Coin-GPS

In this paper we propose a global positioning algorithm of multiple assets based on Received Signal Strength (RSS) measurements that takes into account the gain uncertainties of each hardware transceiver involved in the system, as well as the uncertainties on the Log-Distance Path Loss (LDPL) parameters. Such a statistical model is established and its Maximum Likelihood Estimator (MLE) is given with the analytic expression of the Cramér-Rao Lower Bound (CRLB). Typical values of those uncertainties are given considering whether calibration is done in production, in situ, or if hardware is used uncalibrated, in order to know what is the expected accuracy in function of the calibration setup. Results are tested by numerical simulations and confronted to real measurements in different room configurations, showing that the theoretical bound can be reached by the proposed MLE algorithm.

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“…In this case the FIM can only be obtained in a numerical way. Taking the assumption of a well-calibrated path-loss exponents (14) and (11) gives…”

Section: Log-likelihood Expressionmentioning

confidence: 99%

“…Indoor positioning using GNNS requires additional infrastructure because of the poor signal level; the monolithic solutions [11][12][13][14] can be very precise but the need of a great number of external antenna does not meet most economical requirements.…”

Section: Introductionmentioning

confidence: 99%

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

et al. 2019

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“…Therefore, studies have begun on position estimation for indoor environments. There are lots of different approaches related to indoor positioning, such as those that are motion-captured-based, lidar-based, Wi-Fi-based, Bluetooth-based, ultra-wideband (UWB)-based, and IMU-based [1][2][3]. Motioncapturing-based systems like VICON and OptiTrack employ multiple high-speed cameras to determine the position of an object relative to one another; however, these systems have a drawback in that they require a complex setup and challenging calibration.…”

Section: Introductionmentioning

confidence: 99%

3D Indoor Position Estimation Based on a UDU Factorization Extended Kalman Filter Structure Using Beacon Distance and Inertial Measurement Unit Data

Bodrumlu,

Caliskan

2024

Sensors

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The development of the GPS (Global Positioning System) and related advances have made it possible to conceive of an outdoor positioning system with great accuracy; however, for indoor positioning, more efficient, reliable, and cost-effective technology is required. There are a variety of techniques utilized for indoor positioning, such as those that are Wi-Fi, Bluetooth, infrared, ultrasound, magnetic, and visual-marker-based. This work aims to design an accurate position estimation algorithm by combining raw distance data from ultrasonic sensors (Marvelmind Beacon) and acceleration data from an inertial measurement unit (IMU), utilizing the extended Kalman filter (EKF) with UDU factorization (expressed as the product of a triangular, a diagonal, and the transpose of the triangular matrix) approach. Initially, a position estimate is calculated through the use of a recursive least squares (RLS) method with a trilateration algorithm, utilizing raw distance data. This solution is then combined with acceleration data collected from the Marvelmind sensor, resulting in a position solution akin to that of the GPS. The data were initially collected via the ROS (Robot Operating System) platform and then via the Pixhawk development card, with tests conducted using a combination of four fixed and one moving Marvelmind sensors, as well as three fixed and one moving sensors. The designed algorithm is found to produce accurate results for position estimation, and is subsequently implemented on an embedded development card (Pixhawk). The tests showed that the designed algorithm gives accurate results with centimeter precision. Furthermore, test results have shown that the UDU-EKF structure integrated into the embedded system is faster than the classical EKF.

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“…However, many of these methods are not suitable for animal localisation, especially for small animals housed indoors. For instance, due to the external walls of buildings, the Global Positioning System (GPS) cannot localise indoor objects [14], INS may be susceptible to errors requiring sophisticated filtering techniques such as the Kalman filter [15] and highly precise magnetic technology is sensitive to conductive and ferromagnetic materials at low frequencies [16].…”

Section: Introductionmentioning

confidence: 99%

Validation of an IoT System Using UHF RFID Technology for Goose Growth Monitoring

Černilová,

Linda,

Kuře

et al. 2023

Preprint

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Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimise the negative aspects associated with measuring in contact with humans. The designed system aims to incorporate modern approaches in animal husbandry and utilise the acquired data for the potential development of breeding approaches for various animal breeds. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 93.87%, specificity of 99.94%, accuracy of 99.85%, and precision of 95.09%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813.

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Coin-GPS

Cited by 78 publications

References 35 publications

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

3D Indoor Position Estimation Based on a UDU Factorization Extended Kalman Filter Structure Using Beacon Distance and Inertial Measurement Unit Data

Validation of an IoT System Using UHF RFID Technology for Goose Growth Monitoring

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