Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.

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DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽

10.3390/s21062000 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2000

Author(s):

Marius Laska ◽

Jörg Blankenbach

Keyword(s):

Wireless Local Area Network ◽

Indoor Localization ◽

Local Area Network ◽

Success Probability ◽

Local Area ◽

Position Estimation ◽

Location Based Services ◽

Global Navigation Satellite Systems ◽

Area Network ◽

Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

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A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics

Mathematics in Engineering ◽

10.3934/mine.2022021 ◽

2021 ◽

Vol 4 (3) ◽

pp. 1-16

Author(s):

Giulio Ortali ◽

◽

Nicola Demo ◽

Gianluigi Rozza ◽

Keyword(s):

Gaussian Process ◽

Stokes Problem ◽

Gaussian Process Regression ◽

Engineering Problem ◽

Data Driven ◽

Regression Approach ◽

Data Driven Approach ◽

Naval Engineering ◽

Proper Orthogonal ◽

Industrial Problem

<abstract><p>This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.</p></abstract>

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Bayesian Assignment of Coverage Count Locations to Factor Groups and Estimation of Mean Daily Traffic

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196154200105 ◽

1996 ◽

Vol 1542 (1) ◽

pp. 30-37 ◽

Cited By ~ 5

Author(s):

Gary A. Davis ◽

Yuzhe Guan

Keyword(s):

Likelihood Function ◽

Factor Group ◽

Bayes Estimator ◽

Data Driven Approach ◽

Seasonal Factor ◽

Group A ◽

Sample Count ◽

Typical Day ◽

Classification Probability ◽

A Site

Mean daily traffic (MDT) is the expected traffic volume at some site on a typical day, and it is usually estimated from short-count data by computing average daily traffic (ADT) and then correcting this ADT for the season or day of week of the count. Although considerable guidance exists on how to construct seasonal factor groups from automatic traffic recorder counts, less guidance is available on how to select the appropriate factors for correcting a particular short count or for estimating MDT when the appropriate factors are uncertain. A data-driven approach to assigning highway sites to factor groups using arbitrary samples of daily traffic counts, a method for designing traffic count samples to minimize the likelihood of assigning the site to the wrong factor group, and a Bayes estimator of MDT are described. A likelihood function describing the sample count is combined with prior estimates of the probabilities that a site belongs to each factor group to produce posterior classification probabilities. The site is then assigned to that factor group showing the highest posterior classification probability. The classification method is evaluated by using actual traffic data and appears to perform reliably with 14-day samples. A Bayes estimator of MDT is then developed, which is applicable even when it is unclear to which factor group a short-count site ought to be assigned. This estimator is evaluated by using actual data, and it also performs creditably with 14-day samples.

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Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Applied Sciences ◽

10.3390/app9183930 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3930 ◽

Cited By ~ 1

Author(s):

Jaehyun Yoo ◽

Jongho Park

Keyword(s):

Feature Extraction ◽

Indoor Localization ◽

Signal Strength ◽

Received Signal Strength ◽

Position Estimation ◽

Training Data ◽

Data Sets ◽

Feature Extraction Method ◽

Fingerprinting Method ◽

Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

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Joint Delay Doppler Probability Density Functions for Air-to-Air Channels

International Journal of Antennas and Propagation ◽

10.1155/2014/814218 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Michael Walter ◽

Dmitriy Shutin ◽

Uwe-Carsten Fiebig

Keyword(s):

Probability Density ◽

Channel Model ◽

Three Dimensional ◽

Doppler Frequency ◽

Probability Density Functions ◽

Density Functions ◽

Wide Sense ◽

Channel Measurements ◽

Vector Calculus ◽

Air Channels

Recent channel measurements indicate that the wide sense stationary uncorrelated scattering assumption is not valid for air-to-air channels. Therefore, purely stochastic channel models cannot be used. In order to cope with the nonstationarity a geometric component is included. In this paper we extend a previously presented two-dimensional geometric stochastic model originally developed for vehicle-to-vehicle communication to a three-dimensional air-to-air channel model. Novel joint time-variant delay Doppler probability density functions are presented. The probability density functions are derived by using vector calculus and parametric equations of the delay ellipses. This allows us to obtain closed form mathematical expressions for the probability density functions, which can then be calculated for any delay and Doppler frequency at arbitrary times numerically.

