P. Sankhe, S. Azim, Sachin Goyal, Tanya Choudhary, K. Appaiah, S. Srikant
{"title":"Indoor Distance Estimation using LSTMs over WLAN Network","authors":"P. Sankhe, S. Azim, Sachin Goyal, Tanya Choudhary, K. Appaiah, S. Srikant","doi":"10.1109/WPNC47567.2019.8970257","DOIUrl":"https://doi.org/10.1109/WPNC47567.2019.8970257","url":null,"abstract":"The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy degradation and are almost unavailable in indoor environments. Indoor positioning systems (IPS) based on WiFi signals have been gaining popularity. However, owing to the strong spatial and temporal variations of wireless communication channels in the indoor environment, the achieved accuracy of existing IPS is around several tens of centimeters. We present the detailed design and implementation of a self-adaptive WiFi-based indoor distance estimation system using LSTMs. The system is novel in its method of estimating with high accuracy the distance of an object by overcoming possible causes of channel variations and is self-adaptive to the changing environmental and surrounding conditions. The proposed design has been developed and physically realized over a WiFi network consisting of ESP8266 (NodeMCU) devices. The experiments were conducted in a real indoor environment while changing the surroundings in order to establish the adaptability of the system. We compare different architectures for this task based on LSTMs, CNNs, and fully connected networks (FCNs). We show that the LSTM based model performs better among all the above-mentioned architectures by achieving an accuracy of 5.85 cm with a confidence interval of 93% on the scale of (8.46 m × 6.98 m). To the best of our knowledge, the proposed method outperforms other methods reported in the literature by a significant margin.","PeriodicalId":284815,"journal":{"name":"2019 16th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116404971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Constanze Hungar, Jenny Fricke, Stefan Jürgens, F. Köster
{"title":"Detection of Feature Areas for Map-based Localization Using LiDAR Descriptors","authors":"Constanze Hungar, Jenny Fricke, Stefan Jürgens, F. Köster","doi":"10.1109/WPNC47567.2019.8970180","DOIUrl":"https://doi.org/10.1109/WPNC47567.2019.8970180","url":null,"abstract":"Map-based localization is an essential challenge for the development of autonomous vehicles. Popular localization solutions depend on static, semantic objects, like road signs. In this paper, we introduce a novel approach to extract feature areas (FAs) within LiDAR point clouds enabling the detection of non-semantic map (MFAs) as well as on-board (KFAs) areas. KFAs compose a set of connected points with similar geometry-based descriptors which are extracted based on their benefit for the localization task. As opposed to other extraction methods based on LiDAR descriptors, our approach selects areas rather than detecting single key points. This input is used by our extraction approach in a two-stepped clustering and discarding process resulting in non-semantic segments. Our simple localization algorithm following the feature-based approach is more accurate than point-based localization on a real-world data set. We show that the feature extraction works persistently over data sets spanning one and a half year.","PeriodicalId":284815,"journal":{"name":"2019 16th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116877050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"E-SALDAT: Efficient Single-Anchor Localization of Dual-Antenna Tags","authors":"L. Botler, K. Diwold, K. Römer","doi":"10.1109/WPNC47567.2019.8970253","DOIUrl":"https://doi.org/10.1109/WPNC47567.2019.8970253","url":null,"abstract":"Accurate localization is required to enable location awareness of devices in many IoT applications. Current state-of-the-art ultra-wideband solutions make use of ranging measurements in a multi-anchor setup. In this paper, we propose a novel single-anchor scheme (E-SALDAT): an improvement of the Dual Wireless Radio Localization (DWRL). While four ranging measurements were previously required for semi-localization (usually, the first step towards definite localization/rigid-localization), E-SALDAT allows for semi-localization with only two ranging measurements. This results in a significant improvement of overall power efficiency and localization ratio. We present a comparison of the proposed localization scheme E-SALDAT to its predecessors I-DWRL and DWRL. The comparison was performed based on simulations. Our results suggest that E-SALDAT, on average, can achieve double the localization ratio of I-DWRL with just 23% more messages.","PeriodicalId":284815,"journal":{"name":"2019 16th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133113135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN","authors":"Grigorios G. Anagnostopoulos, Alexandros Kalousis","doi":"10.1109/WPNC47567.2019.8970177","DOIUrl":"https://doi.org/10.1109/WPNC47567.2019.8970177","url":null,"abstract":"The use of fingerprinting localization techniques in outdoor IoT settings has started gaining popularity over the recent years. Communication signals of Low Power Wide Area Networks (LPWAN), such as LoRaWAN, are used to estimate the location of low power mobile devices. In this study, a publicly available dataset of LoRaWAN RSSI measurements is utilized to compare different machine learning methods and their accuracy in producing location estimates. The tested methods are: the k Nearest Neighbours method, the Extra Trees method and a neural network approach using a Multilayer Perceptron. To facilitate the reproducibility of tests and the comparability of results, the code and the train/validation/test split of the dataset used in this study have become available. The neural network approach was the method with the highest accuracy, achieving a mean error of 357 meters and a median error of 206 meters.","PeriodicalId":284815,"journal":{"name":"2019 16th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121369062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}