A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

Impulse-radio ultrawideband (IR-UWB) is a promising technique for indoor localization due to its high accuracy and robustness against multipath interferences. In this paper, to deal with the synchronization challenges among anchors in traditional time-difference-of-arrival (TDOA) localization systems, we propose an asynchronous TDOA (ATDOA) localization method. Based on the ranging error model, we derive the theoretical lower bounds as the performance metrics of localization accuracy. Compared with the ideal TDOA method, ATDOA degrades on localization accuracy for eliminating the high accuracy synchronization requirements, which is pretty much attractive in energy and complexity limited scenarios. Based on the performance analysis, we show that there exists optimal anchor deployment in ATDOA that minimizes the localization errors. We also formulate the relationship between this optimal deployment and the size of the covered area, which is meaningful in both theoretical analysis and practical system designs.

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Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Sensors ◽

10.3390/s21165346 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5346

Author(s):

Elias Hatem ◽

Sergio Fortes ◽

Elizabeth Colin ◽

Sara Abou-Chakra ◽

Jean-Marc Laheurte ◽

...

Keyword(s):

Radio Frequency Identification ◽

Indoor Localization ◽

Cost Effective ◽

Low Complexity ◽

Propagation Model ◽

Cumulative Distribution ◽

Navigation Systems ◽

Localization System ◽

Fingerprinting Method ◽

Two Stages

Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity.

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

Sensors ◽

10.3390/s20143933 ◽

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.

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