Signals In Indoor Environments Research Articles

Research on the 3D indoor positioning technology based on feature light source

Nowadays, positioning technologies mainly consist of three methods: positioning based on satellites, positioning based on RF and positioning based on self-contained sensors. However, due to the complex propagation characteristics of wireless signals in indoor environments, the existing positioning technologies widely used in outdoor environments cannot meet the requirements of indoor applications. Compared to outdoor environments, indoor environments are more complex, and wireless signals are affected by various factors, making it difficult for existing indoor positioning systems to meet the requirements of high accuracy and low cost. Thus, a three-dimensional indoor positioning technology based on a characteristic light source and a spherical lighting device is proposed in this paper to solve the problems. The technology measures the angle of the optical signal through a spherical lighting device, which can receive light in all directions and obtain height information compared with flat mode. Pseudo-source interference caused by the wave nature of light will not be introduced with the use of the particle nature of light. Array layout and optimization algorithms can further improve positioning accuracy. A simulation experiment in Matlab is conducted. The experimental results and the analysis of accuracy confirmed that the positioning accuracy can be controlled within a small error. Three-dimensional indoor positioning technology can achieve high accuracy at a low cost.

A Python Tool to Predict Wireless Network Signals in Indoor Environments using Neural Networks

A Python Tool to Predict Wireless Network Signals in Indoor Environments using Neural Networks

Wavelet and Neural Network-Based Multipath Detection for Precise Positioning Systems

Multipath errors are significantly challenging in radio navigation systems. In particular, multipath errors in indoor environments cause significant errors in the position domain because not only the building materials that surround the environment but also all objects inside the building can reflect the navigation signals. Multipath errors in outdoor environments, such as in global navigation satellite system (GNSS) signal applications, have been widely studied for precise positioning. However, multipath studies for indoor applications have rarely been conducted because of the complicated environment and the many objects made of various materials in small areas. In this study, multipath mitigation methods using a shallow neural network and a transfer learning-based deep neural network were respectively considered to overcome the complexity caused by the reflected signals in indoor environments. These methods classify each measurement according to whether the measurement exhibits a severe multipath error. Carrier-phase measurements broadcasted from the transmitter were used for the wavelet transform, and the magnitude values after the transform were used for neural network-based learning. Shallow and deep networks attain approximately 87.1% and 85.6% detection accuracies, respectively, and the positioning error can be reduced by 10.4% and 9.4%, respectively, after multipath mitigation.

A Study on Propagation Models for 60 GHz Signals in Indoor Environments

The millimeter-waves band will enable multi-gigabit data transmission due to the large available bandwidth and it is a promising solution for the spectrum scarcity below 6 GHz in future generations of mobile networks. In particular, the 60 GHz band will play a crucial role in providing high-capacity data links for indoor applications. In this context, this tutorial presents a comprehensive review of indoor propagation models operating in the 60 GHz band, considering the main scenarios of interest. Propagation mechanisms such as reflection, diffraction, scattering, blockage, and material penetration, as well as large-scale path loss, are discussed in order to obtain a channel model for 60 GHz signals in indoor environments. Finally, comparisons were made using data obtained from a measurement campaign available in the literature in order to emphasize the importance of developing accurate channel models for future wireless communication systems operating in millimeter-waves bands.

DFF-EDR: An indoor fingerprint location technology using dynamic fusion features of channel state information and improved edit distance on real sequence

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for location-based services continues to increase. Channel state information (CSI) can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers. However, the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data. In this paper, the Dynamic Fusion Feature (DFF) is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system, which combines the pre-processed amplitude and phase data. Then, the improved edit distance on real sequence (IEDR) is used as a similarity metric for fingerprint matching. Based on the above studies, we propose a new indoor fingerprint positioning method, named DFF-EDR, for improving positioning performance. During the experimental stage, data were collected and analyzed in two typical indoor environments. The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures, has good anti-noise capability, and effectively reduces the localization errors.

