3D building model-assisted multipath signal parameter estimation

Kumar, Rakesh; Petovello, Mark G.

doi:10.1109/ipin.2015.7346755

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Measurement domain processing technologies are mainly based on a physical or empirical model to mitigate multipath interference, such as terrain assistance (Kbayer et al, 2018;Kumar et al, 2015;Ng et al, 2021;Zhang et al, 2021), vector tracking (Hsu et al, 2015), Bayesian receiver autonomous integrity monitoring (Pesonen et al, 2011), satellite selection (Blanco-Delgado et al, 2010), wavelet transform (Su et al, 2018), carrier phase multipath mitigation (Moradi et al, 2015), consistency checking (Jiang et al, 2011), Wireless Fidelity (WiFi) aided multipath mitigation (Nur et al, 2013), network-based multipath mitigation (Klimenko et al, 2021), Vondrak filtering (Zheng et al, 2005), empirical mode decomposition (Dai et al, 2014), stochastic state estimation (Zhang et al, 2018), Sparsity-Promoting Regularization (Chen et al, 2019), and Signal to Noise Ratio (SNR) based multipath error correction (Axelrad et al, 1996). However, these methods require external assistance or static long-term observation (Wen et al, 2019(Wen et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%

A multipath mitigation algorithm for GNSS signals based on the steepest descent approach

Qiu

Zeng

et al. 2022

Satell Navig

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Multipath interference seriously degrades the performance of Global Navigation Satellite System (GNSS) positioning in an urban canyon. Most current multipath mitigation algorithms suffer from heavy computational load or need external assistance. We propose a multipath mitigation algorithm based on the steepest descent approach, which has the merits of less computational load and no need for external aid. A new ranging code tracking loop is designed based on the steepest descent method, which can save an early branch or a late branch compared with the narrow-spacing correlation method. The power of the Non-Line-of-Sight (NLOS) signal is weaker than that of the Line-of-Sight (LOS) signal when the LOS signal is not obstructed and with a relatively high Carrier Noise Ratio (CNR). The peak position in the X-axis of the ranging code autocorrelation function does not move with the NLOS interference. Meanwhile, the cost function is designed according to this phenomenon. The results demonstrate that the proposed algorithm outperforms the narrow-spacing correlation and the Multipath Estimated Delay Locked Loop (MEDLL) in terms of the code multipath mitigation and computation time. The Standard Deviation (STD) of the tracking error with the proposed algorithm is less than 0.016 chips. Moreover, the computation time of the proposed algorithm in a software defined receiver is shortened by 24.21% compared with the narrow-spacing correlation.

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Section: Introductionmentioning

confidence: 99%

A multipath mitigation algorithm for GNSS signals based on the steepest descent approach

Qiu

Zeng

et al. 2022

Satell Navig

View full text Add to dashboard Cite

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“…These methods are effective in mitigating narrowband and wideband interference, while increasing software and hardware complexity. There have been several research projects on improvement of positioning accuracy in areas where Non Line-of-Sight (NLOS) reception is a major problem (Kumar and Petovello, 2015; Groves, 2011). A 3D-mapping-aided GNSS approach can significantly improve navigation solutions in locations where satellite signals are blocked.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers

2017

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The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) using pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this approach. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation and real data measurements using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments. Test results showed that the proposed classifiers have an accuracy between 86% and 92%, and the tracking performance improved by about 15%.

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3D building model-assisted snapshot positioning algorithm

Kumar

Petovello

2017

GPS Solut

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3D building model-assisted multipath signal parameter estimation

Cited by 4 publications

References 11 publications

A multipath mitigation algorithm for GNSS signals based on the steepest descent approach

A multipath mitigation algorithm for GNSS signals based on the steepest descent approach

Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers

3D building model-assisted snapshot positioning algorithm

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