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Session B6: Emerging PNT Consumer Applications

GNSS Positioning with Multicorrelators as an Alternative to Direct Position Estimation: How Good Can It Get?
Sergio Vicenzo, Chin Lok Tsang, Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University; Shuo Tang, Department of Electrical and Computer Engineering, Northeastern University; Bing Xu, Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University; Pau Closas, Department of Electrical and Computer Engineering, Northeastern University
Location: Prince David Room
Date/Time: Thursday, Apr. 16, 2:11 p.m.

The Global Navigation Satellite System (GNSS) was introduced with the introduction of Global Positioning System (GPS) from the United States. In the last several decades, applications of GNSS have been rising rapidly following the growth of Internet of Things (IoT) technologies, such as smartphones, self-driving cars, and unmanned aerial vehicles (UAVs). With growing demand for reliable and accurate positioning from GNSS, research into improving the performance of GNSS has been increasingly under the spotlight. Among the most major problems for GNSS positioning is the existence of Multipath (MP) and non-line-of-sight (NLOS) reception.
While the issue of Multipath (MP) and non-line-of-sight (NLOS) is generally at manageable levels in open-sky or light urban environments, where limited urban structures permit a high sky visibility for the receiver, allowing MP and NLOS errors to be easily mitigated from a generally high number satellites in view. But in highly economically developed areas, such as Hong Kong (such as Central and Admiralty), Tokyo (such as Ginza and Shinjuku), and downtown Jakarta (such as Jalan Jenderal Sudirman), high rise buildings typically not only block the satellites in view, generating NLOS reception, but also generates additional reflected the signals to the non-blocked satellites, causing MP reception.
Research into solving the issue of MP and NLOS has been long and is continuously on the rise. On the signal processing-level, Multipath Mitigation Technology (MMT) was introduced as an efficient Maximum Likelihood Estimator (MLE) to correctly estimate both the LOS and reflected path signal parameters, namely the code delays, carrier phase, and amplitudes (Weill, 2002, 2003). Multipath Estimating Delay Lock Loop (MEDLL) was also proposed as a similar MLE MP estimating algorithm which makes use of multicorrelators in two-step positioning (2SP) tracking to iteratively subtracts the reflected signal elements from the Autocorrelation function (ACF), allowing the code discriminators to correctly estimate the LOS code delay (Townsend et al., 1995; Van Nee, 1992; Van Nee et al., 1994). A Coupled Amplitude Delay Lock Loop (CADLL) architecture was also recently introduced, which made use of an additional Amplitude Lock Loop (ALL) in addition to the classic Delay Lock Loop (DLL) to separately track the LOS and reflected signal components (Chen et al., 2013; Chen et al., 2011). Other various MP mitigations have also been proposed here (Jia et al., 2017; Kubo et al., 2005; Kumar & Singh, 2019; McGraw & Braasch, 1999; Qiu et al., 2022; Weill, 1995).
Conversely, the problem of NLOS is typically difficult to detect on the intermediate frequency (IF)-level, especially for scalar tracking loop (STL) two-step positioning (2SP) receivers. Previously, an NLOS detection method was introduced for Vector Tracking Loop (VTL) receivers (Hsu et al., 2015). This was later further refined into a detection and correction algorithm (Xu et al., 2020). For STL-based receivers, 3D mapping-aided (3DMA) GNSS or 3D Light Detection and Ranging (LiDar) are usually employed to detect and correct the NLOS errors (Ng et al., 2022; Ng et al., 2019, 2020; Wen et al., 2018; Wen et al., 2019).
Direct Position Estimation or DPE, on the other hand, is a relatively new MLE positioning algorithm. It was found to be naturally robust against MP and NLOS reception in comparison with 2SP, such as STL and VTL (Closas et al., 2007a). DPE uses real IF signal correlations to solve the navigation solution directly in the navigation domain (Closas & Gao, 2020). DPE performance against MP was extensively investigated in (Closas et al., 2009a, 2009b; Gusi?Amigó et al., 2018; Tang et al., 2024), The positioning accuracy of DPE usually outperforms 2SP in unban environments, in the cases when the LOS signal is stronger (Bialer et al., 2013; Di et al., 2024; Xie & Petovello, 2015).
Its NLOS mitigation performance was also found when the majority of signals are LOS, as the global maxima is contributed mainly by the LOS signals (Amar & Weiss, 2005; Bialer et al., 2013; Vicenzo & Xu, 2025; Vicenzo et al., 2024). Though research into mitigating NLOS for DPE has been previously investigated in (Ng & Gao, 2016; Strandjord et al., 2020), this research will only focus on utilizing DPE performance against MP, and DPE is regarded as naturally robust against NLOS.
