Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Design and realization of precise indoor localization mechanism for Wi-Fi devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version

A survey of deep learning approaches for WiFi-based indoor positioning

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Automatic Wi-Fi Fingerprint System based on Unsupervised Learning

Recently, smartphones and Wi-Fi appliances have been generalized in daily life, and location-based service(LBS) has gradually been extended to indoor environments. Unlike outdoor positioning, which is typically handled by the global positioning system(GPS), indoor positioning technologies for providing LBSs have been studied with algorithms using various short-range wireless communications such as Wi-Fi, Ultra-wideband, Bluetooth, etc. Fingerprint-based positioning technology, a representative indoor LBS, estimates user locations using the received signal strength indicator(RSSI), indicating the relative transmission power of the access point(AP). Therefore, a fingerprint-based algorithm has the advantage of being robust to distorted wireless environments, such as radio wave reflections and refractions, compared to the time-of-arrival(TOA) method for non-line-of-sight(NLOS), where many obstacles exist. Fingerprint is divided into a training phase in which a radio map is generated by measuring the RSSIs of all indoor APs and positioning phase in which the positions of users are estimated by comparing the RSSIs of the generated radio map in real-time. In the training phase, the user collects the RSSIs of all APs measured at reference points set at regular intervals of 2 to 3m, creating a radio map. In the positioning phase, the reference point, which is most similar to the RSSI, compares the generated radio map from the training phase to the RSSI measured from user movements. This estimates the real-time indoor position. Fingerprint algorithms based on supervised and semi-supervised learning such as support vector machines and principal component analysis are essential for measuring the RSSIs in all indoor areas to produce a radio map. As the building size and the complexity of structures increases, the amount of work and time required also increase. The radio map generation algorithm that uses channel modeling does not require direct measurement, but it requires considerable effort because of building material, three-dimensional reflection coefficient, and numerical modeling of all obstacles. To overcome these problems, this thesis proposes an automatic Wi-Fi fingerprint system that combines an unsupervised dual radio mapping(UDRM) algorithm that reduces the time taken to acquire Wi-Fi signals and leverages an indoor environment with a minimum description length principle(MDLP)-based radio map feedback(RMF) algorithm to simultaneously optimize and update the radio map. The proposed UDRM algorithm in the training phase generates a radio map of the entire building based on the measured radio map of one reference floor by selectively applying the autoencoder and the generative adversarial network(GAN) according to the spatial structures. The proposed learning-based UDRM algorithm does not require labeled data, which is essential for supervised and semi-supervised learning algorithms. It has a relatively low dependency on RSSI datasets. Additionally, it has a high accuracy of radio map prediction than existing models because it learns the indoor environment simultaneously via a indoor two-dimensional map(2-D map). The produced radio map is used to estimate the real-time positioning of users in the positioning phase. Simultaneously, the proposed MDLP-based RMF algorithm analyzes the distribution characteristics of the RSSIs of newly measured APs and feeds the analyzed results back to the radio map. The MDLP, which is applied to the proposed algorithm, improves the performance of the positioning and optimizes the size of the radio map by preventing the indefinite update of the RSSI and by updating the newly added APs to the radio map. The proposed algorithm is compared with a real measurement-based radio map, confirming the high stability and accuracy of the proposed fingerprint system. Additionally, by generating a radio map of indoor areas with different structures, the proposed system is shown to be robust against the change in indoor environment, thus reducing the time cost. Finally, via a euclidean distance-based experiment, it is confirmed that the accuracy of the proposed fingerprint system is almost the same as that of the RSSI-based fingerprint system.