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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

Initial alignment is of vital importance to the inertial navigation system, and the in-motion initial alignment is a critical stage for the moving vehicles. In this paper, an in-motion alignment method for SINS/GPS integration is investigated. Using the velocity, which is obtained from GPS, the observation and the reference vectors are constructed. Different from previous methods, a robust attitude determination method is devised to finish the in-motion alignment process when there are outliers contained in the GPS outputs. To implement the robust attitude determination method, the error of the magnitudes of the observation and reference vectors is calculated, and this error is used to detect the outliers. However, the bias of the inertial sensors, the expectation and variance of the calculated error of the magnitude is time-varying, so that the detected accuracy of the outliers degrades. To address this defect, the new normalized error of the magnitude is constructed by a robust parameter identification method, then the expectation and variance of the new normalized error are constant. Using the new normalized error, the initial attitude is obtained by the robust optimization-based alignment (ROBA) method. The simulation and field tests are designed to validate the performance of the proposed method, the alignment results show that the proposed method can detect the outliers accurately, and it gets the similar alignment accuracy with the current popular method when there are no outliers in the auxiliary velocity of GPS.

A Robust In-Motion Optimization-Based Alignment for SINS/GPS Integration

Misalignment angle will result in a considerable error for the integration of Doppler velocity log (DVL) and of Strapdown Inertial Navigation System (SINS). In this article, a robust initial alignment method for SINS/DVL is proposed to solve a practical applicable issue, which is that the outputs of DVL are often corrupted by the outliers. First, the alignment principle for SINS/DVL is summarized. Second, based on the principle of this alignment method, the apparent velocity model is investigated, and the parameters expression of the apparent velocity model are derived detailed. Using the apparent velocity model, the unknown parameters of the apparent velocity model are estimated by the developed robust Kalman filter, then the reconstructed observation vector, where the outliers are detected and isolated, is reconstructed by the estimated parameters. Based on the reconstructed observation vectors, the initial attitude is determined. Finally, the simulation and field tests are carried out to verify the performance of the proposed method. The test results are shown that the proposed method can detect and isolate the outliers effectively and get better performance than the previous work.

Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors

In this paper, an in-motion coarse alignment based on the optimization-based method and vector observation for strapdown inertial navigation system is proposed. The traditional coarse alignment method converges slowly on the moving base, owing to the measurement noises of the observation measurements. To solve this problem, the method of sliding fixed-interval integration is utilized to construct the observation vector model, which can suppress the noises in the measurements. An optimization-based method named optimal-REQUEST algorithm is used to determine the attitude quaternion, which can further filter the measurement noise by adjusting the gain of the filter adaptively. To verify the performance of the proposed method, an in-motion simulation and vehicle tests are carried out. The results show that the convergence rate of the proposed method is faster than that of the compared methods, and the convergence process of the first one is more stable than that of the others.

In-Motion Coarse Alignment Based on the Vector Observation for SINS

The accumulated positioning errors are required to be eliminated after a long run-up of an underwater vehicle equipped with an inertial navigation system (INS). For the better position accuracy with less cost, a single-source aided passive location method is proposed, which applies acoustic positioning together with inertial navigation. The fixed sound source periodically emits the acoustic wave pulse under water. After a number of signals are received, a high-precision synthetic array is built with the positions measured by inertial instruments and clock on board. Then, the source is located by time-difference-of-arrival positioning technology and the navigation error of the moving vehicle is reduced. Different from fully autonomous, there is a radiation source and sound is provided as intermediary locally. It is independent of the control and the algorithm. The operation and calculation are performed on the vehicle, so it is called semi-autonomous. Multiple users are supported simultaneously. Since no radiation is sent from the vehicle, it is extremely concealed. More importantly, it is inexpensive and the accuracy has nothing to do with the amount of error before. In the simulation, the position error could be suppressed to 50 m or less. The results show that the new approach for underwater positioning is effective and reliable. After a period of time, INS error can be eliminated quickly when the vehicle returns to the correction area within the range of the single-source. In this way, long-time and accurate navigation is realized underwater.

Single-Source Aided Semi-Autonomous Passive Location for Correcting the Position of an Underwater Vehicle

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.

Tao Zhang

Papers

A Robust In-Motion Optimization-Based Alignment for SINS/GPS Integration

Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors

In-Motion Coarse Alignment Based on the Vector Observation for SINS

Single-Source Aided Semi-Autonomous Passive Location for Correcting the Position of an Underwater Vehicle

Virtual DVL Reconstruction Method for an Integrated Navigation System Based on DS-LSSVM Algorithm