Multipath Mitigation Method Research Articles

IRNSS/NavIC software receiver design with squared correlators code discriminator (SCCD) multipath mitigation method

IRNSS/NavIC software receiver design with squared correlators code discriminator (SCCD) multipath mitigation method

On the GPS signal multipath at ASG-EUPOS stations

Abstract The accuracy of the results of satellite measurements is influenced by many factors. One of them is the multipath phenomenon resulting from reflections of the satellite signal, mainly from objects in the vicinity of the GNSS antenna. The multipath remains a domain source of ranging errors in satellite positioning. Despite the groups of multipath mitigation methods, it is impossible to totally eliminate the influence of this error on the measurement results. This error has two main effects in the case of carrier phase differential positioning. First, the multipath increases the initial search space for correct ambiguities. Secondly, the accuracy of the vector solution between the reference station and the rover receiver is affected. The authors of this article analyzed the presence of multipath in the Polish network of permanent GNSS stations ASG-EUPOS. Data from the year 2021 were used for the analysis. Two computational strategies were adopted to determine the multipath. The pseudorange multipath observable combination (MP) for L1, L2 and L5 signals was used for code measurements. In the case of the carrier phase, multipath analyses of double-differenced L1, L2, and L5 carriers between neighbouring stations were performed. Based on the research, the average multipath level for the Polish GNSS reference stations network was determined. Stations where the levels of particular combinations exceed the assumed values were successively determined. Finally, multipath models in the form of sidereal maps were created. Based on these models, six stations were identified and recommended for further analysis concerning the impact of multipath on GNSS measurements.

A GPS multipath mitigation method in coordinate-domain considering the effects of gross errors and missing data

Multipath is a significant obstacle to achieving high-precision deformation monitoring using the Global Positioning System (GPS) technique. Sidereal filtering (SF) can mitigate GPS multipath effects based on the multipath models derived from historical measurements, but its practical application in complex scenarios is still challenging due to unavoidable gross errors and missing data problems. In this paper, an improved SF algorithm is proposed that accurately extracts multipath immune to the effects of gross errors and missing values in historical measurements, aiming to obtain accurate displacements in a harsh environment. Specifically, a gross error detection method is first developed based on the dynamic time warping (DTW) algorithm to filter gross errors. Then, we adopt the multichannel singular spectrum analysis (MSSA) algorithm to extract initial multipath errors. Finally, we propose a weighted fusion (WF) algorithm to eliminate the impact of missing values and obtain the final multipath sequences. Extensive experiments with datasets collected from a real-world landslide, including both stable and sliding stations, demonstrate the effectiveness of our proposed method. Results show that compared with the traditional SF method, our proposed method can effectively mitigate the effects of gross errors and missing values on multipath modeling, with the multipath filtering accuracy improving by approximately 12% and 21% for stable and sliding scenarios, respectively.

Machine learning-based multipath modeling in spatial domain applied to GNSS short baseline processing

Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: They are either not easy to use or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modeling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total, five short baselines were formed and multipath errors were extracted from the postfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data and data from a low-cost device. We conclude that the ML-based multipath mitigation method is effective, easy to use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio, during model training.

An improved joint modeling method for multipath mitigation of GPS, BDS-3, and Galileo overlapping frequency signals in typical environments

An improved joint modeling method for multipath mitigation of GPS, BDS-3, and Galileo overlapping frequency signals in typical environments

Pseudolite Multipath Estimation Adaptive Mitigation of Vector Tracking Based on Ref-MEDLL

Among many indoor positioning technologies, pseudolite positioning technology has become an important supplement to GNSS. In indoor open environments, pseudolite positioning technology can usually perform high-precision positioning. However, in a complex environment, the pseudolite receiver is seriously interfered by multipath and other interference signals, which will lead to a serious decline in the observation accuracy, signal lock loss or even no positioning results. Therefore, this work proposes a pseudolite indoor anti-multipath receiver based on reference multipath estimating delay lock loop (Ref-MEDLL). It adds the Ref-MEDLL multipath estimator module and the multipath mitigation module to the traditional receiver signal processing architecture. In the multipath mitigation module, the multipath estimation adaptive mitigation of vector tracking (MEAM-VT) method and the multipath estimation direct mitigation (MEDM) method for multipath mitigation is proposed. Experimental results show that the Ref-MEDLL multipath estimation method has good adaptability to multipath signals that have different time delays and different amplitudes; both the MEDM receiver and the MEAM-VT receiver have good multipath mitigation performance. The MEAM-VT method performs better than the MEDM method in multipath mitigation and tracking, but the stability of the pseudorange observations of the MEAM-VT method is not as good as that of the MEDM method. The positioning accuracy of the MEDM receiver and the MEAM-VT receiver has been improved to different degrees in static positioning experiments and dynamic positioning experiments.

