Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

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Indoor Localization System Using Wireless Sensor Network

Iraqi Journal of Computer Communication Control and System Engineering ◽

10.33103/uot.ijccce.18.1.3 ◽

2018 ◽

pp. 29-38

Keyword(s):

Indoor Localization ◽

Low Cost ◽

Wide Band ◽

Human Movement ◽

Indoor Positioning ◽

Positioning System ◽

Wireless System ◽

Anchor Nodes ◽

Indoor Positioning System ◽

Better Than

Building a precise low cost indoor positioning and navigation wireless system is a challenging task. The accuracy and cost should be taken together into account. Especially, when we need a system to be built in a harsh environment. In recent years, several researches have been implemented to build different indoor positioning system (IPS) types for human movement using wireless commercial sensors. The aim of this paper is to prove that it is not always the case that having a larger number of anchor nodes will increase the accuracy. Two and three anchor nodes of ultra-wide band with or without the commercial devices (DW 1000) could be implemented in this work to find the Localization of objects in different indoor positioning system, for which the results showed that sometimes three anchor nodes are better than two and vice versa. It depends on how to install the anchor nodes in an appropriate scenario that may allow utilizing a smaller number of anchors while maintaining the required accuracy and cost.

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A NOVEL HIGH ACCURACY INDOOR POSITIONING SYSTEM BASED ON WIRELESS LANS

Progress In Electromagnetics Research C ◽

10.2528/pierc11070805 ◽

2011 ◽

Vol 24 ◽

pp. 25-42 ◽

Cited By ~ 4

Author(s):

Yongxiang Zhao ◽

Qiang Shen ◽

Lianmeng Zhang

Keyword(s):

Wireless Lans ◽

Indoor Positioning ◽

High Accuracy ◽

Positioning System ◽

Indoor Positioning System

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Low-Power Indoor Positioning Algorithm Based on iBeacon Network

Complexity ◽

10.1155/2021/8475339 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaona Zhang ◽

Shufang Zhang ◽

Shuaiheng Huai

Keyword(s):

Low Power ◽

Performance Improvement ◽

General Framework ◽

Logical Structure ◽

Indoor Positioning ◽

Positioning System ◽

Depth Analysis ◽

Communication Distance ◽

Positioning Algorithm ◽

Indoor Positioning System

In this article, we use a low-power iBeacon network to conduct an in-depth analysis and research on the principle of indoor positioning and adopt an efficient and fast positioning algorithm. Secondly, based on the obtained analysis, an iBeacon-based indoor positioning system is proposed to analyze how to use iBeacon for accurate positioning and whether it can effectively compensate for the current mainstream positioning system. We analyze the requirements of the iBeacon-based indoor positioning system and propose the design of this positioning system. We analyze the platform and environment for software development, design the general framework of the positioning system, and analyze the logical structure of the whole system, the structure of data flow, and the communication protocols between each module of the positioning system. Then, we analyze the functions of the server module and the client module of the system, implement the functions of each module separately, and debug the functions of each module separately after each module is implemented. The feasibility of the algorithm and the performance improvement are confirmed by the experimental data. Our results show that the communication distance is improved by approximately 20.25% and the accuracy is improved by 5.62% compared to other existing results.

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Mobile Robot Indoor Positioning System Based on K-ELM

Journal of Sensors ◽

10.1155/2019/7547648 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

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.

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Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽

10.3390/s19173657 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3657 ◽

Cited By ~ 1

Author(s):

Michał R. Nowicki ◽

Piotr Skrzypczyński

Keyword(s):

Indoor Localization ◽

Dead Reckoning ◽

Indoor Positioning ◽

Positioning System ◽

Indoor Environments ◽

University Campus ◽

Sources Of Information ◽

Visual Place Recognition ◽

Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

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