show that the proposed approach has a better performance in terms of efficiency, smoothness, foreseeability, and flexibility, and autonomous navigation is realized in large-scale, dynamic, partially unknown, and unstructured indoor environments. Note to Practitioners —This paper was motivated by the motion planning problem for mobile robots in complex indoor environments. Existing approaches generally implement a two-level planning scheme, namely, global path planning and local trajectory planning. This paper proposes a novel multilevel humanlike motion planning approach, which is able to guarantee efficiency, smoothness, foreseeability, and flexibility in the presence of kinematic and environmental constraints. Experimental results show that autonomous navigation with the proposed approach is realized in large-scale, dynamic, partially unknown, and unstructured indoor environments, but human motion prediction has not yet been incorporated. In the future research, we will concentrate on human–robot interaction and environment-adaptive planning.

A BOOK RETRIEVAL AND LOCATION SYSTEM BASED ON REAL-SCENE 3D

Abstract. With the development of urbanization, the building structures are more and more complex, and various moving objects, such as human beings, robots and unmanned aerial vehicles, often travel through indoor and outdoor 3D space, which puts forward higher requirements for the accurate search and location in indoor and outdoor space. At present, most of the spatial location methods for indoor entities are carried out through 2D maps. However, the indoor environment is a complex 3D space, which increases the difficulty of the search process. In addition, 2D map cannot accurately display the 3D spatial position of the entity. Therefore, it is difficult for 2D maps to search and locate in complex environment. Therefore, how to quickly and effectively carry out spatial location query in complex indoor environment has become an urgent problem to be solved. Taking the library of Beijing University of Civil Engineering and Architecture as an example, this paper obtains the indoor 3D information of the library based on SLAM, processes and publishes the acquired 3D information on IndoorViewer, and uses its API in the book retrieval system. Finally, a book retrieval and location system based on real-scene 3D is finished.