Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters

2014 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Benjamin Sanda ◽  
Ikhlas Abdel-Qader ◽  
Abiola Akanmu
Keyword(s):  
Real Time ◽  
Radio Frequency Identification ◽  
Kalman Filters ◽  
Tracking Error ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Real World Data ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Localization Systems

The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.

Improving Accuracy for Range-Based Localization in Multiple Target Wireless Sensor Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Oscar Rodríguez ◽  
Tomoaki Ohtsuki
Keyword(s):  
Location Estimation ◽  
Wireless Sensor ◽  
Target Range ◽  
Time Of Arrival ◽  
Multiple Target ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Improving Accuracy ◽  
Localization Systems

We introduce a system for multiple target range-based localization systems, such as those based on time-of-arrival (TOA) and received signal strength indicator (RSSI) for distance measurement estimations. In order to improve the accuracy of the location estimation for all target nodes, our system makes use of distance estimations between target nodes as well as between anchor and target nodes, and weights for all nodes. We propose variations on two popular localization algorithms and compare their accuracy against that of the conventional algorithms using simulations and experiments. Our results show that our proposal consistently offers a better localization accuracy than the conventional algorithms.

scholarly journals A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 440 ◽  
Author(s):  
Jing Xin ◽  
Kaiyuan Gao ◽  
Mao Shan ◽  
Bo Yan ◽  
Ding Liu
Keyword(s):  
Real Time ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Bayesian Filtering ◽  
Tracking Accuracy ◽  
Noise Characteristics ◽  
Error Mitigation ◽  
Filtering Approach

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems.

scholarly journals Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3933
Author(s):  
Mohammed El-Absi ◽  
Feng Zheng ◽  
Ashraf Abuelhaija ◽  
Ali Al-haj Abbas ◽  
Klaus Solbach ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Channel Modeling ◽  
Low Complexity ◽  
Small Scale ◽  
Indoor Environments ◽  
Wave Band ◽  
Localization Algorithm ◽  
Localization Accuracy

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

Dynamic multipath mitigation applying unscented Kalman filters in local positioning systems

2011 ◽  
Vol 3 (3) ◽  
pp. 365-372 ◽  
Author(s):  
Thorsten Nowak ◽  
Andreas Eidloth
Keyword(s):  
Kalman Filters ◽  
Multipath Propagation ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Multipath Mitigation ◽  
Positioning Systems ◽  
Channel Dynamics ◽  
Outdoor Environments ◽  
Sequential Bayesian Estimation

Multipath propagation is still one of the major problems in local positioning systems today. Especially in indoor environments, the received signals are disturbed by blockages and reflections. This can lead to a large bias in the user's time-of-arrival (TOA) value. Thus multipath is the most dominant error source for positioning. In order to improve the positioning performance in multipath environments, recent multipath mitigation algorithms based upon the concept of sequential Bayesian estimation are used. The presented approach tries to overcome the multipath problem by estimating the channel dynamics, using unscented Kalman filters (UKF). Simulations on artificial and measured channels from indoor as well as outdoor environments show the profit of the proposed estimator model. Furthermore, the quality of channel estimation applying the UKF and the channel sounding capabilities of the estimator are shown.

scholarly journals Evaluating Localization Accuracy of Automated Driving Systems

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5855
Author(s):  
Karl Rehrl ◽  
Simon Gröchenig
Keyword(s):  
Ground Truth ◽  
Vehicle Body ◽  
Automated Driving ◽  
High Definition ◽  
Localization Accuracy ◽  
Confidence Levels ◽  
Accurate Localization ◽  
Localization Test ◽  
Pros And Cons ◽  
Localization Systems

Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.

Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

scholarly journals Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 642
Author(s):  
Luis Miguel González de Santos ◽  
Ernesto Frías Nores ◽  
Joaquín Martínez Sánchez ◽  
Higinio González Jorge
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Execution Time ◽  
Point Cloud ◽  
Indoor Environments ◽  
Calculation Algorithm ◽  
Time Restrictions ◽  
Planning Algorithm ◽  
Inspection Tasks ◽  
Path Planning Algorithm

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

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