An Adaptive Filter Approach for GPS Multipath Error Estimation and Mitigation

Swathi, N.; Dutt, V. B. S. Srilatha Indira; Rao, G. Sasibhushana

doi:10.1007/978-81-322-2728-1_50

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…The ninetieth percentile of positioning error of common handheld GPS devices was previously reported as 13.1 m in high forest [34]. The GPS error under a tree canopy increases due to unfavorable satellite constellation geometry, atmospheric interference, multi-path effect, and lower signal-to-noise ratio [35,36]. Since the positioning error can far exceed the inventory plot radius (7.3 m), it is possible that there is minor or no overlap between an actual and a nominal plot area in extreme cases.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

Jung

Shao

et al. 2022

Remote Sensing

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Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R2 = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage.

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Section: Discussionmentioning

confidence: 99%

High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

Jung

Shao

et al. 2022

Remote Sensing

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“…Not only does it save costs, but it also greatly weakens the multipath errors. Swathi et al [4] proposed a multipath correction method based on adaptive filtering, which avoids the problem of losing the effective signal when denoising is applied to the observed data. However, its overall efficiency is relatively low and its performance is unstable.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Anti-Multipath Algorithm for GNSS-RTK Monitoring Application

Wang,

Sun

2023

Sensors

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During short baseline measurements in the Real-Time Kinematic Global Navigation Satellite System (GNSS-RTK), multipath error has a significant impact on the quality of observed data. Aiming at the characteristics of multipath error in GNSS-RTK measurements, a novel method that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and adaptive wavelet packet threshold denoising (AWPTD) is proposed to reduce the effects of multipath error in GNSS-RTK measurements through modal function decomposition, effective coefficient sieving, and adaptive thresholding denoising. It first utilizes the ICEEMDAN algorithm to decompose the observed data into a series of intrinsic mode functions (IMFs). Then, a novel IMF selection method is designed based on information entropy to accurately locate the IMFs containing multipath error information. Finally, an optimized adaptive denoising method is applied to the selected IMFs to preserve the original signal characteristics to the maximum possible extent and improve the accuracy of the multipath error correction model. This study shows that the ICEEMDAN-AWPTD algorithm provides a multipath error correction model with higher accuracy compared to singular filtering algorithms based on the results of simulation data and GNSS-RTK data. After the multipath correction, the accuracy of the E, N, and U coordinates increased by 49.2%, 65.1%, and 56.6%, respectively.

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“…Since the GPS is a widespread network, the environmental factors severely affect the performance of the GPS model. The researchers have introduced the space correlation–based techniques in the GPS for diminishing the environmental effects . Besides, environmental factors, ie, the multipath and the jamming, are major error sources affecting the performance of the GPS.…”

Section: Introductionmentioning

confidence: 99%

Multipath mitigation in GPS receiver using Taylor integrated bidirectional least mean square algorithm

Siridhara

Ratnam

2019

Trans Emerging Tel Tech

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Research interest in Global Navigation Satellite System (GNSS) is increasing in the past few years due to its wide applicability in many application-oriented environments, such as automobile navigation system and aircraft landing system. Global Positioning System (GPS) is one of the GNSSs having high precision navigation performance. Multipath is a dominant error source that occurs in several GPS applications, limiting the performance. To reduce the multipath effects of satellite signals, an effective multipath mitigation technique is required. This paper introduces the Taylor series-based bidirectional least mean square (Taylor-BLMS) beamforming model for the multipath mitigation problems occurring in the GPS receiver. The performance metrics, such as antenna gain and bit error rate (BER), measure the performance of the proposed Taylor-BLMS model against various comparative models. For the system with six sensors, the proposed Taylor-BLMS model has the best performance with the values of 23.460 dB and 4.78E−05 for the antenna gain and the BER, respectively. For the system with 1 jammer, the proposed Taylor-BLMS model has the antenna gain and the BER values of 22.2828 dB and 3.69E−05, respectively. Trans Emerging Tel Tech. 2019;30:e3760. wileyonlinelibrary.com/journal/ett

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An Adaptive Filter Approach for GPS Multipath Error Estimation and Mitigation

Cited by 12 publications

References 2 publications

High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

A Multi-Scale Anti-Multipath Algorithm for GNSS-RTK Monitoring Application

Multipath mitigation in GPS receiver using Taylor integrated bidirectional least mean square algorithm

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