This paper introduces a weighted context-based step length estimation algorithm for pedestrian dead reckoning. Six pedestrian contexts are considered: stationary, walking, walking sideways, climbing and descending stairs, and running. Instead of computing the step length based on a single context, the step lengths computed for different contexts are weighted by the context probabilities. This provides more robust performance when the context is uncertain. The proposed step length estimation algorithm is part of a pedestrian dead reckoning system which includes the procedures of step detection and context classification. The step detection algorithm detects the step time boundaries using continuous wavelet transform analysis, while the context classification algorithm determines the pedestrian context probabilities using a relevance vector machine. In order to assess the performance of the pedestrian dead reckoning system, a dataset of pedestrian activities and actions has been collected. Fifteen subjects have been equipped with a waist-belt smartphone and traveled along a predefined path. Acceleration, angular rate and magnetic field data were recorded. The results show that the traveled distance is more accurate using step lengths weighted by the context probabilities compared to using step lengths based on the highest probability context.

The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy. Copyright © 2018 Institute of Navigation

Navigation and positioning systems depend on both the operating environment and the behavior of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, and the behavior can contribute additional information to the navigation solution. In order to operate across different contexts, a context‐adaptive navigation solution is required to detect the operating contexts and adopt different positioning techniques accordingly. This paper focuses on determining both environments and behaviors from smartphone sensors, serving for a context‐adaptive navigation system. Behavioral contexts cover both human activities and vehicle motions. The performance of behavior recognition in this paper is improved by feature selection and a connectivity‐dependent filter. Environmental contexts are detected from global navigation satellite system (GNSS) measurements. They are detected by using a probabilistic support vector machine, followed by a hidden Markov model for time‐domain filtering. The paper further investigates how behaviors can assist within the processes of environment detection. Finally, the proposed context‐determination algorithms are tested in a series of multicontext scenarios, showing that the proposed context association mechanism can effectively improve the accuracy of environment detection to more than 95% for pedestrian and more than 90% for vehicle.

GNSS intermediate spoofing is a big threat to GNSS‐dependent services because of its strong concealment. When the carrier Doppler of the spoofing signal is not locked to that of the authentic signal, such spoofing will result in the presence of dual‐peak in the signal spectrum. In the absence of interference, there should be no dual‐peak. In the multipath scenario, dual‐peak may exist, but the number of dual‐peaks and relative velocity residual magnitudes of dual‐peak signals are different from those in the spoofing scenario. Therefore, an intermediate spoofing detection technique based on dual‐peak in frequency domain and relative velocity residuals is proposed in this study, which can not only detect spoofing but also distinguish spoofing scenario from the multipath scenario. Fast Fourier transform based methods are used to detect the dual‐peak and extract the Doppler difference of the dual‐peak, and the relative velocity residual calculation based on Doppler differences is derived. The performance of this approach is evaluated both analytically and experimentally: simulation results show spoofing false alarm probability in the multipath scenario is small, which indicates that spoofing scenario and multipath scenario can be well distinguished; and the effectiveness is verified based on the Texas Spoofing Test Battery (TEXBAT).

GNSS real-time kinematic (RTK) and LiDAR odometry are complementary, offering global and local positioning capabilities, respectively. GNSS-RTK/LiDAR integration primarily focuses on two aspects: (1) 3D LiDAR-aided (3DLA) GNSS non-line-of-sight (NLOS) mitigation using point cloud data. (2) State estimation via tightly-coupled GNSS/LiDAR integration, which improves GNSS geometry, yielding better float solutions and lower estimation uncertainty. Combining 3DLA NLOS mitigation with tightly-coupled integration can thus enhance urban positioning reliability. However, the current 3DLA GNSS-RTK system remains limited in terms of NLOS detection reliability, tightly-coupled integration effectiveness, and computational efficiency. This paper proposes a tightly-coupled GNSS-RTK/LiDAR/INS integration system with 3DLA NLOS mitigation. We focus on improving the robustness of 3DLA NLOS detection, the efficiency and effectiveness of the tightly-coupled GNSS-RTK/LiDAR integration. Evaluations on the challenging UrbanNav dataset show over 70% improvement in positioning accuracy compared to conventional GNSS-RTK, achieving submeter to meter-level precision. Runtime analysis confirms the system supports real-time optimization and ambiguity resolution with an average computation time of 70 ms.

Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can contribute additional information to the navigation solution. In order to operate across different contexts, a context-adaptive navigation solution introduces an element of self-awareness by detecting the operating context and configuring the positioning system accordingly. This paper presents the detection of both environmental and behavioural contexts as a whole, building the foundation of a context-adaptive navigation system. Behavioural contexts are classified using measurements from accelerometers, gyroscopes, magnetometers and the barometer by supervised machine learning algorithms, yielding an overall 95% classification accuracy. A connectivity dependent filter is then implemented to improve the behavioural detection results. Environmental contexts are detected from GNSS measurements. They are classified into indoor, intermediate and outdoor categories using a probabilistic support vector machine (SVM), followed by a hidden Markov model (HMM) used for time-domain filtering. As there will never be completely reliable context detection, the paper also shows how environment and behaviour association can contribute to reducing the chances of the context determination algorithms selecting an incorrect context. Finally, the proposed context-determination algorithms are tested in a series of multi-context scenarios.