Indoor Position Estimation Using Ultrasonic Beacon Sensors and Extended Kalman Filter

Bodrumlu, Tolga; Çalışkan, Fikret

doi:10.3390/ecsa-9-13353

Cited by 3 publications

(6 citation statements)

References 18 publications

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“…Additionally, the study also includes distance measurements, d 1 , d 2 , and d 3 , up to point A. The determination of the coordinates (x, y, z) of point A is equivalent to finding the solutions to the following system of quadratic equations [11][12][13].…”

Section: Solution Based On Three Reference Pointsmentioning

confidence: 99%

“…The general solution to Equation (3) can be represented as follows: The equations in this system can be expressed as follows [11][12][13]:…”

mentioning

confidence: 99%

“…The solution set of this system has two possible interpretations: In one interpretation, points B 1 , B 2 , and B 3 are not in a straight line. In the other interpretation, they are in a straight line [11][12][13]. In this situation, the following propositions hold true:…”

mentioning

confidence: 99%

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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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Section: Solution Based On Three Reference Pointsmentioning

confidence: 99%

“…The general solution to Equation (3) can be represented as follows: The equations in this system can be expressed as follows [11][12][13]:…”

mentioning

confidence: 99%

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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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“…These are divided into three-reference-point-based and more-than-three-reference-point-based solutions. The details of these solution sets are elaborated in [9,10]. In brief, for the first scenario, the solution space varies depending on whether three reference points are in the same line.…”

Section: Geometric Approachmentioning

confidence: 99%

Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data

Bodrumlu,

Çalışkan

2023

Ecsa 2023

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“…However, the variance of any such estimator must be as high as the inverse of the Fisher information [27]. The approaches can be categorized into four proximity-based [28], trilateration-like based [29], triangulation-based [30], or angle of arrival (AOA), and pattern recognition-based [31]. To demonstrate the stability and stress on the accuracy limit of the proposed WLS/TDOA positioning algorithm, the CRLB is deducted and obtained.…”

Section: Cramer-rao Lower Boundmentioning

confidence: 99%

Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue

Yang,

Li,

Pedrycz

2023

Intelligent Automation &Amp; Soft Computing

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Fire rescue challenges and solutions have evolved from straightforward plane rescue to encompass 3D space due to the rise of high-rise city buildings. Hence, this study facilitates a system with quick and simplified onsite launching and generates real-time location data, enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue. Auto-positioning with step-by-step instructions is proposed when launching the locating system, while no extra measuring instrument like Total Station (TS) is needed. Real-time location tracking is provided via a 3D space real-time locating system (RTLS) constructed using Ultra-wide Bandwidth technology (UWB), which requires electromagnetic waves to pass through concrete walls. A hybrid weighted least squares with a time difference of arrival (WLS/TDOA) positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications. The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound (CRLB). The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter (TDC). The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications. The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.

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Indoor Position Estimation Using Ultrasonic Beacon Sensors and Extended Kalman Filter

Cited by 3 publications

References 18 publications

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

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

Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data

Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue

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