But computation burden of DPE cost function has been a bottleneck for the commercialization of DPE. This high computational complexity comes from the use of real signal correlations. In 2SP, three correlators, namely the Early, Prompt, and Late correlators are employed in tracking to act as inputs for the discriminator. Contrarily, as DPE cost function is non-differentiable, depending on the non-convex optimization method used, such as the grid-based method, it would take at least hundreds of correlations for DPE positioning to converge.
Several research has delved into optimizing DPE computational performance. For iterative methods, among the most widely known method for DPE is using Accelerated Random Search (ARS), which is a variant of Pure Random Search (PRS) that iteratively narrows the search space around the current best position, velocity, and time (PVT) estimate, allowing it to converge much faster compared to PRS (Appel et al., 2004; Closas et al., 2009a). The Space Alternating Generalized Expectation Maximization (SAGE) was also previously introduced, which iteratively estimates one by one the parameters of the PVT while keeping the rest fixed (Closas et al., 2007b). But the use of ARS still requires hundreds iterations to converge, which means hundreds of correlations in each positioning epoch (Tang et al., 2023). And SAGE though promising, was found to be sensitive to the PVT initialization, which may not always be reliable, especially when initialized with 2SP positioning in urban environments (Closas et al., 2007b).
For grid-based methods, an iterative coarse-to-fine grid-search algorithm was first proposed in (Axelrad et al., 2011). The basic idea is to solve DPE cost function by first generating a grid of candidate PVTs of large search space and spacings (coarse spacings) and gradually narrows the search space and spacings to iteratively obtained more refined PVT estimates. This methodology was later further refined in (Cheong, Wu, & Dempster, 2012; Li et al., 2014; Narula et al., 2014).
The use of grid-based method is typically accompanied with approaches to further reduce the computational load of DPE in the “signal correlations computation” side. The works in (Axelrad et al., 2011; Cheong, 2011; Cheong, Wu, & Dempster, 2012; Cheong, Wu, Dempster, et al., 2012; Peretic & Gao, 2021) proposed pre-calculating the signal correlations per every code delay interval using Fast Fourier Transform (FFT). The correlations for each candidate PVTs would later be given from rounding the code delays of the candidate PVTs and using the pre-calculated correlation value with the closest associated code delay value to that of the candidate PVT. A noted drawback of this method is that the use of FFT would constrain the code delay spacings based on the IF data sampling frequency. But the works in (Cheong, 2011; Cheong, Wu, & Dempster, 2012; Cheong, Wu, Dempster, et al., 2012) assumes that the sampling frequency used is always sufficiently high to allow the correlations to be pre-calculated at code delay spacings sufficiently small that it has inconsequential impact to DPE accuracy. Similar approach were later adopted in (Peretic & Gao, 2021), which introduced an open-source parallelized-DPE receiver, designed to run on a graphics processing unit (GPU), as well as (Dampf et al., 2018; Dampf et al., 2019; Koza et al., 2024), which introduced a filtering method against non-Gaussian noises for DPE by transforming the FFT correlations into a probability density function that will be used as input to update the weights for particle filters to perform positioning. Recently, machine learning (ML) method was also investigated to enhance the resolution of the grid points to reduce the computational cost, which led the exploration to the ML-based optimization problem (Li et al., 2025).
In addition to complexity, DPE cannot directly be implemented into current GNSS receiver architectures, which computes tracking measurements and other observables such as pseudorange and pseudorange rates in the measurement engine, and positioning with the observables in the positioning engine. In other words, the positioning engine does not have access to the IF data, and implementation of DPE would require fusing the measurement and positioning engines. To address this, the authors had previously proposed a new DPE implementation, one that makes use of pseudoranges in place of code delays and multicorrelators as the pre-calculated correlations (Vicenzo & Xu, 2025; Vicenzo et al., 2023). Multicorrelators (pre-calculated correlations) can be computed in conjunction with 2SP tracking and later be easily indexed to ranges in the positioning engine, though in our previous work, the multicorrelators are computed following tracking to save computational power (Vicenzo & Xu, 2025; Vicenzo et al., 2023).
