Development of Low-Cost Indoor Positioning Using Mobile-UWB-Anchor-Configuration Approach

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 Low Cost Indoor Positioning System Using Bluetooth Low Energy

IEEE Access ◽

10.1109/access.2020.3012342 ◽

2020 ◽

Vol 8 ◽

pp. 136858-136871

Author(s):

Lu Bai ◽

Fabio Ciravegna ◽

Raymond Bond ◽

Maurice Mulvenna

Keyword(s):

Low Cost ◽

Indoor Positioning ◽

Low Energy ◽

Positioning System ◽

Bluetooth Low Energy ◽

Indoor Positioning System

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Linear error modeling and noise smoothing for improved low-cost IMU-based indoor positioning

2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) ◽

10.1109/mwscas.2019.8885060 ◽

2019 ◽

Author(s):

Nizam Kuxdorf-Alkirata ◽

Fotios Kasolis ◽

Dieter Bruckmann ◽

Oliver Koch

Keyword(s):

Low Cost ◽

Indoor Positioning ◽

Error Modeling ◽

Noise Smoothing

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Hadamard Matrix Design for a Low-Cost Indoor Positioning System in Visible Light Communication

IEEE Photonics Journal ◽

10.1109/jphot.2017.2667038 ◽

2017 ◽

Vol 9 (2) ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

Jong Kang Park ◽

Tae-Gyoo Woo ◽

Myoungha Kim ◽

Jong Tae Kim

Keyword(s):

Visible Light ◽

Hadamard Matrix ◽

Low Cost ◽

Visible Light Communication ◽

Indoor Positioning ◽

Positioning System ◽

Indoor Positioning System ◽

Matrix Design

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

Entropy ◽

10.3390/e23111401 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1401

Author(s):

Haq Nawaz ◽

Ahsen Tahir ◽

Nauman Ahmed ◽

Ubaid U. Fayyaz ◽

Tayyeb Mahmood ◽

...

Keyword(s):

Low Power ◽

Indoor Localization ◽

Low Cost ◽

Smart Homes ◽

Indoor Positioning ◽

High Accuracy ◽

Positioning System ◽

Telemetry Data ◽

Ultra Low Power ◽

Indoor Positioning System

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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PROGRESS ON ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1709-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1709-1713

Author(s):

C. Wang ◽

Y. Dai ◽

N. El-Sheimy ◽

C. Wen ◽

G. Retscher ◽

...

Keyword(s):

Feature Extraction ◽

Laser Scanning ◽

Low Cost ◽

Point Clouds ◽

Indoor Positioning ◽

Benchmark Dataset ◽

Scanning System ◽

Indoor Environments ◽

Building Management ◽

Common Framework

<p><strong>Abstract.</strong> This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on the elements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building management and navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone indoor positioning methods.</p>

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Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Sensors ◽

10.3390/s18103186 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3186 ◽

Cited By ~ 7

Author(s):

Van Pham ◽

Duc Nguyen ◽

Nhu Dang ◽

Hong Pham ◽

Van Tran ◽

...

Keyword(s):

Low Cost ◽

Indoor Positioning ◽

Measurement Unit ◽

Health Apps ◽

Average Accuracy ◽

Step Counting ◽

Health Monitoring Systems ◽

Commercial Applications ◽

Public Datasets ◽

Indoor Positioning System

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).

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