사물인터넷을 위한 무선 실내 측위 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 김성철.실내 위치 기반 서비스는 스마트폰을 이용한 실내에서의 경로안내, 스마트 공장에서의 자원 관리, 실내 로봇의 자율주행 등 많은 분야에 접목될 수 있으며, 사물인터넷 응용에도 필수적인 기술이다. 다양한 위치 기반 서비스를 구현하기 위해서는 정확한 위치 정보가 필요하며, 적합한 거리 및 위치를 추정 기술이 핵심적이다. 야외에서는 위성항법시스템을 이용해서 위치 정보를 획득할 수 있다. 본 학위논문에서는 와이파이 기반 측위 기술에 대해 다룬다. 구체적으로, 전파의 신호 세기 및 도달 시간을 이용한 정밀한 실내 위치 추정을 위한 세 가지 기술에 대해 다룬다. 먼저, 비가시경로 환경에서의 거리 추정 정확도를 향상시켜 거리 기반 측위의 정확도를 향상시키는 하이브리드 알고리즘을 제안한다. 제안하 알고리즘은듀얼 밴드 대역의 신호세기를 감쇄량을 측정하여 거리 기반 측위 기법을 적용할 때, 거리 추정부 단계만을 데이터 기반 학습을 이용한 깊은 신경망 회귀 모델로 대체한 방안이다. 적절히 학습된 깊은 회귀 모델의 사용으로 비가시경로 환경에서 발생하는 거리 추정 오차를 효과적으로 감소시킬 수 있으며, 결과적으로 위치 추정 오차 또한 감소시켰다. 제안한 방법을 실내 광선추적 기반 모의실험으로 평가했을 때, 기존 기법들에 비해서 위치 추정 오차를 중간값을 기준으로 22.3% 이상 줄일 수 있음을 검증했다. 추가적으로, 제안한 방법은 실내에서의 AP 위치변화 등에 강인함을 확인했다. 다음으로, 본 논문에서는 비가시경로에서 단일 대역 수신신호세기를 측정했을 때 비가시경로가 많은 실내 환경에서 위치 추정 정확도를 높이기 위한 방안을 제안한다. 단일 대역 수신신호세기를 이용하는 방안은 기존에 이용되는 와이파이, 블루투스, 직비 등의 기반시설에 쉽게 적용될 수 있기 때문에 널리 이용된다. 하지만 신호 세기의 단일 경로손실 모델을 이용한 거리 추정은 상당한 오차를 지녀서 위치 추정 정확도를 감소시킨다. 이러한 문제의 원인은 단일 경로손실 모델로는 실내에서의 복잡한 전파 채널 특성을 반영하기 어렵기 때문이다. 본 연구에서는 실내 위치 추정을 위한 목적으로, 중첩된 다중 상태 경로 감쇄 모델을 새롭게 제시한다. 제안한 모델은 가시경로 및 비가시경로에서의 채널 특성을 고려하여 잠재적인 후보 상태들을 지닌다. 한 순간의 수신 신호 세기 측정치에 대해 각 기준 기지국별로 최적의 경로손실 모델 상태를 결정하는 효율적인 방안을 제시한다. 이를 위해 기지국별 경로손실모델 상태의 조합에 따른 측위 결과를 평가할 지표로서 비용함수를 정의하였다. 각 기지국별 최적의 채널 모델을 찾는데 필요한 계산 복잡도는 기지국 수의 증가에 따라 기하급수적으로 증가하는데, 유전 알고리즘을 이용한 탐색을 적용하여 계산량을 억제하였다. 실내 광선추적 모의실험을 통한 검증과 실측 결과를 이용한 검증을 진행하였으며, 제안한 방안은 실제 실내 환경에서 기존의 기법들에 비해 위치 추정 오차를 약 31% 감소시켰으며 평균적으로 1.92 m 수준의 정확도를 달성함을 확인했다. 마지막으로 FTM 프로토콜을 이용한 실내 위치 추적 알고리즘에 대해 연구하였다. 스마트폰의 내장 관성 센서와 와이파이 통신에서 제공하는 FTM 프로토콜을 통한 거리 추정을 이용하여 실내에서 사용자의 위치를 추적할 수 있다. 하지만 실내의 복잡한 다중경로 환경으로 인한 피크 검출 실패는 거리 측정치에 편향성을 유발한다. 또한 사용하는 디바이스의 종류에 따라 예상치 못한 거리 오차가 발생할 수있다. 본 논문에서는 실제 환경에서 FTM 거리 추정을 이용할 때 발생할 수 있는 오차들을 고려하고 이를 보상하는 방안에 대해 제시한다. 확장 칼만 필터와 결합하여 FTM 결과를 사전필터링 하여 이상값을 제거하고, 거리 측정치의 편향성을 제거하여 위치 추적 정확도를 향상시킨다. 실내에서의 실험 결과 제안한 알고리즘은 거치 측정치의 편향성을 약 44-65% 감소시켰으며 최종적으로 사용자의 위치를 서브미터급으로 추적할 수 있음을 검증했다.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions. In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics. First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error. Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario. Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation. By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i Contents iv List of Tables vi List of Figures vii 1 INTRODUCTION 1 2 Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-BandWi-Fi 6 2.1 Motivation 6 2.2 Preliminary 8 2.3 System model 11 2.4 Proposed Ranging Method 13 2.5 Performance Evaluation 16 2.5.1 Ray-Tracing-Based Simulation 16 2.5.2 Analysis of the Ranging Accuracy 21 2.5.3 Analysis of the Neural Network Structure 25 2.5.4 Analysis of Positioning Accuracy 26 2.6 Summary 29 3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based Indoor Localization 31 3.1 Motivation 31 3.2 Preliminary 34 3.2.1 RSS-based Ranging Techniques 35 3.2.2 Positioning Technique 37 3.3 Proposed localization method 38 3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss Model 38 3.3.2 Localization with Genetic Algorithm-Based Search 41 3.4 Performance evaluation 46 3.4.1 Numerical simulation 50 3.4.2 Experimental results 56 3.5 Summary 60 4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol 62 4.1 Motivation 62 4.2 Preliminary 63 4.2.1 FTM ranging 63 4.2.2 PDR-based trajectory estimation 65 4.3 EKF design for adaptive compensation of ranging bias 66 4.4 Performance evaluation 69 4.4.1 Experimental scenario 69 4.4.2 Experimental results 70 4.5 Summary 75 5 Conclusion 76 Abstract (In Korean) 89박