A satellite orbit maneuver detection and robust multipath mitigation method for GPS coordinate time series

Multipath effect is the main factor limiting the high-precision positioning of Global Navigation Satellite System (GNSS) because it cannot be eliminated by double-difference observations or existing empirical models. Although the receiver antenna technology can reduce the multipath effect, it also brings the cost burden and cannot effectively solve the short-delay multipath error of carrier phase observation. Sidereal filtering (SF) is a common method used to mitigate multipath effect in static positioning mode. However, the coordinate time series may be contaminated by outliers, and satellite orbits may be affected by maneuvers. These will lead to inaccurate estimation of the Multipath Repetition Period (MRP), and even deteriorate the multipath mitigation effect of the SF. To solve these problems, a Sidereal Filtering method with the Satellite Maneuver Detection and Robustness (SFSMDR) is proposed. In this method, based on the Orbital Repeat Time Method (ORTM), the Multipath Repetition Period with the Satellite Maneuvers Detection (MRPSMD) is determined. Furthermore, considering the robustness of median filtering to outliers, the coordinate time series of the first day were preprocessed by the median filtering. After applying the proposed SFSMDR method, experimental results show that the proposed SFSMDR can effectively detect satellite maneuvers. In the absence of satellite maneuvers, there is no need to re-estimate MRP, which can save computational cost to a certain extent. When the coordinate time series on the first day is contaminated by outliers, the positioning accuracy of the MRPSMD for the east and north directions, is decreased by 2.26% and 95.73%, respectively. In contrast, the positioning accuracy of the SFSMDR is increased by 46.21% and 52.71%, respectively.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone's ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

Wavelet and Neural Network-Based Multipath Detection for Precise Positioning Systems

Multipath errors are significantly challenging in radio navigation systems. In particular, multipath errors in indoor environments cause significant errors in the position domain because not only the building materials that surround the environment but also all objects inside the building can reflect the navigation signals. Multipath errors in outdoor environments, such as in global navigation satellite system (GNSS) signal applications, have been widely studied for precise positioning. However, multipath studies for indoor applications have rarely been conducted because of the complicated environment and the many objects made of various materials in small areas. In this study, multipath mitigation methods using a shallow neural network and a transfer learning-based deep neural network were respectively considered to overcome the complexity caused by the reflected signals in indoor environments. These methods classify each measurement according to whether the measurement exhibits a severe multipath error. Carrier-phase measurements broadcasted from the transmitter were used for the wavelet transform, and the magnitude values after the transform were used for neural network-based learning. Shallow and deep networks attain approximately 87.1% and 85.6% detection accuracies, respectively, and the positioning error can be reduced by 10.4% and 9.4%, respectively, after multipath mitigation.

Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator

Tracking ambiguity and multipath are two serious threats in processing binary offset carrier (BOC) signals that are widely used in global navigation satellite systems (GNSS). Three-loop tracking methods, such as dual binary phase-shift keying tracking (DBT), use a delay lock loop (DLL), a phase lock loop (PLL), and a subcarrier PLL (SPLL) to track the code, carrier, and subcarrier, respectively, thus solving the tracking ambiguity problem. However, the multipath remains an important factor in the ranging performance deterioration of these tracking methods. Using the offset correlator (OC) technique in the SPLL can effectively mitigate subcarrier multipath errors, but it substantially raises thermal noise errors. To solve this contradiction, this paper proposes a prompt-assisted-offset correlator (PAOC) technique that combines the prompt and offset correlators in a complementary way to mitigate multipath for BOC signals. Compared with the original OC technique, the PAOC technique has less thermal noise performance loss. Moreover, this paper discovers and quantifies the interaction between carrier and subcarrier multipath errors by analyzing the coupling effect between SPLL and PLL. Most multipath mitigation methods for BOC signals ignore the carrier multipath in the PLL, so their subcarrier multipath performance is not optimal. Thus, this paper further proposes applying the PAOC technique in both PLL and SPLL to mitigate carrier and subcarrier multipath errors simultaneously. Theoretical analysis and experimental results demonstrate that the proposed method can significantly improve the multipath performance with small noise performance loss.

