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Spacecraft Attitude Determination And Control

Demands for indoor positioning based services (IPS) in commercial and military fields have spurred many positioning systems and techniques. Complex electromagnetic environments (CEEs) may, however, degenerate the accuracy and robustness of some existing single systems and techniques. To overcome this drawback, fusion-based positioning of multiple systems and/or techniques have been proposed to revamp the positioning performance in CEEs. In this paper, we survey the fusion-based indoor positioning techniques and systems from seminal works to elicit the state of the art within our proposed unified fusion-based positioning framework, which consists of three fusion characteristics: source, algorithm, and weight spaces. Different from other surveys, this survey summarizes and analyzes the existing fusion-based positioning systems and techniques from three characteristics. Meanwhile, discussions in terms of lessons, challenges, and countermeasures are also presented. This survey is invaluable for researchers to acquire a clear concept of indoor fusion-based positioning systems and techniques and also to gain insights from this survey to further develop other advanced fusion-based positioning systems and techniques in the future.

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A Survey on Fusion-Based Indoor Positioning

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages

A new method of seamless land navigation for GPS/INS integrated system

To enhance the performance of the navigation system under the complex underwater environment, a hybrid interacting multiple model (HIMM) aided inertial navigation system (INS)/Doppler velocity logs (DVL) integration solution is proposed. First, to employ the acoustic Doppler current profiler mode of DVL, a novel INS/DVL mechanism is constructed where the water current velocity is estimated in real time and both bottom-track and water-track velocity measurements of DVL are involved in the observation vector. Meanwhile, to deal with the outliers and observation noises in the DVL's measurements, a HIMM algorithm is proposed to adaptively select the proper models to describe the changing environment. Simulations and field test are conducted to evaluate the effectiveness of the proposed algorithm, where the interacting multiple model (IMM) algorithm is employed for comparison. The results indicate that the proposed HIMM-aided INS/DVL integration solution shows superiority than the traditional IMM method when the observation noises and outliers exist. Meanwhile, the proposed integration scheme can successfully bridge the DVL's bottom-track outages.

A Hybrid IMM Based INS/DVL Integration Solution for Underwater Vehicles

In general, the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)-integrated navigation method can provide continuous and accurate navigation information for autonomous underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.

A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

In order to satisfy the requirements of the placement, the operation, and the high-precision navigation and positioning for the underwater vehicles and the underwater operational platform, a SINS/USBL integration navigation strategy is proposed. This paper presents a robust Student’s t-based Kalman filter for strap-down inertial navigation system and ultra-short base line (SINS/USBL) integration system, which is proposed to suppress the measurement uncertainty induced by the acoustic outliers. Firstly, a SINS/USBL integration prototype system is designed and presented, which is constructed by an inertial measurement unit (IMU) and an USBL acoustic array in an inverted configuration, and they can be entirely designed and developed in-house. Furthermore, an improved robust Student’s t-based Kalman filter with the degree of freedom (dof) parameter is proposed to better address the acoustic outliers in the measured range and directions information, the heavy-tailed measurement noise induced by the acoustic outliers can be modelled as a Student’s t distribution, the posterior probability density functions (PDFs) of the state variable, the auxiliary random variable and the dof parameter are updated as Gaussian, Gamma, and Gamma prior PDF, respectively, and the corresponding statistics and the state vector are jointly inferred using the variational Bayesian (VB) approach. Finally, based on the state error equations and the derived measurement equation of SINS/USBL integration navigation system, the mathematical simulation test and the field trial are performed to demonstrate the feasibility and the superiority of the proposed SINS/USBL integration approach.

Student’s t-Based Robust Kalman Filter for a SINS/USBL Integration Navigation Strategy

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

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Tao Zhang

Papers

A new method of seamless land navigation for GPS/INS integrated system

A Hybrid IMM Based INS/DVL Integration Solution for Underwater Vehicles

A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

Student’s t-Based Robust Kalman Filter for a SINS/USBL Integration Navigation Strategy

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.