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas

Tools for ray tracing based radio channel modeling and simulation

Abstract. Ray tracing-based methods have become the state of the art for radio channel propagation modeling simulations. They provide a way to deterministically simulate field strength and multidispersive characteristics of the radio channel, and thus, offer a faster and easier alternative to measuring. Ray tracing is also an important tool for validating algorithms, and many applications can utilize the simulation results. As the wireless networks suffer from increasing complexity, the interest in machine learning and artificial intelligence solutions is increasing as well, and in this context the simulation results can be utilized as training data. We introduce the relevant theory in radio propagation modeling in the context of ray tracing, followed by theory of graphics processing unit-based computing, architecture, and ray tracing. We present multiple existing graphics processing unit and ray tracing-based radio channel propagation modeling implementations from the literature. We then develop multiple optimized versions of an existing environment discretization-based path search implementation and develop a path refiner for refining the coarse paths generated by the path search. The path refiner computes the optimal paths, and then validates them by utilizing ray tracing. Experiments for the developed solutions are conducted with an indoor and an outdoor model on two different computer setups. We achieve on average over 25 times faster computation in the outdoor scene and over 4 times faster computation in the indoor scene when compared to the original path search implementation. The path refiner is able to find the optimal paths fulfilling the Fermat’s principle of least time on average for over 96% of the coarse paths in the outdoor scene, and for over 99% in the indoor scene. From these refined paths, on average about 62% pass the validation phase in the outdoor case, and around 30% in the indoor case. The results show that the path refinement combined with validation is essential for improving the quality of the paths found by the initial discretization-based search.Työkaluja säteenseurantaan perustuvaan radiokanavamallinnukseen ja simulointiin. Tiivistelmä. Säteenseurantaan perustuvat menetelmät ovat edistyneintä tekniikkaa radiokanavien etenemisen mallinnussimulaatioissa. Ne tarjoavat tavan deterministisesti arvioida radiokanavan kentänvoimakkuutta ja monidispersiivisiä ominaisuuksia ja siten tarjoavat nopeamman ja helpomman vaihtoehdon mittaamiselle. Säteenseuranta on myös tärkeä työkalu algoritmien validoinnissa ja useissa sovelluksissa voidaan hyödyntää simulointien tuloksia. Langattomien verkkojen monimutkaisuuden lisääntyessä myös kiinnostus koneoppimis- ja tekoälypohjaisiin ratkaisuihin lisääntyy, ja tässä yhteydessä simulointien tuloksia voidaan hyödyntää opetusdatana. Tässä työssä esitellään teoriaa radiokanavan etenemisen mallinnuksesta säteenseurantaan perustuen, jonka jälkeen esitellään näytönohjainpohjaisen laskennan, arkkitehtuurin, sekä säteenseurannan teoriaa. Tämän jälkeen tarkastellaan useita olemassa olevia näytönohjain- ja säteenseurantapohjaisia radiokanavan etenemistä mallintavia toteutuksia. Työssä kehitetään useita optimoituja versioita olemassa olevasta ympäristön diskretisointiin perustuvasta polunetsintätoteutuksesta ja kehitetään poluntarkentaja tarkentamaan sen tuottamia epäoptimaalisia polkuja. Poluntarkentaja laskee optimaaliset polut ja validoi ne hyödyntämällä säteenseurantaa. Ratkaisuiden tehokkuutta arvioidaan sekä ulko- että sisätilan malleille tehtävillä laskennoilla kahdella eri tietokoneella. Paras polunetsintäversio saavuttaa keskimäärin yli 25 kertaa nopeamman laskennan ulkotilassa ja yli 4 kertaa nopeamman laskennan sisätilassa verrattaessa alkuperäiseen toteutukseen. Poluntarkentaja löytää optimaaliset polut, jotka täydentävät Fermat’n periaatteen lyhyimmästä ajasta keskimäärin yli 96 prosentille karkeista poluista ulkotilassa ja yli 99 prosentille sisätilassa. Näistä tarkennetuista poluista keskimäärin noin 62 prosenttia pääsee läpi validoinnista ulkotilassa ja noin 30 prosenttia sisätilassa. Tulokset osoittavat, että polkujen tarkennus ja validointi ovat tärkeitä alkuperäisen diskretisointipohjaisen haun löytämien polkujen laadun parantamiseksi