Random walk multipath method for Galileo real-time phase multipath mitigation

Random walk multipath method for Galileo real-time phase multipath mitigation

Multipath Mitigation Methods of BOC-Family Signals Based on Dual BPSK Tracking Techniques

It is well known that for binary offset carrier (BOC) signals the notorious ambiguity problem due to the multipeaked autocorrelation function must be tackled first in order to obtain the potential of improving ranging performance promised by its broader Gabor bandwidth. The existence of multipath makes it more difficult to solve. Dual binary phase-shift keying (BPSK) tracking (DBT) is a kind of low complexity, unambiguous tracking method for BOC-family signals, which makes full use of the coherence between the upper and lower sidebands and employs subcarrier phase locked loop (SLL) and delay locked loop (DLL) to track subcarrier and code components, respectively. Considering that the thermal noise performance of DBT has been fully studied, this article focuses on the analysis of multipath performance and the design of antimultipath methods under DBT framework. The influence of multipath on SLL and DLL of DBT is analyzed first and the corresponding subcarrier and code multipath error envelopes are obtained. Then according to different requirements of subcarrier and code multipath errors, a multipath mitigation scheme specially designed for DBT is proposed, in which SLL and DLL adopt offset correlator and narrow correlation, respectively. Theoretical analysis and simulation are given to verify the effectiveness of the proposed methods.

Indoor Positioning With Adaptive Wavelet Denoise Enhancement and Trend Surface Analysis Based Multipath Map

The channel multipath and non-line-of-sight (NLOS) reflections introduce a bias on the time difference of arrival (TDOA) observations, which reduce the accuracy of the ultra-wideband (UWB) positioning performance in an indoor environment. In previous research, the multipath map (MPM) method is based on spatial domain modeling principle in the reverse positioning framework and is used to mitigate the multipath effect. Utilizing the mean value of the multipath residuals as the calibration value will inevitably lead to insufficient or excessive multipath modification in the grid. By combining the trend surface analysis method, this paper proposes an advanced multipath mitigation method named trend surface analysis based multipath map (TMPM). Meanwhile, this paper propose an adaptive wavelet denoise (AWD) method in order to eliminate the noise of data set in advance. We mainly demonstrate the effect of TMPM in reverse positioning scenarios, analyze the multipath correction effect of TMPM. The experimental results show that the accuracy of the tag positioning is 16.0 cm with TMPM, which is improved by 22.7% compared to that of MPM. In dynamic positioning case, results with TMPM also show benefit over MPM.

A multipath mitigation method in long-range RTK for deformation monitoring

A multipath mitigation method in long-range RTK for deformation monitoring

An improved time-domain multipath mitigation method based on the constraint of satellite elevation for low-cost single frequency receiver

Multipath effect seriously affects the accuracy of GNSS high precision positioning since it cannot be corrected by empirical model or eliminated by difference techniques. Sidereal filtering method can effectively mitigate multipath error for static positioning by using the characteristics of satellite time-domain repetition. The key of this method is to extract accurate multipath model on the reference day and mitigate multipath error by searching the multipath model accurately on the subsequent observation day. We analyze the relationship between the multipath error and satellite elevation first by using the data sets which collected from the Multi-GNSS Experiment (MGEX) stations. Based on the result, an improved time-domain sidereal filtering multipath mitigation method based on satellite elevation constraints is proposed. The information of satellite elevation is used to constrain the threshold strategy and decomposition layer of wavelet packets transform, which can effectively improve the denoising effect and extract a more precision multipath model. In addition, the performance of positioning of the proposed method at different mask angles is also evaluated. Real short baseline data sets are collected by low-cost single frequency receivers to evaluate the performance of the positioning of the proposed method, experimental results show that using the proposed method in multipath mitigation can improve approximately 25.00, 21.43 and 13.98% in the east, north and up components compared with the original results, respectively. Even compared with the traditional wavelet packets transform method, the improvement rate can reach approximately 10.78% for 3D positioning. Moreover, the proposed method can also improve the performance of positioning at different mask angles to a certain degree, especially for the low mask angle seriously affected by multipath effect.

