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1. Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

   https://doi.org/10.3390/s21041250  

   Medina, Daniel; Li, Haoqing; Vilà-Valls, Jordi; Closas, Pau (February 2021, Sensors)

   null (Ed.)

   Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness. 

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2. A computationally-efficient bound for the variance of measuring the orientation of single molecules

   https://doi.org/10.1117/12.2543813  

   Wu, Tingting; Ding, Tianben; Mazidi, Hesam; Zhang, Oumeng; Lew, Matthew D. (February 2020, Proc. SPIE)

   Modulating the polarization of excitation light, resolving the polarization of emitted fluorescence, and point spread function (PSF) engineering have been widely leveraged for measuring the orientation of single molecules. Typically, the performance of these techniques is optimized and quantified using the Cramér-Rao bound (CRB), which describes the best possible measurement variance of an unbiased estimator. However, CRB is a local measure and requires exhaustive sampling across the measurement space to fully characterize measurement precision. We develop a global variance upper bound (VUB) for fast quantification and comparison of orientation measurement techniques. Our VUB tightly bounds the diagonal elements of the CRB matrix from above; VUB overestimates the mean CRB by ~34%. However, compared to directly calculating the mean CRB over orientation space, we are able to calculate VUB ~1000 times faster. 

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3. Leveraging Spatial Diversity for Ambiguity-Free Ultranarrowband Phase-Based 3-D Localization

   https://doi.org/10.1109/JIOT.2024.3382046  

   Xu, Guoyi; Kapoor, Aakash; Kan, Edwin C (June 2024, IEEE Internet of Things Journal)

   Phase-based 3D localization of radio-frequency (RF) markers has high sensitivity and accuracy. However, phase measurements suffer from oscillator phase noises, wavelength ambiguities, and multi-path interferences. Additionally in the near field, antenna detuning and medium inhomogeneity render the phase-distance relation nonlinear and non-monotonic and bring forth extra ambiguities, especially with obstructed line-of-sight (LoS). In this work, we present a novel precision localization framework which leverages spatially diverse redundant channels to resolve ambiguities without relying on broad bandwidth. First, measured differential phases were used to accurately retrieve differential distances from spline-fitted phase-distance curves. Then, distances from multiple channels were combined to generate 3D location estimates. Finally, location ambiguities were removed by taking different channel subsets to identify one unambiguous location using spatial clustering. An experimental multiple-input multiple-output (MIMO) network was implemented by a Universal Software Radio Peripheral (USRP) platform and harmonic RF markers to demonstrate millimeter-level 3D localization at sub-1GHz carrier frequencies within heavy multi-path ambient, simulating the condition inside building structures. 

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

   https://doi.org/10.3390/s22176533  

   Ng, Hoi-Fung; Hsu, Li-Ta; Lee, Max Jwo; Feng, Junchi; Naeimi, Tahereh; Beheshti, Mahya; Rizzo, John-Ross (September 2022, Sensors)

   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) of 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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5. Deep Learning of GNSS Acquisition

   https://doi.org/10.3390/s23031566  

   Borhani-Darian, Parisa; Li, Haoqing; Wu, Peng; Closas, Pau (February 2023, Sensors)

   Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios. 

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