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Broadband Extended Array Response-Based Subspace Multiparameter Estimation Method for Multipolarized Wireless Channel Measurements

10.36227/techrxiv.12682061.v1 ◽

2020 ◽

Author(s):

Bensheng Yang ◽

Peize Zhang ◽

Haiming Wang ◽

Cheng-Xiang Wang ◽

Xiaohu You

Keyword(s):

Antenna Array ◽

Mobile Communications ◽

Channel Model ◽

Estimation Method ◽

Wireless Channel ◽

Antenna Element ◽

Channel Measurements ◽

Multiple Signal Classification ◽

Channel Sounder ◽

Multiple Parameters

The clustered delay line channel model, in which each cluster consists of a number of rays, is widely used for link-level evaluations in mobile communications. Multiple parameters of each ray, including delay, amplitude, cross polarization ratio (XPR), initial phases of four polarization combinations and the azimuth and elevation angles of arrival and departure, shall be known. These parameters are measured using a channel sounder. The number of rays in every cluster is usually greater than the number of elements in the antenna array of the channel sounder, which represents a challenging issue in multipolarized channel measurements. A new subspace estimation method based on the broadband extended array response of an electromagnetic vector antenna array is proposed to resolve a large number of rays. The interelement spacing of the array can be greater than half the carrier wavelength, which reduces interelement coupling and simplifies the array design, especially for millimeter wave bands. First, the delay of each cluster is estimated using the reference antenna element. Then, the 2D angles of every ray are estimated using the classic rank-deficient multiple signal classification (MUSIC). Lastly, the initial phases, XPR and amplitude of every ray is estimated. Simulation results validate the proposed method.

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Indoor Localization Using Bayesian Filter

10.35543/osf.io/afkej ◽

2019 ◽

Author(s):

Karthik Muthineni ◽

Attaphongse Taparugssanagorn

Keyword(s):

Communication Technology ◽

Indoor Localization ◽

Position Estimation ◽

Received Signal Strength Indicator ◽

Localization Accuracy ◽

Ambient Intelligent ◽

Unknown Node ◽

Visualization Of Data ◽

Log Normal ◽

Environment Parameters

Ambient Intelligent (AmI) Wireless Sensor Networks (WSN) provide intelligent services based on user and environment data obtained by sensors. Such networks are developed to give environmental monitoring and indoor localization services. In this work, Zigbee which is a wireless communication technology is used for localization based on Received Signal Strength Indicator (RSSI) method. In practice, Extended Kalman Filter (EKF) is adapted to filter RSSI values influenced by multi-path fading and noise. Log-Normal Shadowing Method (LNSM) together with the Trilateration method was implemented to locate the position of the unknown node or entity. In addition, Cramer Rao Lower Bound (CRLB) is derived for the position estimation, that can be used to evaluate the performance of the system in terms of localization accuracy. Along with indoor localization, the deployed WSN could also monitor environment parameters like temperature and humidity surrounding entity using Digital Humidity and Temperature (DHT11) sensor. Using Zigbee location coordinates of entity and environment parameters are transmitted to remote desktop where visualization of data is done using Matrix Laboratory (MATLAB).

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An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

Journal of Sensors ◽

10.1155/2021/7621393 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yushuai Zhang ◽

Jianxin Guo ◽

Feng Wang ◽

Rui Zhu ◽

Liping Wang

Keyword(s):

Indoor Localization ◽

Inertial Navigation ◽

Detection Algorithm ◽

Position Estimation ◽

Position Error ◽

Indoor Navigation ◽

Bayesian Filter ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

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Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Sensors ◽

10.3390/s21217051 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7051

Author(s):

José Manuel Villadangos ◽

Jesús Ureña ◽

Juan Jesús García-Domínguez ◽

Ana Jiménez-Martín ◽

Álvaro Hernández ◽

...

Keyword(s):

Indoor Localization ◽

Weighting Factor ◽

Position Estimation ◽

Correlation Technique ◽

Time Of Arrival ◽

Dynamic Adjustment ◽

Positioning Systems ◽

Positioning Errors ◽

Local Positioning System ◽

Cross Correlation Technique

Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor β (GCC-PHAT-β). To improve the performance of the GCC-PHAT-β in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

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Indoor Localization System Based on Bluetooth Low Energy for Museum Applications

Electronics ◽

10.3390/electronics9061055 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1055 ◽

Cited By ~ 1

Author(s):

Romeo Giuliano ◽

Gian Carlo Cardarilli ◽

Carlo Cesarini ◽

Luca Di Nunzio ◽

Francesca Fallucchi ◽

...

Keyword(s):

Indoor Localization ◽

Position Estimation ◽

Low Energy ◽

Least Square ◽

Feed Forward Neural Network ◽

Position Information ◽

Bluetooth Low Energy ◽

Performance Results ◽

Linear Least Square ◽

Position Estimate

In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the managing of people and/or robots for internal logistics in very large warehouses (e.g., Amazon warehouses, etc.). In this paper, we study and develop a system allowing the accurate indoor localization of people visiting a museum or any other cultural institution. We assume visitors are equipped with a Bluetooth Low Energy (BLE) device (commonly found in modern smartphones or in a small chipset), periodically transmitting packets, which are received by geolocalized BLE receivers inside the museum area. Collected packets are provided to the locator server to estimate the positions of the visitors inside the museum. The position estimation is based on a feed-forward neural network trained by a measurement campaign in the considered environment and on a non-linear least square algorithm. We also provide a strategy for deploying the BLE receivers in a given area. The performance results obtained from measurements show an achievable position estimate accuracy below 1 m.

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