Indeed, that since DPE does not rely on pseudorange and pseudorange rates estimated by 2SP, and the fact that the performance of our previously proposed DPE would inextricably be linked to the 2SP tracking performance, it can be argued that the method cannot exactly be namedas DPE. However, under nominal conditions i.e., no error sources and good signal quality, the principle of our previously proposed DPE method aligns with that of DPE, as the pseudoranges estimated by 2SP would represent fully the received signal code delays. To put an end to the debate, this paper renames our previously introduced DPE method in Vicenzo and Xu (2025) as Corr-DPE, a variant of DPE.
The idea of using multicorrelators from tracking for DPE was originally proposed in (Closas et al., 2015). Corr-DPE was also used by the authors in (Vicenzo et al., 2023) to assess the performance of DPE in real urban environments, and finally made open-sourced in (Vicenzo & Xu, 2025). But details of their DPE method were not fully explained in (Closas et al., 2015), and crucial information such as the number of multicorrelators used, the spacings of the multicorrelators, or how the correlogram was generated from the multicorrelator outputs were left out. The idea for using tracking for DPE has been in the air for some time, but was most investigated in (Liu et al., 2013), who introduced a vector tracking architecture for DPE, which may provide improved resilience against weak signals. A notable drawback for such tracking method is that the errors from one satellite (such as NLOS) would inherently be propagated into other satellites’ measurements. As the aim of Corr-DPE here is for urban navigation, where MP and NLOS are prominent, our Corr-DPE architecture would be based on that of 2SP scalar tracking.
Corr-DPE approach also shares similarity with the aforementioned pre-calculated correlations method introduced in (Axelrad et al., 2011) and further used in (Cheong, 2011; Cheong, Wu, & Dempster, 2012; Cheong, Wu, Dempster, et al., 2012). But in (Axelrad et al., 2011), DPE was applied as a higher sensitivity acquisition and rapid positioning method, and similarly in (Cheong, 2011; Cheong, Wu, & Dempster, 2012; Cheong, Wu, Dempster, et al., 2012), DPE was proposed as an Assisted GNSS (A-GNSS) positioning of higher sensitivity compared to conventional A-GNSS methods. Thus, the pre-calculated FFT correlations spans over the whole code period i.e., from 0 to 1023 of the C/A code to generate estimates of the code delay in the case of acquisition and solve for the fractional code delay in DPE application for A-GNSS.
Differing from (Axelrad et al., 2011; Cheong, 2011; Cheong, Wu, & Dempster, 2012; Cheong, Wu, Dempster, et al., 2012), the approach introduced in this research is meant for single point positioning (SPP) with DPE, which would not require the pre-calculation of correlations that spans over the entire code period. This research proposes to compute and use a bank of multicorrelators spanning only from the Early to the Late (E-L) correlators during 2SP tracking. This way, the computational load from computing the correlations can be kept at much-reduced levels, levels that puts Corr-DPE closer to real-time positioning. Also, since conventional correlation process is used in tracking, the multicorrelator spacings would not be constrained by the IF signal sampling frequency like FFT would. Consequently, the Corr-DPE approach outlined in this paper would not be restricted to high sampling frequency IF data, which can be highly computational in the front-end side.
Further minimising the computational load, ARS is employed for Corr-DPE to obtain the PVT estimates. But unlike in (Closas et al., 2009a; Closas & Gao, 2020; Tang et al., 2023; Tang et al., 2024), each iteration of ARS would not require computation of the signal correlations. Instead, the correlations for each iteration of ARS would be interpolated based on the multicorrelator values following the approach in Vicenzo et al. (2023), Vicenzo et al. (2024), and Vicenzo and Xu (2025).
While previously the authors treated Corr-DPE the same way as DPE, this research aims to fully introduce the methodology of a Corr-DPE as a low-complexity alternative to DPE and evaluates whether it can provide positioning resilience against MP and NLOS in urban environments. We aim to extensively discuss how the multicorrelator spacing affects its MP mitigation performance. Previous research that makes use of pre-calculated correlations for DPE have always assumed that the bias generated from the multicorrelator spacing is considered negligible (Cheong, 2011; Cheong, Wu, & Dempster, 2012; Cheong, Wu, Dempster, et al., 2012; Closas et al., 2015; Vicenzo & Xu, 2025; Vicenzo et al., 2023). Thus, we evaluate the positioning performance of Corr-DPE through both Monte Carlo simulations as well as real data across various multicorrelator spacings, MP delay tracking error, and E-L spacing used in 2SP tracking. Monte Carlo simulations would also be used to evaluate Corr-DPE performance against NLOS. 2SP STL with Least Squares (LS) as well as conventional DPE will act as a reference for positioning accuracy in real data testing. In terms of positioning accuracy, Corr-DPE is expected to partially harness DPE advantages i.e., it will offer superior positioning against 2SP, but under severe MP/NLOS, cannot defeat conventional DPE performance. The computational load from computing the multicorrelators and ARS positioning would also be assessed, together with positioning results with real GNSS receiver data as proof-of-concept.
