Complex Kalman Filter Algorithm For Smartphone-based Indoor UWB/INS Navigation Systems

Brovko, Tatiana A.; Chugunov, Alexander; Malyshev, Alexander P.; Korogodin, Ilya V.; Petukhov, Nikita; Glukhov, O. V.

doi:10.1109/usbereit51232.2021.9455064

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Classification accuracy could be further improved by creating different models for different device types and operators. • Signal diversity: besides Wi-Fi and LTE signals, we are planning to add other types of signal measurements that are available on current phones, such as Bluetooth [27], [28], and UWB [29]. These were not considered in this paper due to limited deployments, but in future these may become more sidespread.…”

Section: Discussionmentioning

confidence: 99%

ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

et al. 2022

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Future spectrum sharing rules very likely will be based on device environment: indoors or outdoors. For example, the 6 GHz rules created different power regimes for unlicensed devices to protect incumbents: "indoor" devices, subject to lower transmit powers but not required to access an Automatic Frequency Control database to obtain permission to use a channel, and "outdoor" devices, allowed to transmit at higher power but required to do so to determine channel availability. However, since there are no reliable means of determining if a wireless device is indoors or outdoors, other restrictions were mandated: reduced power for client devices and indoor access points that cannot be battery powered, have detachable antennas or be weatherized. These constraints lead to sub-optimal spectrum usage and potential for misuse. Hence, there is a need for robust identification of device environments to enable spectrum sharing. In this paper we study automatic indoor/outdoor classification based on the radio frequency (RF) environment experienced by a device. Using a custom Android app, we first create a labeled data set of a number of parameters of Wi-Fi and cellular signals in various indoor and outdoor environments, and then evaluate the classification performance of various machine learning (ML) models on this data set. We find that tree-based ensemble ML models can achieve greater than 99% test accuracy and F1-Score, thus allowing devices to self-identify their environment and adapt their transmit power accordingly.

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Section: Discussionmentioning

confidence: 99%

ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

et al. 2022

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“…From this, we can derive that if there are N available satellites in the current epoch, the pseudorange observation equation in error form is given by Equation ( 9), which can be simplified as Equation (10):…”

Section: Gnss Pseudorange Observation Equationmentioning

confidence: 99%

“…L gnss = H gnss •δX (10) Using information such as the elevation angle-based weighting and signal-to-noise ratio, the observation noise matrix R gnss can be defined as:…”

Section: Gnss Pseudorange Observation Equationmentioning

confidence: 99%

“…Addressing the aforementioned issues, UWB positioning technology is considered as an effective complementary means in GNSS-denied environments, due to its ability to provide high-precision indoor/outdoor positioning, strong anti-interference, excellent penetration power, low power consumption, and insensitivity to obstruction by overhead obstacles [9,10]. The currently developed GNSS/UWB/INS integrated system has emerged as a reliable positioning solution for complex outdoor environments [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Sun,

Gao,

Tao

et al. 2024

Remote Sensing

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Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively.

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A survey of indoor positioning systems based on a six-layer model

Sartayeva,

Chan,

et al. 2023

Computer Networks

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Complex Kalman Filter Algorithm For Smartphone-based Indoor UWB/INS Navigation Systems

Cited by 11 publications

References 16 publications

ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

A survey of indoor positioning systems based on a six-layer model

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