Indoor Geomagnetic Positioning Using the Enhanced Genetic Algorithm-Based Extreme Learning Machine

Indoor positioning using the geomagnetic field has become a popular technique because of the infrastructure-free characteristic and the ubiquitous magnetic signals in indoor environments. Geomagnetic intensity will generate fluctuations due to the factors such as device heterogeneity, dates, and electronic facilities. Stable geomagnetic data are essential for high-precision positioning. To find a robust and low time-consuming geomagnetic positioning model (GPM), the extreme learning machine (ELM) optimized by the enhanced genetic algorithm (EGA) is constructed. The EGA with three optimization strategies is designed to find the best initial parameters solutions for the ELM. Magnetic patterns collected by different participants using different mobile phones on different dates are extracted characteristics and divided into segments for ELM training. Extensive experiments are conducted to evaluate the proposed model’s performance. The experimental results demonstrate that the EGA-based ELM model can achieve meter-level location accuracy and has good robustness under different testing conditions. The model construction is much faster than that of the popular learning algorithms, such as the convolutional neural networks (CNNs) and backpropagation (BP) networks.

Feature Selection for Real-Time NLOS Identification and Mitigation for Body-Mounted UWB Transceivers

Ultra-wideband (UWB) is a popular technology for indoor positioning systems (IPSs) due to its robust signaling in harsh environments, through-wall propagation, and high-resolution ranging. However, the propagation of wireless signals in indoor environments is affected by nonline-of-sight (NLOS) conditions, which can lead to positively biased distance estimates. Several research works were carried out in this area, but the NLOS effect caused by the human body shadowing on the ranging performance is not properly addressed in the literature. In this paper, a commercial UWB transceiver is used to assess the impact of the human body shadowing on the ranging accuracy. A set of real-time features is combined with different machine learning algorithms for NLOS identification and mitigation. With a subset of four features, a 0.97 F1-score was obtained for NLOS identification. For NLOS mitigation, with a subset of three features, the residual error follows a Gaussian distribution, has a mean error close to zero, and an STD of 0.67 m.

Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals

Current localization techniques in the outdoors cannot work well in indoors. The Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However, in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use the affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also, we assign lower weights to alter APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs.

Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization

Wi-Fi fingerprinting is widely used in indoor localization due to the ubiquitous availability of Wi-Fi infrastructure in indoor environments. The basic assumption of fingerprinting localization is that the received signal strength indicator (RSSI) distance is consistent with the location distance. However, due to the fluctuation of Wi-Fi signals in indoor environments, the nearest neighbors selected using the RSSI distance may not be those whose corresponding locations are nearest to the target, which could lead to a large localization error. In this paper, we propose a novel fingerprinting method for indoor localization by transforming raw RSSI into features with a learned non-linear mapping function. To learn such mapping function, we design a triple loss function that measures the difference between the rank of RSSI distance and that of location distance. By minimizing the loss function iteratively, we can learn the non-linear mapping function with the gradient boosting regression forest (GBRF) method. Experiments have been conducted in a complex environment and experimental results show that our method outperforms the state-of-the-art methods.

Sensor Radar for Object Tracking

Precise localization and tracking of moving objects is of great interest for a variety of emerging applications including the Internet-of-Things (IoT). The localization and tracking tasks are challenging in harsh wireless environments, such as indoor ones, especially when objects are not equipped with dedicated tags (noncollaborative). The problem of detecting, localizing, and tracking noncollaborative objects within a limited area has often been undertaken by exploiting a network of radio sensors, scanning the zone of interest through wideband radio signals to create a radio image of the objects. This paper presents a sensor network for radio imaging (sensor radar) along with all of the signal processing steps necessary to achieve highaccuracy objects tracking in harsh propagation environments. The described sensor radar is based on the impulse radio (IR) ultrawideband (UWB) technology, entailing the transmission of very short duration pulses. Experimental results with actual UWB signals in indoor environments confirm the sensor radar’s potential in IoT applications.

A glass based dual band frequency selective surface for protecting systems against WLAN signals

A polarization independent, double layer frequency selective surface on glass substrate is proposed that can transmit 900MHz and 1.8GHz GSM bands but inhibit transmission of wireless local area network (WLAN) bands, 2.4–2.5GHz and 4.98–5.825GHz. A parametric analysis is conducted to tailor the resonance frequencies at WLAN bands and the effect of each geometrical parameter on band stop response is investigated. Band stop responses of each FSS layer and the double layer final design are presented both numerically and experimentally. The simulation results showed a good agreement with the experimental results and supported the polarization independent behavior. In particular, very wide rejection bands are obtained (−10dB bandwidths more than 1.75GHz) for the 5GHz band, both numerically and experimentally, for vertical and horizontal polarizations, under normal incidence. The design can be used in double layer window glasses for protecting systems against interference with WLAN signals in indoor environments and buildings.