The findings of this research can be summarized into the following points.
1. Fully introducing the methodology for positioning with multicorrelators from 2SP tracking as a low-complexity alternative to DPE; nicknamed Corr-DPE. Unlike conventional DPE, Corr-DPE is readily applicable to existing 2SP receiver architectures.
2. In-depth analysis of the effect of multicorrelator spacings on Corr-DPE robustness against MP. Findings indicate that Corr-DPE can outperform 2SP when the MP delay tracking error is equal or larger than half of its multicorrelator spacings.
3. Analysis on the effect of NLOS reception. Findings indicate that NLOS error is majorly affected by the NLOS satellite’s elevation angle; the higher the elevation, the more resilient Corr-DPE is towards NLOS, and vice versa.
4. Real data validation of Corr-DPE. Corr-DPE was found to offer 30% positioning accuracy improvement to 2SP LS in real data scenario with both MP and NLOS, but as expected, cannot fully perform as good as conventional DPE.
5. Corr-DPE can naturally maintain position lock when the tracking loops lose track of one of the satellites in view, unlike 2SP which loses lock on positioning.
6. Corr-DPE positioning is found to take a fraction of a second (around 100 ms) per epoch, which serves as a considerable reduction in computational load compared to conventional DPE with ARS, which takes roughly 4 minutes and 50 seconds.
REFERENCES
Amar, A., & Weiss, A. J. (2005). Analysis of direct position determination approach in the presence of model errors. IEEE/SP 13th Workshop on Statistical Signal Processing, 2005, Bordeaux, France.
Appel, M. J., LaBarre, R., & Radulovic, D. (2004). On Accelerated Random Search. SIAM journal on optimization., 14(3), 708-731. https://doi.org/10.1137/S105262340240063X
Axelrad, P., Bradley, B. K., Donna, J., Mitchell, M., & Mohiuddin, S. (2011). Collective Detection and Direct Positioning Using Multiple GNSS Satellites. NAVIGATION, 58(4), 305-321. https://doi.org/10.1002/j.2161-4296.2011.tb02588.x
Bialer, O., Raphaeli, D., & Weiss, A. J. (2013). Maximum-Likelihood Direct Position Estimation in Dense Multipath. IEEE Transactions on Vehicular Technology, 62(5), 2069-2079. https://doi.org/10.1109/TVT.2012.2236895
Borre, K., Akos, D. M., Bertelsen, N., Rinder, P., & Jensen, S. H. (2007). A software-defined GPS and galileo receiver: A single-frequency approach. In (pp. 1-168).
Chen, X., Dovis, F., Peng, S., & Morton, Y. (2013). Comparative studies of GPS multipath mitigation methods performance. IEEE Transactions on Aerospace and Electronic Systems, 49(3), 1555-1568.
Chen, X., Dovis, F., Pini, M., & Mulassano, P. (2011). Turbo architecture for multipath mitigation in global navigation satellite system receivers. Radar, Sonar & Navigation, IET, 5, 517-527. https://doi.org/10.1049/iet-rsn.2010.0356
Cheong, J. W. (2011). Towards multi-constellation collective detection for weak signals: A comparative experimental analysis. 5, 3709-3719.
Cheong, J. W., Wu, J., & Dempster, A. (2012). Efficient Implementation of Collective Detection.
Cheong, J. W., Wu, J., Dempster, A. G., & Rizos, C. (2012). Assisted-GPS based snap-shot GPS receiver with FFT-accelerated collective detection: Time synchronisation and search space analysis. Proceedings of the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2012),
Closas, P., Fernandez-Prades, C., & Fernandez-Rubio, J. A. (2007a). Maximum Likelihood Estimation of Position in GNSS. IEEE Signal Processing Letters, 14(5), 359-362. https://doi.org/10.1109/lsp.2006.888360
Closas, P., Fernandez-Prades, C., & Fernandez-Rubio, J. A. (2007b, 15-20 April 2007). ML Estimation of Position in a GNSS Receiver using the SAGE Algorithm. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07,
Closas, P., Fernández-Prades, C., & Fernández-Rubio, J. A. (2009a). CramÉr–Rao Bound Analysis of Positioning Approaches in GNSS Receivers. IEEE Transactions on Signal Processing, 57(10), 3775-3786. https://doi.org/10.1109/TSP.2009.2025083
Closas, P., Fernández-Prades, C., & Fernández-Rubio, J. A. (2009b, 24-28 Aug. 2009). Direct Position Estimation approach outperforms conventional two-steps positioning. 2009 17th European Signal Processing Conference, Glasgow, United Kingdom.
