Urban positioning: 3D mapping‐aided GNSS using dual‐frequency pseudorange measurements from smartphones

Ng, Hoi‐Fung; Zhang, Guohao; Luo, Yiran; Hsu, Li‐Ta

doi:10.1002/navi.448

Cited by 20 publications

(18 citation statements)

References 26 publications

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“…In particular, the matching probability for the j-th satellite (p match j ) is given as Eq. ( 15) in works such as [23,27]…”

Section: Gbsm and Sa-gbsm Detailsmentioning

confidence: 99%

“…With recent advances in sensor technologies and computing resources, there is an increasing interest in using Artificial Intelligence (AI)-driven tools for localization of autonomous vehicles, such as selfdriving cars and unmanned aerial vehicles (UAVs) [2,3,4]. Specifically, AI tools are popular in localization constellations, such as BeiDou and Galileo, and multiple frequencies, such as the GPS L1 and L5 signals [23].…”

Section: Introductionmentioning

confidence: 99%

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Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments

Neamati¹,

Bhamidipati²,

Gao³

2022

Preprint

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Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user's street or side of the street. Significant advances in 3D mapaided GNSS use grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS) classifiers and server-based processing to improve localization accuracy, especially in the cross-street direction. Our prior work introduces a new paradigm for shadow matching that proposes set-valued localization with computationally efficient zonotope set representations. While existing literature improved accuracy and efficiency, the current state of shadow matching theory does not address the needs of risk-aware autonomous systems. We extend our prior work to propose Mosaic Zonotope Shadow Matching (MZSM) that employs a classifier-agnostic polytope mosaic architecture to provide risk-awareness and certifiable guarantees on urban positioning. We formulate a recursively expanding binary tree that refines an initial location estimate with set operations into smaller polytopes. Together, the smaller polytopes form a mosaic. We weight the tree branches with the probability that the user is in line of sight of the satellite and expand the tree with each new satellite observation. Our method yields an exact shadow matching distribution from which we guarantee uncertainty bounds on the user localization. We perform high-fidelity simulations using a 3D building map of San Francisco to validate our algorithm's risk-aware improvements. We demonstrate that MZSM provides certifiable guarantees across varied data-driven LOS classifier accuracies and yields a more precise understanding of the uncertainty over existing methods. We validate that our tree-based construction is efficient and tractable, computing a mosaic from 14 satellites in 0.63 seconds and growing quadratically in the satellite number.

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“…In particular, the matching probability for the j-th satellite (p match j ) is given as Eq. ( 15) in works such as [23,27]…”

Section: Gbsm and Sa-gbsm Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments

Neamati¹,

Bhamidipati²,

Gao³

2022

Preprint

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show abstract

“…The complementary nature of the two approaches inspired researchers to integrate them. The latest study shows that an integrated solution of 3DMA GNSS can provide positioning accuracy of around 10 m or less in urban canyons with both single frequency [25] and multi-frequency [26] measurements.…”

Section: Related Work On 3dma Gnssmentioning

confidence: 99%

“…Due to their computational efficiency, this study integrates shadow matching and likelihood-based ranging 3DMA GNSS. The implementation can be found in [26].…”

Section: Dma Gnss Positioning Algorithmmentioning

confidence: 99%

“…For each candidate 𝐩 𝑗=1…𝐽 , the integrated likelihood score, 𝑆 𝑗,𝑆𝐷𝑀+𝐿𝐵𝑅 , will be evaluated, 𝑆 𝑗,𝑆𝐷𝑀+𝐿𝐵𝑅 = √𝑆 𝑗,LBR × 𝑆 𝑗,SDM (5) where 𝑆 𝑗,LBR and 𝑆 𝑗,SDM are the likelihood score of likelihood-based ranging 3DMA GNSS and shadow matching, respectively. The detail of the calculation of the likelihood scores can be found at [26]. Shadow matching evaluates the visibility consistency between the measured carrierto-noise-ratio (𝐶 𝑁 0 ⁄ ) and prediction with skymask.…”

Section: Integrated Solution Of 3dma Gnssmentioning

confidence: 99%

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Real-time Loosely Coupled 3DMA GNSS /Doppler Measurements Integration Using a Graph Optimization and Its Performance Assessments in Urban Canyons of New York

Ng¹,

Hsu²,

Lee³

et al. 2022

Preprint

Self Cite

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Smart health applications have received significant attention in recent years. Novel applications hold significant promise to overcome many of the inconveniences faced by persons with disabilities throughout daily living. For people with blindness and low vision (BLV), environmental perception is compromised, creating myriad difficulties. Precise localization is still a gap in the field and is critical to safe navigation. Conventional GNSS positioning cannot provide satisfactory performance in urban canyons. 3D mapping-aided (3DMA) GNSS may serve as an urban GNSS solution, since the availability of 3D city models has widely increased. As a result, this study developed a real-time 3DMA GNSS-positioning system based on state-of-the-art 3DMA GNSS algorithms. Shadow matching was integrated with likelihood-based ranging 3DMA GNSS, generating positioning hypothesis candidates. To increase robustness, the 3DMA GNSS solution was then optimized with Doppler measurements using factor graph optimization (FGO) in a loosely-coupled fashion. This study also evaluated positioning performance using an advanced wearable system’s recorded data in New York City. The real-time forward processed FGO can provide a root-mean-square error (RMSE) with about 21 m. The RMSE drops to 16 m when the data is post-processed with FGO in a combined direction. Overall results show that the proposed loosely-coupled 3DMA FGO algorithm can provide a better and more robust positioning performance for the multi-sensor integration approach used by this wearable for persons with BLV.

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GNSS direct position estimation-inspired positioning with pseudorange correlogram for urban navigation

Vicenzo,

Xu,

et al. 2024

GPS Solut

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Multipath (MP) reception has been among the main issues for accurate and reliable positioning in urban environments. It has been shown to introduce positioning errors of up to tens of meters for conventional two-step (2SP) receivers. The direct position estimation (DPE) has been introduced as a more robust positioning algorithm compared to the conventional two-step (2SP) receivers in terms of MP mitigation. However, its high computational load prevents DPE from real-time positioning for commercial receivers. Thus, we present a novel grid-based maximum likelihood estimation (MLE) algorithm based on DPE by making use of pseudorange measurements to obtain the correlogram on a predefined searching space. Unlike DPE, which performs correlations at the intermediate frequency (IF) level, correlations are done by directly comparing the code phase of each candidate position, velocity, and timing with the incoming pseudorange. This way, the proposed method retains MP mitigation properties from DPE through the use of MLE from DPE and allows for a significantly reduced computational load compared to DPE. The proposed method was tested with both open-sourced datasets collected in urban environments as well as IF simulation data, and its performance is evaluated against a 2SP receiver. Results show that the proposed method manages to acquire the MP mitigation capability of DPE and outperforms 2SP by up to around 23% in the tested urban datasets and 91% in the simulation data, at a much-reduced computational time. The resilience of our proposed method against MP and NLOS could even potentially offer applications in geodetic networks, where robust estimators are traditionally employed to counteract outliers.

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Urban positioning: 3D mapping‐aided GNSS using dual‐frequency pseudorange measurements from smartphones

Cited by 20 publications

References 26 publications

Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments

Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments

Real-time Loosely Coupled 3DMA GNSS /Doppler Measurements Integration Using a Graph Optimization and Its Performance Assessments in Urban Canyons of New York

GNSS direct position estimation-inspired positioning with pseudorange correlogram for urban navigation

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