Reduction of wireless signals in indoor environments by using an active frequency selective wall based on spectrum sensing

SummaryThis paper presents a wireless local area network (WLAN) spectrum control system that uses active frequency selective shielding walls to selectively reduce or pass WLAN signals in the 5 GHz band indoors. The proposed system utilizes a well‐known active frequency selective structure based on a rectangular loop with PIN diodes and is thus capable of either reducing or passing the WLAN frequency by using the on/off switching of the PIN diode based on the measured electric field strength in indoor. We designed and simulated the proposed active frequency selective shielding wallpaper by using commercial electromagnetic simulation software and confirmed that the proposed structure can reduce the WLAN signal by switching the PIN diode by applying the manufactured prototype on the exterior of an experimental apparatus containing a WLAN receiver antenna that is capable of measuring the received electric field strength. Extension of the results presented herein can be applied to reduce the wireless signal to enhance the spectrum efficiency of an indoor space.

SDHT for Fast Detection of Weak GNSS Signals

Successful and fast Global Navigation Satellite System (GNSS) positioning in indoor environments can enable many location based services (LBS). However, fast indoor GNSS positioning has been one of the biggest challenges for GNSS receivers due to the huge computational cost. To detect weak GNSS signals in indoor environments, a GNSS receiver should perform numerous correlations with a longer coherent integration interval for a denser Doppler frequency search, which is computationally too expensive. For a fast and low computational weak GNSS signal detection, we propose the synthesized Doppler frequency hypothesis testing (SDHT) technique that, utilizing the test results of only sparse Doppler frequency hypotheses, can estimate the test results of entire Doppler frequency hypotheses with small computations. We provide theoretical performance analysis of the proposed technique and demonstrate that the proposed technique reduces the computational cost for weak GNSS signal acquisition significantly and achieves faster signal acquisition than conventional techniques.

A survey on mobile anchor assisted localization techniques in wireless sensor networks

Identifying locations of sensor nodes in wireless sensor networks (WSNs) is significant for both network operations and most application level tasks. Although, geographical positioning system (GPS) based localization schemes are used for determining node locations but the cost of GPS devices and non-availability of GPS signals in indoor environments prevent their use in large scale WSNs. A substantial amount of research work exist that intend at obtaining precise and relative spatial locations of sensor nodes without requiring large amount of specialized hardware. Mobile anchor assisted localization is one typical approach that significantly reduces the implementation cost by using limited number of mobile anchors. In this survey, we present key issues and inherent challenges faced by the mobile anchor assisted localization techniques in WSNs. We take a closer look at the algorithmic approaches of various important fine-grained mobile anchor assisted localization techniques applicable in WSNs. In addition, we highlight the error refinement mechanisms adopted by the state-of-the-art works associated with their approaches. Well known mobile anchor trajectories presented in existing works are also reviewed. Finally, open research issues are discussed for future research scope in this field.

A New Narrow Band Frequency Selective Surface Geometry Design at the Unlicensed 2.4‐GHz ISM Band

ABSTRACTWireless devices in the unlicensed instruments, scientific, and measurements (ISM) bands cause mutual interference in indoor environments. This interference degrades the system performance and compromises security. Transforming existing building walls to a band stop frequency selective surface (FSS) can be an efficient solution for such problems. This work is proposed to design FSS for interference mitigation and network security within the buildings in the unlicensed 2.4‐GHz ISM band and to provide maximum transparency at broadcast frequencies. A 2.4‐GHz ISM band is a relatively narrow band which cannot be met by a conventional FSS for a wide range of incidence angle. Whereas wireless signals in indoor environment have a wide range of incidence angle. To obtain the desired narrow band response, a new FSSs element geometry is introduced which has a stable frequency response for TE polarization when the angle of incident wave is varied from 0° to 70°. HFSS software is used for simulation and design purposes. The obtained simulation and measurement results show that desired frequency response is achieved for TE polarization successfully. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2986–2990, 2013