Closas, P., Fernández-Prades, C., Fernández, A., Wis, M., Vecchione, G., Zanier, F., Garcia-Molina, J., & Crisci, M. (2015). Evaluation of GNSS direct position estimation in realistic multipath channels. 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015), Tampa, Florida.
Closas, P., & Gao, G. (2020). Direct Position Estimation. In Position, Navigation, and Timing Technologies in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications (Vol. 1, pp. 529-550).
Closas, P., Gusi-Amigó, A., & Blanch, J. (2017). Integrity measures in direct-positioning. Proceedings of the 30th International Technical Meeting of The Satellite Division of The Institute of Navigation (ION GNSS+ 2017),
Dampf, J., Frankl, K., & Pany, T. (2018). Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver. Sensors, 18(8), 2736. https://doi.org/10.3390/s18082736
Dampf, J., Lichtenberger, C. A., & Pany, T. (2019). Probability analysis for Bayesian direct position estimation in a real-time GNSS software receiver. Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019),
Di, H., Tsang, C. L., Zhang, G., & Hsu, L.-T. (2024). GNSS Measurement Performance in Vegetation Environments: Assessment and Analysis in Signal Processing Level. Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland.
Gusi?Amigó, A., Closas, P., Mallat, A., & Vandendorpe, L. (2018). Ziv?Zakai Bound for Direct Position Estimation. NAVIGATION, 65(3), 463-475. https://doi.org/10.1002/navi.259
Hsu, L.-T., Jan, S.-S., Groves, P. D., & Kubo, N. (2015). Multipath mitigation and NLOS detection using vector tracking in urban environments. GPS Solutions, 19(2), 249-262. https://doi.org/10.1007/s10291-014-0384-6
Huang, J., Yang, R., Gao, W., & Zhan, X. (2024). Geometric characterization on gnss direct position estimation in navigation domain. IEEE Transactions on Aerospace and Electronic Systems, 60(4), 4105-4123.
Jia, Q., Wu, R., Wang, W., Lu, D., Wang, L., & Li, J. (2017). Multipath interference mitigation in GNSS via WRELAX. GPS Solutions, 21, 487-498.
Koza, T., Morgan, S., & Martin, S. (2024). Performance Evaluation of LEO-Aided GPS Direct Position Estimation in Degraded Signal Environments. https://doi.org/10.33012/2024.19634
Kubo, N., Suzuki, T., Yasuda, A., & Shibazaki, R. (2005). An effective method for multipath mitigation under severe multipath environments. Proceedings of the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2005),
Kumar, A., & Singh, A. K. (2019). A novel multipath mitigation technique for GNSS signals in urban scenarios. IEEE Transactions on Vehicular Technology, 69(3), 2649-2658.
Li, H., Tang, S., O'Keefe, K., & Closas, P. (2025). Cross Ambiguity Function Resolution Enhancement Using Neural Networks for Direct Position Estimation Computational Cost Reduction. In 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) (pp. 1107-1113). IEEE.
Li, L., Cheong, J. W., Wu, J., & Dempster, A. G. (2014). Improvement to multi-resolution collective detection in GNSS receivers. The Journal of Navigation, 67(2), 277-293.
Liu, J., Cui, X., Lu, M., & Feng, Z. (2013). Direct position tracking loop based on linearised signal model for global navigation satellite system receivers. IET Radar, Sonar & Navigation, 7(7), 789-799. https://doi.org/https://doi.org/10.1049/iet-rsn.2012.0307
Liu, L., & Amin, M. G. (2009). Tracking performance and average error analysis of GPS discriminators in multipath. Signal Processing, 89(6), 1224-1239.
McGraw, G. A., & Braasch, M. S. (1999). GNSS multipath mitigation using gated and high resolution correlator concepts. Proceedings of the 1999 national technical meeting of the institute of navigation,
Narula, L., Singh, K. P., & Petovello, M. G. (2014). Accelerated collective detection technique for weak GNSS signal environment. 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS),
Ng, H.-F., Hsu, L.-T., Lee, M. J. L., Feng, J., Naeimi, T., Beheshti, M., & Rizzo, J.-R. (2022). Real-time loosely coupled 3DMA GNSS/Doppler measurements integration using a graph optimization and its performance assessments in urban canyons of New York. Sensors, 22(17), 6533.
