scholarly journals Localization Reliability Improvement Using Deep Gaussian Process Regression Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4164 ◽  
Author(s):  
Fei Teng ◽  
Wenyuan Tao ◽  
Chung-Ming Own
Keyword(s):  
Gaussian Process ◽  
Dynamic Environment ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Nonparametric Model ◽  
Positioning System ◽  
Positioning Accuracy

With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The method that is based on received signal strength (RSS) is the most widely used. However, manually measuring RSS signal values to build a fingerprint database is costly and time-consuming, and it is impractical in a dynamic environment with a large positioning area. In this study, we propose an indoor positioning system that is based on the deep Gaussian process regression (DGPR) model. This model is a nonparametric model and it only needs to measure part of the reference points, thus reducing the time and cost required for data collection. The model converts the RSS values into four types of characterizing values as input data and then predicts the position coordinates using DGPR. Finally, after reinforcement learning, the position coordinates are optimized. The authors conducted several experiments on a simulated environment by MATLAB and physical environments at Tianjin University. The experiments examined different environments, different kernels, and positioning accuracy. The results showed that the proposed method could not only retain the positioning accuracy, but also save the computation time that is required for location estimation.

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

scholarly journals Analyzing Machine Learning Models with Gaussian Process for the Indoor Positioning System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yunxin Xie ◽  
Chenyang Zhu ◽  
Wei Jiang ◽  
Jia Bi ◽  
Zhengwei Zhu
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Learning Approaches ◽  
Positioning Accuracy ◽  
Access Points ◽  
Indoor Positioning System

Recently, there has been growing interest in improving the efficiency and accuracy of the Indoor Positioning System (IPS). The Received Signal Strength- (RSS-) based fingerprinting technique is essential for indoor localization. However, it is challenging to estimate the indoor position based on RSS’s measurement under the complex indoor environment. This paper evaluates three machine learning approaches and Gaussian Process (GP) regression with three different kernels to get the best indoor positioning model. The hyperparameter tuning technique is used to select the optimum parameter set for each model. Experiments are carried out with RSS data from seven access points (AP). Results show that GP with a rational quadratic kernel and eXtreme gradient tree boosting model has the best positioning accuracy compared to other models. In contrast, the eXtreme gradient tree boosting model could achieve higher positioning accuracy with smaller training size and fewer access points.

scholarly journals Lightweight Workload Fingerprinting Localization Using Affinity Propagation Clustering and Gaussian Process Regression

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4267 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Reference Points ◽  
Affinity Propagation ◽  
Real Field ◽  
Positioning System ◽  
Online Computation ◽  
Affinity Propagation Clustering

Fingerprinting localization approach is widely used in indoor positioning applications owing to its high reliability. However, the learning procedure of radio signals in fingerprinting is time-consuming and labor-intensive. In this paper, an affinity propagation clustering (APC)-based fingerprinting localization system with Gaussian process regression (GPR) is presented for a practical positioning system with the reduced offline workload and low online computation cost. The proposed system collects sparse received signal strength (RSS) data from the deployed Bluetooth low energy beacons and trains them with the Gaussian process model. As the signal estimation component, GPR predicts not only the mean RSS but also the variance, which indicates the uncertainty of the estimation. The predicted RSS and variance can be employed for probabilistic-based fingerprinting localization. As the clustering component, the APC minimizes the searching space of reference points on the testbed. Consequently, it also helps to reduce the localization estimation error and the computational cost of the positioning system. The proposed method is evaluated through real field deployments. Experimental results show that the proposed method can reduce the offline workload and increase localization accuracy with less computational cost. This method outperforms the existing methods owing to RSS prediction using GPR and RSS clustering using APC.

scholarly journals Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanfen Le ◽  
Shijialuo Jin ◽  
Hena Zhang ◽  
Weibin Shi ◽  
Heng Yao
Keyword(s):  
Ridge Regression ◽  
Local Area Network ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Feature Reduction ◽  
Reference Points ◽  
Area Network ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Positioning Method

An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel-based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

An Improved and Low-dimensional Fingerprint-based Localization Method in Collocated Massive MIMO-OFDM Systems