Accurate and efficient full-wave modelling for indoor radio wave propagation

The transition towards next-generation communication technologies has increased the need for accurate knowledge about the wireless channel. Knowledge of radio wave propagation is vital to the continued development of efficient wireless communications systems capable of providing a high data throughput and reliable connection. Thus, there is an increased need for accurate propagation models that can rapidly predict and describe the propagation channel. This is extremely challenging for indoor environments given the large variety of materials encountered and very complex and widely varying geometries.Currently, empirical or ray optical models are the most common for indoor propagation. Empirical models based on measurement campaigns provide limited accuracy, are very costly and time-consuming but provide rapid predictions. Deterministic models are applied to the geometrical representation of the environment and are based on Maxwell’s equations. They can produce more accurate predictions than empirical models. Ray tracing, an approximate model, is the most popular deterministic model for indoor propagation. The current trend of research is focused on improving its accuracy. Full-wave propagation models are based on the numerical solution of Maxwell’s equations. They are able to produce accurate predictions about the wireless channel. However, they are very computationally expensive. Thus, there has been limited attempts at developing indoor propagation models based on full-wave techniques. In this work, the Volume Electric Field Integral Equation (VEFIE) is used as the basis of a full-wave indoor propagation model. The 2D and 3D formulations of the VEFIE are applied to model the propagation of radio waves indoors. An enhancement to the 2D VEFIE, called 2D to 3D models, is developed to improve its accuracy and utilise its efficiency. It is primarily used for the prediction of time domain characteristics due to its high efficiency whereas the 3D VEFIE is shown to be suitable for frequency domain predictions

Doctor of Philosophy

dissertationWireless communication has become an essential part of everyday life. The hunger for more data, more phone calls, more video, and more access in more places, including vehicles, is growing massively. Communication in vehicles is particularly challenging because of their extremely high multipath environment. In addition, there is significant interest in reducing the number of wires in vehicles to reduce weight, complexity, maintenance, etc. and replace them with wireless systems. Preliminary research shows that MIMO systems take advantage of the extreme multipath environment found in aircraft and other vehicles and also provides more consistent channel capacity than SISO systems. The purpose of this research was to quantify complex channels (including the aircraft/vehicle environment) and their relation to other environments, evaluate MIMO in aircraft, provide design constraints for accurately modeling complex channels, and provide information to predict optimum antenna type and location to enable communication in aircraft/cars/buses/ships/trains/etc. and other extreme channels. The ability to evaluate and design MIMO technologies from the guidelines in this paper is potentially transformative for aircraft safety - enabling a new generation of location specific monitoring and maintenance. Average measured capacity was found to be between 18 and 21 bits/s/Hz using a 4x4 array of antennas, and had no direct relation to the size of the channel. Site-specific capacity showed a multipath rich channel, varying between 15 to 23 bits/s/Hz. The capacity decreased for increasing measurement distance, with exceptions near reflective objects that increase multipath. Due to these special circumstances for site-specific locations within complex channels, it is recommended that 3D ray tracing be used for modeling as it is more accurate than commonly used statistical models, within 1.1 bits/s/Hz. This showed that our 3D ray tracing is adaptable to various environments and gives a more accurate depiction than statistical models that average channel variations. This comes at the cost of greater model complexity. If increased complexity is not desirable, Nakagami 1.4 could be used as the next most accurate model. Design requirements for modeling different complex channels involve a detailed depiction of channel geometry, including height, width, length, shape (square, cylindrical, slanted walls, etc.), large windows, and reflective objects inside the channel space, especially those near the transmitter. Overall, the multipath rich channel found in vehicles is an excellent environment for MIMO systems. These complex channels can be simulated accurately without measurement and before they are even built using our sitespecific 3D ray tracing software combined with a detailed signal model to incorporate antenna effects