Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

Zhao, Wenda; Goudar, Abhishek; Schoellig, Angela P.

doi:10.1109/lra.2022.3165181

Cited by 32 publications

(23 citation statements)

References 36 publications

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“…However, the above algorithms are required to optimize anchors' layout for different environments. Using the MSE positioning error, Zhao et al [41] has developed an optimized UWB deployment scheme to balance the geometric and NLOS effects of radio locations, which reduced the twodimensional RMSE positioning error by 47 %. Additional information can also be fused such as fusing IMU and UWB by EKF [42], which can ensure dynamic positioning continuity and reduce the standard deviation (STD) of the location to 1.14 cm.…”

Section: B Nlos Ranging Error Suppression Algorithmmentioning

confidence: 99%

“…The first methodology [34][35][36][37][38] is pivoted on the correction of the I-NLOS components in the dataset and combined with the I-LOS components, which can improve the position accuracy, but requires lots of complicated processing work during the correction. The second methodology [39][40][41][42][43] introduces additional positional information to suppress ranging error. Pedestrian Dead Reckoning (PDR) provides high relative positioning accuracy but subjects to heavy drift in the IMU components.…”

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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: B Nlos Ranging Error Suppression Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2023

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“…However, in [20], the purpose of the evaluation is to demonstrate the effectiveness of the anchor selection method, whereas we quantified the effect of the channel diversity brought by the scheduling method proposed in FlexTDOA. In [22], the authors propose a sensor placement strategy for cluttered environments that is validated through experimental data. A UL TDOA localization system that takes into account NLOS conditions has been proposed and evaluated experimentally in [23].…”

Section: A Scalable Uwb Localizationmentioning

confidence: 99%

“…We also noticed that TDOA localization is more sensitive to a poor anchor placement than TWR localization, which means that the deployment should be done according to a simulation of the expected localization errors for different configurations. Strategies for optimal anchor placement are outside the scope of the paper, but we refer the reader to [22], [39] for analyses of sensor placement in TDOA localization.…”

Section: Number Of Anchorsmentioning

confidence: 99%

FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices

et al. 2023

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The authors would like to acknowledge the support of NXP Romania towards supporting the PhD studies of George-Cristian Pȃtru, as well as providing valuable insights regarding trends for IoT solutions. The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints). The results presented in this article were obtained with the support of the Ministry of European Investments and Projects through the

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“…Indoor positioning technology can be roughly classified into two types, namely, range-free and range-based. Time of arrival (TOA) [ 6 ], time difference of arrival (TDOA) [ 7 ], angle of arrival (AOA) [ 8 ], and received signal strength (RSS) [ 9 ] are examples of traditional range-based approaches. Range-based positioning technology needs to maintain the accuracy of positioning under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Cheng

Niu

Zhang

et al. 2022

Sensors

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With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method.

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Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

Cited by 32 publications

References 36 publications

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

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

FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

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