The Implementation of Precise Point Positioning (PPP): A Comprehensive Review

Elsheikh, Mohamed; Iqbal, Umar; Noureldin, Aboelmagd; Korenberg, Michael

doi:10.3390/s23218874

Cited by 8 publications

(3 citation statements)

References 67 publications

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“…where F2 is the CIR of the second index after the MFP, as shown in Figure 1, and S is the same as in (1). In LOS, the CIR rises rapidly and reaches maximum amplitude within three units after the MFP.…”

Section: Uwb Channel Cir Feature Extractionmentioning

confidence: 99%

“…Indoor high-precision localization is a rapidly growing field, driven by the increasing demand for indoor location-based services such as emergency response, navigation systems, and the Internet of Things (IoT). However, commercial Global Satellite Navigation Systems (GNSSs) [ 1 ] are not designed for indoor location services due to severe interference from building structures. As a result, researchers have explored various techniques for indoor positioning, including fingerprint-matching methods based on Wireless Fidelity (Wi-Fi) [ 2 , 3 ], Bluetooth [ 4 ], or geomagnetism [ 5 ]; ranging positioning methods based on ultra-wideband (UWB) [ 6 , 7 ] and pseudo-satellite systems [ 8 ]; and angle-positioning methods [ 9 ] based on antenna array technology.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification

Yang,

Wang,

et al. 2024

Sensors

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The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory–extended Kalman filter (LSTM-EKF), least-squares–support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms.

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Section: Uwb Channel Cir Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification

Yang,

Wang,

et al. 2024

Sensors

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“…In outdoor environments, GPSs (Global Positioning Systems) can provide high-precision positioning services [ 1 ]. However, within indoor environments, satellite positioning signals are often blocked, making it challenging to obtain positioning information through satellites.…”

Section: Introductionmentioning

confidence: 99%

5G Indoor Positioning Error Correction Based on 5G-PECNN

Yang,

Zhang,

et al. 2024

Sensors

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With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks.

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