GNSS localization using everyday mobile devices is challenging in urban environments, as ranging errors caused by the complex propagation of satellite signals and low-quality onboard GNSS hardware are blamed for undermining positioning accuracy. Researchers have pinned their hopes on data-driven methods to regress such ranging errors from raw measurements. However, the grueling annotation of ranging errors impedes their pace. This paper presents a robust end-to-end Neural Ranging Correction (NeRC) framework, where localization-related metrics serve as the task objective for training the neural modules. Instead of seeking impractical ranging error labels, we train the neural network using ground-truth locations that are relatively easy to obtain. This functionality is supported by differentiable moving horizon location estimation (MHE) that handles a horizon of measurements for positioning and backpropagates the gradients for training. Even better, as a blessing of end-to-end learning, we propose a new training paradigm using Euclidean Distance Field (EDF) cost maps, which alleviates the demands on labeled locations. We evaluate the proposed NeRC on public benchmarks and our collected datasets, demonstrating its distinguished improvement in positioning accuracy. We also deploy NeRC on the edge to verify its real-time performance for mobile devices.

The pseudorange error is one of the root causes of localization inaccuracy in GPS. Previous data-driven methods regress and eliminate pseudorange errors using handcrafted intermediate labels. Unlike them, we propose an end-to-end GPS localization framework, E2E-PrNet, to train a neural network for pseudorange correction (PrNet) directly using the final task loss calculated with the ground truth of GPS receiver states. The gradients of the loss with respect to learnable parameters are backpropagated through a Differentiable Nonlinear Least Squares (DNLS) optimizer to PrNet. The feasibility of fusing the data-driven neural network and the model-based DNLS module is verified with GPS data collected by Android phones, showing that E2E-PrNet outperforms the baseline weighted least squares method and the state-of-the-art end-to-end data-driven approach. Finally, we discuss the explainability of E2E-PrNet.

The multi-user Augmented Reality (AR) is powered by shared mapping and localization obtained from Visual Inertial Odometry (VIO) using AR sensors, including cameras and Inertial Measurement Units (IMU). However, VIO is vulnerable to sparse environment features, low lighting conditions, and dynamic motions. The Ultra-Wideband (UWB) transceiver, as a radio-sensing modality robust to visual and dynamic defects, has been considered as a formfitting patch on VIO to flatter multi-user AR. Nevertheless, like other wireless sensors, UWB suffers from noise and interference. Therefore, how to fuse UWB and VIO for multi-user AR is a promising but challenging research direction. To facilitate this process, we designed and released a tool, UarLogger, to log the relative location measurements from UWB and AR sensors mounted on iOS devices, as well as context-related data. We provide two examples–environmental condition evaluation and sensor fusion–to demonstrate its usefulness and showcase how it can boost the development of new algorithms with daily devices in hand.

This poster introduces an Augmented Reality (AR)-assisted framework to help exclude Non-Line-of-Sight (NLOS) signals from the Global Navigation Satellite Systems (GNSS). We developed an AR mobile app named GnssQuest, augmenting the user's real-time camera view with a visualization of GNSS satellites. Our real-world experiment demonstrates that GnssQuest helps users to exclude NLOS satellites blocked by surrounding buildings, leading to significant improvements in GNSS positioning performance.

We present a neural network for mitigating pseudoranges errors to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange error correction from six satellite, receiver, context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To train the MLP, we carefully calculate the target values of pseudorange errors using location ground truth and smoothing techniques and optimize a loss function involving the estimation residuals of smartphone clock offsets. The corrected pseudoranges are then used by a model-based localization engine to compute locations. The Google Smartphone Decimeter Challenge (GSDC) dataset, which contains Android smartphone data collected from both rural and urban areas, is utilized for evaluation. Both fingerprinting and cross-trace localization results demonstrate that our proposed method outperforms model-based and state-of-the-art data-driven approaches.

Correlations among process variables and inconsistencies in alarm decision making are quite common in multivariate alarm analysis, resulting in a large number of false alarms and missed alarms. The greatest challenges in multivariate alarm analysis are therefore analyzing overall correlations among all process variables and making integrated alarm decisions. In this work, a novel correlation analysis-based multivariate alarm method is developed to address these problems. First, a statistical characteristic-driven decision making trial and evaluation laboratory (DEMATEL) is proposed that can analyze the overall correlations among all process variables. Second, the sample space model (SSM) and evidence space model (ESM) can be used to convert process data into reference alarm evidence. Third, online samples are transformed into alarm evidence by matching them with the ESMs and holistically considering the data-level correlations and the evidence-level reliability and weight; the comprehensive alarm evidence is obtained by fusing this matched alarm evidence generated from the information of highly correlated or even colinear variables via maximum likelihood evidential reasoning (MAKER), and thus, more accurate and integrated alarm decisions are made. A real case study shows the superiority of the proposed method, which can therefore be generalized to other multivariate industrial processes.Note to Practitioners—Multivariate industrial processes generally have a large number of process variables, and with the rapid transfer of energy, material, and information, these process variables interact with each other or are even colinear. The focus of this study is to develop a multivariate alarm method for the correlation analysis of process variables and fusion of complementary, redundant and contradictory process information. The information fusion concept takes the place of the conventional alarm mechanism. From the perspective of the precise characterization of process information, process data are transformed into alarm evidence instead of alarm data. In addition, the proposed method can fully consider the overall correlations among all process variables and fuse each piece of process variable information to yield correct and integrated alarm decision results. It is noted that the information fusion concept is universal and can be extended to other real multivariate industrial processes.

Abstract H7N9 avian influenza is a novel virus with high morbidity and mortality that threatens human health and life. Therefore, it is necessary to diagnose H7N9 avian influenza in a timely and rapid manner to prevent further transmission of the virus and greatly reduce the infection and mortality rates. This paper proposes an H7N9 avian influenza diagnostic model that is based on a multilayer belief rule‐based (BRB) inference methodology by considering five typical characteristics of influenza: epidemiology, clinical manifestations, complications, characteristics of imaging tests and positive pathogen test results. Specifically, the severity of H7N9 avian influenza is gradually identified by a multilayer BRB model, and then the diagnostic model is optimized by a genetic algorithm (GA) to improve the diagnostic accuracy. Finally, the feasibility of the model is verified by fivefold cross‐validation with a real clinical dataset. The performance of the proposed diagnostic model is compared with those of the BP neural network (BPNN) model and support vector machine (SVM) model, and the results show that the multilayer BRB model can achieve rapid and satisfactory diagnostic results for H7N9 avian influenza. The experiment shows that the accuracy of the BRB model for H7N9 avian influenza hierarchical diagnosis provided in this paper is 0.903, which is higher than 0.818 of the BP neural network (BPNN) modules and 0.844 of the support vector machine (SVM) models. Especially when diagnosing the suspected and confirmed degree of H7N9 disease, it is more realized satisfactory diagnostic accuracy.