A novel UWB channel identification parameter for indoor positioning applications

Khodjaev, Jasurbek; Park, Yongwan; Choi, Jaemin

doi:10.1007/s12243-010-0217-8

Cited by 2 publications

(3 citation statements)

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“…The delay-spread estimate [24] of 𝜏 𝑟𝑚𝑠 can ensure classification performance when the distance is unknown, and hence improve positioning accuracy once TOF and received signal strength are further utilized. Jasurbek et al [5] uses effective and dominant path number as a new CCP feature to reduce the complexity of the parameter set on the premise of ensuring accuracy. However, the recall of LOS is universally lower than the recall of NLOS.…”

Section: A Los/nlos Identification Algorithmsmentioning

confidence: 99%

“…In NLOS conditions, TFP is weakened by obstacles or hidden in ENS. It is recorded in S of (5), which increases L and causes the RFP used to calculate TOF to be later than TFP, resulting in a positive offset-ranging error. Traditionally, location methods that combined corrected I-NLOS and I-LOS ranging have the following problems.…”

Section: Ranging Error Compensation and Position Estimationmentioning

confidence: 99%

“…This leads to poor positioning accuracy. To realize indoor localization, extensive research has been carried out on various localization technology such as Wireless Fidelity (Wi-Fi) [2], Bluetooth [3], geomagnetism [4], Ultra-wideband (UWB) [5][6][7][8], pseudo-satellite [9]. Among these approaches, UWB has attracted extensive attention due to its large transmission bandwidth that leads to high localization accuracy resolution, which can meet the requirements of indoor highprecision positioning.…”

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confidence: 99%

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UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

et al. 2023

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Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response's (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibilitybased support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms.

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Section: A Los/nlos Identification Algorithmsmentioning

confidence: 99%

Section: Ranging Error Compensation and Position Estimationmentioning

confidence: 99%