Optimizing artificial neural network-based indoor positioning system using genetic algorithm

The Global Positioning System (GPS) is expected to play an integral role in the development of digital earth; however, the GPS cannot provide positioning information in regions where a majority of the population spends their time, that is, in urban and indoor environments. Hence, alternate positioning systems that work in indoor and urban environments should be developed to achieve the vision of digital earth. Wi-Fi-based positioning systems (WPS) stand out because of the near-ubiquitous presence of the associated infrastructure and signals in indoor environments. The WPS-based fingerprinting is the most widely adopted technique for position determination, but its accuracy is lower than that of techniques such as time of arrival and angle of arrival. Improving the accuracy is still a challenging task because of the complex nature of the propagation of Wi-Fi signals. Here, a novel server-based, genetic-algorithm-optimized, cascading artificial neural network-based positioning model is presented. The model is tested in 2D and 3D indoor environments under varying conditions. The model is thoroughly investigated on a real Wi-Fi network, and its accuracy is found to be better than that of other well-known techniques. A mean accuracy of 1.9 m is achieved with 87% of the distance error within the range of 0–3 m.

A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.

실내 음향신호 경로감쇠 모형 및 분석

본 논문에서는 경로감쇠에 기반한 실내 음향측위 시스템에 활용할 수 있는 실내 음향신호의 경로 감쇠 특성을 분석한다. 실내 음향시스템은 주로 25 평방미터 이내의 협소한 공간에서 적용되기 때문에 기존에 정의된 넓은 공간에서의 모형만으로는 경로감쇠에 기반한 실내음향측위 시스템을 설계할 수 없다. 따라서 본 논문에서는 자유음장 모형과 더불어 실내공간의 온도와 습도, 그리고 대기의 주요구성성분의 홉음영향을 고려한 실내 음향신호 경로 감쇠 모형을 보이고, 무향실에서 실제 실험을 통해 모형의 적절성을 평가한다. 실험을 위해서 1kHz-20kHz의 단일 주파수를 갖는 감쇠정현파를 사용한다. 그리고 실험 음원으로 두개 이상의 단일 주파수를 갖는 감쇠정현파를 함게 사용할 대의 경로감쇠를 보이고, 이를 분석한다. Start This paper analyzes the propagation characteristics of acoustic signals in indoor environments, which is applicable to indoor positioning system. Indoor stereo sound system is generally valid within <TEX>$25m^2$</TEX>. So it is not possible to apply prevalent sound propagation characteristic to indoor positioning system because the prevalent acoustic signals propagation characteristic is defined under free space condition. Therefore, in this paper, we present the propagation characteristics of acoustic signals in indoor environments considering the free space propagation characteristic as well as room characteristic such as humidity, temperature, absorption of atmosphere and so on. To verify the designed propagation model of indoor acoustic signals, this paper presents the propagation characteristics of decreasing sinusoidal signals whose frequencies ate from 1kHz to 20kHz in anechoic room. In addition, this paper also presents the propagation characteristics of decreasing sinusoidal signals which have multiple frequencies.

1P10-4 Target Detection Using Multiple M-sequence Signals in Indoor Environments(Poster Session)

1P10-4 Target Detection Using Multiple M-sequence Signals in Indoor Environments(Poster Session)

Path-loss and time dispersion parameters for indoor UWB propagation

The propagation of ultra wideband (UWB) signals in indoor environments is an important issue with significant impacts on the future direction and scope of the UWB technology and its applications. The objective of this work is to obtain a better assessment of the potentials of UWB indoor communications by characterizing the UWB indoor communication channels. Channel characterization refers to extracting the channel parameters from measured data. An indoor UWB measurement campaign is undertaken. Time-domain indoor propagation measurements using pulses with less than 100 ps width are carried out. Typical indoor scenarios, including line-of-sight (LOS), non-line-of-sight (NLOS), room-to-room, within-the-room, and hallways, are considered. Results for indoor propagation measurements are presented for local power delay profiles (local PDP) and small-scale averaged power delay profiles (SSA-PDP). Site-specific trends and general observations are discussed. The results for path-loss exponent and time dispersion parameters are presented.