High-precision positioning and navigation is highly important for unmanned vehicles in GNSS-challenged environments. The main aim of this paper is to develop an adaptive-robust fusion strategy for low-cost GNSS/SINS integrated systems with aiding information predicted by data predictors, which can provide reliable fusion positioning solutions when the GNSS signal is challenged. For handling the GNSS degraded problem, we make an adaptive-robust modification to the Kalman filter by introducing a new adaptive factor that can accurately adjust the estimation error covariance matrix and Kalman gain according to the real process. In addition, we design a self-organizing data-driven modeling network with a broad-deep structure for synthesizing navigation data predictors, in order to struggle with the GNSS denied problem. To testify the effectiveness and robustness of the new fusion algorithm, practical experiments with real data sets gathered from road tests in urban areas have been carried out. The results, that is with more than 80% increase in both north and east direction in GNSS-challenged area of our data sets, show that the proposed adaptive-robust fusion strategy can significantly improve the continuity and reliability of integrated navigation, and offer a more precise, robust and reliable solution for autonomous navigation in GNSS-challenged environments.

The vision-aided inertial navigation is of vital importance to guarantee reliable navigation for unmanned ground vehicles (UGV) in GNSS-challenged environments. Yet, it is almost inevitable to occur unpredictable stochastic disturbances caused by ego-motion uncertainty due to uneven pavements, unstructured and dynamic scenes, which may lead to the divergence of estimation algorithms and the unreliability of positioning. In this paper, we aim at developing a robust strategy for vision-aided inertial navigation to resist uncertainties, and modifying unscented Kalman filter by formulating a novel adaptive factor according to the degree of abnormity (DoA). Applying the robust strategy and algorithms, a monocular camera and an inertial measurement unit are tightly fused to construct a robust vision-aided inertial navigation system. The robust method can automatically estimate the DoA online, and adaptively adjust the estimation error covariance matrix and the Kalman gain for different scenarios. As the modification is made in the framework of Kalman filter, the real-time performance, robustness and high-accuracy positioning can be achieved using the proposed adjustment strategy. Experiments with the KITTI dataset show that the robust vision-aided inertial navigation system possesses good robustness and self-adaptation for protection against ego-motion uncertainty, and can improve the positioning accuracy of unmanned ground vehicles.

This paper concerns the problem of quantifying the observability of different states in linear (or linearized) time‐varying systems with focus on the usability to integrated navigation. The main goal is the derivation of a novel scalar method to measure the degree of observability in order to quantitatively analyze the observability of each state variable of a system. The new proposed observability analysis method is easily employed in practice with less computer memory and clearer physical meanings. The application to integrated navigation systems was illustrated to analyze the observability properties of navigation error‐states. According to the simulation results, the degree of observability can help us to quantitatively compare the observability of each state variable and further function as an indicator to analyze the influence of physical model parameters on the observability and estimability of navigation errors. Copyright © 2018 Institute of Navigation

Low-cost inertial measurement unit (IMU) is gradually applied for providing reliable positioning and navigation information in the area of Internet of Things (IoT) applications recently. However, the accuracy of IMU is highly influenced by inertial sensor errors in global navigation satellite system-denied and indoor navigation environment. In order to improve the accuracy and robustness of IMU, rotation modulation and cooperative navigation techniques can serve as effective ways for indoor and outdoor seamless navigation and positioning. In this article, we propose a cooperative navigation system with a low-cost inertial sensor array composed of four IMUs. For optimizing the configuration and information processing of this system, observability analysis is carried out based on the concept of the Degree of Observability (DoO). Furthermore, a criterion for calculating the DoO is formulated to simplify the observability analysis of rotational IMUs. According to the simulation experiments of unmanned vehicle at low, medium, and high driving speeds, the IMU rotation technique can improve the observability of inertial sensor errors and thus increase the accuracy of orientation, while the cooperative navigation technique can highly improve the accuracy of positioning.

The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.