Ng, H.-F., Zhang, G., & Hsu, L.-T. (2019). GNSS NLOS pseudorange correction based on skymask for smartphone applications. Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019),
Ng, H.-F., Zhang, G., & Hsu, L.-T. (2020). A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method. Journal of Navigation, 73(6), 1202-1222. https://doi.org/10.1017/s037346332000003x
Ng, Y., & Gao, G. X. (2016). Direct position estimation utilizing non-line-of-sight (NLOS) GPS signals. Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland, Oregon.
Peretic, M., & Gao, G. X. (2021). Design of a parallelized direct position estimation-based GNSS receiver. NAVIGATION, 68(1), 21-39. https://doi.org/10.1002/navi.402
Qiu, W., Zeng, Q., Xu, R., Liu, J., Shi, J., & Meng, Q. (2022). A multipath mitigation algorithm for GNSS signals based on the steepest descent approach. Satellite Navigation, 3(1), 14.
Strandjord, K., Axelrad, P., Akos, D. M., & Mohiuddin, S. (2020). Improved urban navigation with direct positioning and specular matching. Proceedings of the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California.
Tang, S., Li, H., Calatrava, H., & Closas, P. (2023). Precise Direct Position Estimation: Validation Experiments. 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA.
Tang, S., Li, H., & Closas, P. (2024). Assessment of Direct Position Estimation Performance in Multipath Channels. Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland.
Townsend, B. R., Fenton, P. C., Van Dierendonck, K. J., & Van Nee, D. J. R. (1995). Performance Evaluation of the Multipath Estimating Delay Lock Loop. NAVIGATION, 42(3), 502-514. https://doi.org/10.1002/j.2161-4296.1995.tb01903.x
Van Nee, R. D. J. (1992). The Multipath Estimating Delay Lock Loop. IEEE Second International Symposium on Spread Spectrum Techniques and Applications,
Van Nee, R. D. J., Siereveld, J., Fenton, P. C., & Townsend, B. R. (1994). The multipath estimating delay lock loop: approaching theoretical accuracy limits. IEEE Position, Location and Navigation Symposium,
Vicenzo, S., & Xu, B. (2025). Open-Source Multipath Mitigation Technology-integrated GNSS Direct Position Estimation. 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland.
Vicenzo, S., Xu, B., Dey, A., & Hsu, L.-T. (2023). Experimental Investigation of GNSS Direct Position Estimation in Densely Urban Area. 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado.
Vicenzo, S., Xu, B., Xu, H., & Hsu, L.-T. (2024). GNSS direct position estimation-inspired positioning with pseudorange correlogram for urban navigation. GPS Solutions, 28(2). https://doi.org/10.1007/s10291-024-01627-5
Weill, L. (1995). Achieving theoretical accuracy limits for pseudoranging in the presence of multipath. Proceedings of the 8th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 1995),
Weill, L. R. (2002). Multipath mitigation using modernized GPS signals: how good can it get? Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002), Portland, Oregon.
Weill, L. R. (2003). " How good can it get with new signals?," Multipath Mitigation. GPS world, 14(6), 106-113.
Weill, L. R. (2010). A high performance code and carrier tracking architecture for ground-based mobile GNSS receivers. Proceedings of the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2010), Portland, Oregon.
Wen, W., Zhang, G., & Hsu, L.-T. (2018). Correcting GNSS NLOS by 3D LiDAR and Building Height. https://doi.org/10.33012/2018.15984
Wen, W., Zhang, G., & Hsu, L. T. (2019). Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning. NAVIGATION, 66(4), 705-718. https://doi.org/10.1002/navi.335
Xie, P., & Petovello, M. G. (2015). Measuring GNSS Multipath Distributions in Urban Canyon Environments. IEEE Transactions on Instrumentation and Measurement, 64(2), 366-377. https://doi.org/10.1109/tim.2014.2342452
Xu, B., & Hsu, L.-T. (2019). Open-source MATLAB code for GPS vector tracking on a software-defined receiver. GPS Solutions, 23(2). https://doi.org/10.1007/s10291-019-0839-x
Xu, B., Jia, Q., & Hsu, L.-T. (2020). Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis. IEEE Transactions on Instrumentation and Measurement, 69(7), 4604-4619. https://doi.org/10.1109/tim.2019.2950578

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