GNSS Multipath Mitigation Method Based on K-means Classification in Urban Environment

GNSS Multipath Mitigation Method Based on K-means Classification in Urban Environment

Extended Double-delta Correlator Technique for GNSS Multipath Mitigation

Multipath is one of the main error sources in global navigation satellite system (GNSS). Many multipath mitigation methods have been proposed, such as the narrow correlator technique and the double-delta (DD) correlator technique. In light of the BPSK modulated signals used in many GNSS, an extended DD (EDD) correlator technique expanding from the DD correlator, which uses multiple pairs of correlators with more general spacing and weight, is proposed in this paper. Furthermore, the paper provides the discriminator structure, the closed-form expressions for multipath error envelope under infinite bandwidth and the closed-form expressions for the relationship between multipath error, thermal noise performance and design parameters of the discriminator. Based on the explicit performance expressions, the appropriate selection principles of parameters are given and verified by theoretical analysis and simulation. The results show that the proposed method can improve the performance optimization freedom of the DD technique without significantly increasing the design complexity. Moreover, the multipath performance is better than DD under infinite bandwidth and has great adaptability to limited bandwidth and the next generation GNSS signals, such as BOC and MBOC. The EDD technique can also be utilized as a noncoherent discriminator and has better anti-multipath performance. Furthermore, the EDD technique can achieve balance between multipath mitigation performance and thermal noise performance by adjusting correlator spacing and weight and can be self-adaptive to multipath relative amplitude in the environment.

An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

Enhanced multipath mitigation method based on multi-resolution CNR model and adaptive statistical test strategy for real-time kinematic PPP

This study proposes an enhanced multipath mitigation method based on multi-resolution carrier-to-noise-ratio (CNR) model and adaptive statistical test strategy for real-time kinematic precise point positioning (PPP) applications. The multi-resolution CNR model is established with GPS observation data collected from DOY 152 to 181 of 2019 by 230 globally distributed IGS stations, which used to analyze the relevant factors affecting CNR. Statistical results indicate that the CNR is not only related to the satellite elevation, but also closely related to the receiver types and specific satellite. The maximum difference between different receivers can reach 20 dB for the same satellite at the same elevation. In addition, the performance of the CNR is also obviously different between each satellite, and the maximum difference between different satellites is about 10 dB for the same receiver at the same elevation. Hence, in terms of the method which is based on CNR information for multipath detection and mitigation, the independence of receiver types, satellite and frequency must be considered. With the above analysis, this study developed a multi-resolution CNR model based on different receiver types, different satellites and different elevation firstly. Then, combined with the adaptive statistical test strategy which is based on the difference of CNR between inter-frequency and the difference of CNR between adjacent epochs, the multipath can be detected effectively. For the epoch which affected by multipath, the down-weighted strategy based on CNR is adopted to mitigate the influence of multipath on positioning. Real-time kinematic PPP data are collected to assess the proposed method, and the results demonstrate that the proposed method can detect the multipath effectively, and the detection rate can reach 90.28%. Moreover, after adopting the mitigation strategy, the RMS bias of the east, north and up components are improved about 19.95%, 17.89% and 23.07% compared to the original results, respectively. It is worth noting that this proposed method is also suitable for other GNSS, such as GLONASS and BDS, but the corresponding CNR model must be established simultaneously.

Site-specific real-time GPS multipath mitigation based on coordinate time series window matching

Multipath effect is one of the main errors in precise global navigation satellite system (GNSS) positioning. Although there are many GNSS multipath mitigation models available, each model has some limitations. We discuss the problem of mitigating low-frequency multipath in a specific site from the coordinate domain. At present, sidereal filtering is one of the most commonly used methods, in which the repetition period of multipath has to be estimated before multipath mitigation. However, in such a method, the maneuver of the satellite orbit and the long ephemeris update interval will cause inaccurate estimation of the repetition period. We propose a variation of the sidereal filtering method based on window matching. In that method, the window of coordinate time series formed by the current epoch and several previous epochs is used as the query window, and the matched window is searched in the time series of the previous day. The degree of similarity between windows is evaluated by similarity measures. Based on the coordinate point pairs in the matching window, multipath mitigation of the current epoch is performed by an affine transformation. The effects of different similarity measures in window matching are explored, and an early and late matching algorithm is proposed to solve the ambiguous problem of multiple matching sequences in integer-valued similarity metrics. Two sets of short baseline data were collected by different types of receivers in harsh environments to validate the method. The results show the feasibility and effectiveness of the proposed method for site-specific real-time multipath mitigation.