|최근 스마트폰과 Wi-Fi가 실생활에 보편화되면서 위치기반 서비스에 대한 개발 분야가 실내 환경으로 점차 확대되고 있다. GPS로 대표되는 실외 위치 인식과 달리 위치기반 서비스를 제공하기 위한 실내 위치 인식 기술은 Wi-Fi, UWB, 블루투스 등과 같은 다양한 근거리 무선 통신 기반의 알고리즘들이 연구되고 있다. 대표적인 실내 위치인식 알고리즘 중 하나인 Fingerprint는 사용자가 수신한 AP 신호의 상대적인 크기를 나타내는 RSSI를 사용하여 위치를 추정한다. 따라서 Fingerprint기반의 알고리즘은 장애물이 많이 존재하는 비가시 거리에서 TOA 방식에 비해 전파의 반사 및 굴절과 같이 왜곡된 무선 환경에 강인하다는 장점이 있다. Fingerprint는 실내의 모든 AP의 RSSI들을 측정하여 Radio map을 제작하는 과정인 학습 단계와 생성된 Radio map의 RSSI들을 실시간으로 측정된 RSSI와 비교하여 사용자의 위치를 추정하는 위치인식 단계로 나누어진다. 학습 단계에서는 위치를 구분하기 위하여 사용자가 2~3m의 일정한 간격으로 설정된 참조 위치들마다 측정되는 모든 AP들의 RSSI를 수집하고 Radio map으로 제작한다. 위치인식 단계에서는 학습 단계에서 제작된 Radio map과 사용자의 이동에 의해 측정되는 RSSI의 비교를 통해 가장 유사한 RSSI 패턴을 가지는 참조 위치가 실시간 실내 위치로 추정된다. 서포트 벡터 머신(SVM), 주성분 분석(PCA) 등과 같이 지도 및 준지도 학습기반의 Fingerprint 알고리즘은 Radio map을 제작하기 위해 모든 실내 공간에서 RSSI의 측정이 필수적이다. 이러한 알고리즘들은 건물이 대형화되고 구조가 복잡해질수록 측정 공간이 늘어나면서 작업과 시간 소모가 또한 급격히 증가한다. 채널모델링을 통한 Radio map 생성 알고리즘은 직접적인 측정 과정이 불필요한 반면에 건물의 재질, 3차원적인 구조에 따른 반사 계수 및 모든 장애물에 대한 수치적인 모델링이 필수적이기 때문에 상당히 많은 작업량이 요구된다. 따라서 본 논문에서는 이러한 문제점들을 해결하고자 학습 단계에서 Wi-Fi 신호의 수집시간을 최소화하면서 실내 환경이 고려된 Unsupervised Dual Radio Mapping(UDRM) 알고리즘과 위치인식 단계에서 Radio map의 최적화가 동시에 가능한 Minimum description length principle(MDLP)기반의 Radio map Feedback(RMF) 알고리즘이 결합된 비지도학습기반의 자동 Wi-Fi Fingerprint를 제안한다. 학습 단계에서 제안하는 UDRM 알고리즘은 뉴럴 네트워크 기반의 비지도 학습 알고리즘인 Autoencoder와 Generative Adversarial Network (GAN)를 공간구조에 따라 선택적으로 적용하여 하나의 참조 층에서 측정된 Radio map을 기반으로 건물전체의 Radio map을 생성한다. 제안하는 비지도 학습 기반 UDRM 알고리즘은 지도 및 준지도 학습에서 필수적인 Labeled data가 필요하지 않으며 RSSI 데이터 세트의 의존성이 상대적으로 낮다. 또한 2차원 실내 지도를 통해 실내 환경을 동시에 학습하기 때문에 기존의 예측 모델에 비해 Radio map의 예측 정확도가 높다. 제안한 알고리즘에 의해 제작된 Radio map은 위치인식 단계에서 사용자의 실시간 위치인식에 적용된다. 동시에 제안하는 MDLP 기반의 자동 Wi-Fi 업데이트 알고리즘은 새롭게 측정되는 AP들의 RSSI의 분포특성을 분석하고 그 결과를 Radio map에 피드백한다. 제안한 알고리즘에 적용된 MDLP는 무분별한 RSSI의 업데이팅을 방지하고 추가되는 AP를 Radio map에 업데이트함으로서 위치인식의 성능을 향상시키고 Radio map의 크기의 최적화가 가능하다. 제안한 알고리즘은 실제 측정기반의 Radio map과 서로 비교를 통해 제안한 Fingerprint 시스템의 높은 안정성과 정확도를 확인하였다. 또한 구조가 다른 실내공간의 Radio map 생성 결과를 통해 실내 환경 변화에 강인함과 학습 시간 측정을 통한 시간 비용이 감소함을 확인하였다. 마지막으로 Euclidean distance 기반 실험을 통하여 실제 측정한 RSSI기반의 Fingerprint 시스템과 제안한 시스템의 위치인식 정확도가 거의 일치함을 확인하였다.Contents Contents ⅰ Lists of Figures and Tables ⅲ Abstract ⅵ Chapter 1 Introduction 01 1.1 Background and Necessity for Research 01 1.2 Objectives and Contents for Research 04 Chapter 2 Wi-Fi Positioning and Unsupervised Learning 07 2.1 Wi-Fi Positioning 07 2.1.1 Wi-Fi Signal and Fingerprint 07 2.1.2 Fingerprint Techniques 15 2.2 Unsupervised Learning 23 2.2.1 Neural Network 23 2.2.2 Autoencoder 28 2.2.3 Generative Adversarial Network 31 Chapter 3 Proposed Fingerprint System 36 3.1 Unsupervised Dual Radio Mapping Algorithm 36 3.2 MDLP-based Radio Map Feedback Algorithm 47 Chapter 4 Experiment and Result 51 4.1 Experimental Environment and Configuration 51 4.2 Results of Unsupervised Dual Radio Mapping Algorithm 56 4.2 Results of MDLP-based Radio Map Feedback Algorithm 69 Chapter 5 Conclusion 79 Reference 81Docto

Generalizable Deep-Learning-Based Wireless Indoor Localization

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To address the generalizability challenge faced by conventionally trained deep learning localization models, we propose the use of meta-learning-based approaches. By leveraging meta-learning, we aim to improve the models\u27 ability to adapt to new environments without extensive retraining. Additionally, since meta-learning algorithms typically require diverse datasets from various scenarios, which can be difficult to collect specifically for localization tasks, we introduce a novel meta-learning algorithm called TB-MAML (Task Biased Model Agnostic Meta Learning). This algorithm is specifically designed to enhance generalization when dealing with limited datasets. Finally, we conduct an evaluation to compare the performance of TB-MAML-based localization with conventionally trained localization models and other meta-learning algorithms in the context of indoor localization

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored