An UWB Cyclostationary Detection Algorithm Based on Nonparametric Cusum

Song, Xiaoou; Wang, Xiaorong

doi:10.1007/978-3-030-50399-4_10

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Nonetheless, this method is vulnerable to random non-Gaussian outliers with high energy. Song et al [ 43 ] converted the signal amplitude into statistical rank information to suppress heavy-tailed non-Gaussian noise or outliers while effectively preserving TFP data. The algorithm re-evaluates the TFP by setting a threshold, combining the row-rank statistical sequence of the signal frame, and integrating a multipath signal detection algorithm to enhance performance.…”

Section: Related Workmentioning

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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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: Related Workmentioning

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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“…However, the correction effect in some points is detrimental due to outliers. Converting the amplitude information into rank statistics can weaken outliers' interference and effectively maximize the FP's information [36]. TFP is estimated using the row rank statistical sequence of each frame's amplitude and combines maximum likelihood to improve performance.…”

Section: B Nlos Ranging Error Suppression Algorithmmentioning

confidence: 99%

“…Typically, the algorithms to suppress ranging errors' influence on location can be divided into two methodologies. 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.…”

mentioning

confidence: 99%

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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A CEEMDAN-CA Detector for UWB Pulse Signal in Low SNR

Hong

2021

2021 28th International Conference on Telecommunications (ICT)

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An UWB Cyclostationary Detection Algorithm Based on Nonparametric Cusum

Cited by 3 publications

References 5 publications

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

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

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

A CEEMDAN-CA Detector for UWB Pulse Signal in Low SNR

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