2021 ◽  
Author(s):  
Seyedeh Samira Moosavi ◽  
Paul Fortier
Keyword(s):  
Computational Complexity ◽  
Gaussian Process ◽  
Massive Mimo ◽  
High Reliability ◽  
Gaussian Process Regression ◽  
Location Estimation ◽  
Ofdm Systems ◽  
Process Data ◽  
Localization Method ◽  
Low Dimensional

Abstract Localization has drawn significant attention in 5G due to the fast-growing demand for location-based service (LBS). Massive multiple-input multiple-output (M-MIMO) has been introduced in 5G as a powerful technology due to its evident potentials for communication performance enhancement and localization in complicated environments. Fingerprint-based (FP) localization are promising methods for rich scattering environments thanks to their high reliability and accuracy. The Gaussian process regression (GPR) method could be used as an FP-based localization method to facilitate localization and provide high accuracy. However, this method has high computational complexity, especially in large-scale environments. In this study, we propose an improved and low-dimensional FP-based localization method in collocated massive MIMO orthogonal frequency division multiplexing (OFDM) systems using principal component analysis (PCA), the affinity propagation clustering (APC) algorithm, and Gaussian process regression (GPR) to estimate the user's location. Fingerprints are first extracted based on instantaneous channel state information (CSI) by taking full advantage of the high-resolution angle and delay domains. First, PCA is used to pre-process data and reduce the feature dimension. Then, the training fingerprints are clustered using the APC algorithm to increase prediction accuracy and reduce computation complexity. Finally, each cluster's data distribution is accurately modelled using GPR to provide support for further localization. Simulation results reveal that the proposed method improves localization performance significantly by reducing the location estimation error. Additionally, it reduces the matching complexity and computational complexity.

scholarly journals Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4184
Author(s):  
Hanany Tolba ◽  
Nouha Dkhili ◽  
Julien Nou ◽  
Julien Eynard ◽  
Stéphane Thil ◽  
...  
Keyword(s):  
Comparative Study ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Training Process ◽  
Short Term ◽  
Forecast Horizon ◽  
Forecasting Accuracy ◽  
The Comparative Study ◽  
Persistence Model

In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

scholarly journals Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators

2019 ◽  
Vol 39 (4) ◽  
pp. 405-413 ◽  
Author(s):  
Tiago M. de Carvalho ◽  
Eveline A. M. Heijnsdijk ◽  
Luc Coffeng ◽  
Harry J. de Koning
Keyword(s):  
Prostate Cancer ◽  
Simulation Model ◽  
Gaussian Process ◽  
Prediction Error ◽  
Life Histories ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Computational Effort ◽  
Sensitivity Analyses ◽  
Model Parameters

Background. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required. Objective. To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model. Methods. We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error. Results. Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs. Conclusions. For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.

scholarly journals INVESTIGATION OF PRACTICAL AND THEORETICAL ACCURACY OF WIRELESS INDOOR POSITIONING SYSTEM UBISENSE

2013 ◽  
Vol 95 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Marek Woźniak ◽  
Waldemar Odziemczyk ◽  
Kamil Nagórski
Keyword(s):  
Reference Points ◽  
Positioning System ◽  
Accuracy Analysis ◽  
Positioning Accuracy ◽  
Test Measurement ◽  
Calibration Accuracy ◽  
Indoor Positioning System ◽  
Orientation Parameters ◽  
Measurement Area ◽  
Theoretical Accuracy

Abstract This paper presents the accuracy investigation results and functionality of Ubisense RTLS positioning system. Three kinds of studies were conducted: test of calibration accuracy, analysis of theoretical accuracy of the coordinates determination as well as accuracy measurements in field conditions. Test of calibration accuracy was made with several different geometric constellation of reference points (tag positions). We determined changes of orientation parameters of receivers and disturbance of positioning points coordinates against chosen reference points constellations. Analysis of theoretical accuracy was made for several receivers spatial positions and their orientations. It allowed to indicate favourable and unfavourable measurement area considering accuracy and reliability. Real positioning accuracy of the Ubisense system was determined by comparison with coordinates measured using precise tacheometer TCRP1201+. Results of conducted experiments and accuracy analysis of test measurement were presented in figures and diagrams.

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