Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Simultaneous localization and mapping SLAM in unknown GPS-denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single-robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state-of-the-art multiple-robot systems, with a major focus on multiple-robot SLAM. Various issues and problems in multiple-robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.

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Multiple-Robot Simultaneous Localization and Mapping: A Review

Advancements in the field of autonomous underwater vehicle

Joint state and fault estimation for time-varying nonlinear systems with randomly occurring faults and sensor saturations

Prediction of solar energy guided by pearson correlation using machine learning

A new sampling method in particle filter based on Pearson correlation coefficient

Navigation accuracy of an inertial navigation system can be significantly enhanced by rotating inertial measurement unit with gimbals. Therefore, nonorthogonal angles of gimbals, which are coupled into the navigation error during rotation, should be calibrated and compensated effectively. In this paper, the relationship model of nonorthogonal angles and navigation error is established. Then, the calibration scheme and observation equation during gimbals rotation is proposed. Proved by a piecewise constant system method, all of the error parameters are observable and can be estimated by an extended Kalman filter. Experimental results show that compared with the traditional method, the proposed method can substantially reduce velocity error on static base. Moreover, the position accuracy of long-term navigation under moving base is also significantly increased.

A Self-Calibration Method for Nonorthogonal Angles Between Gimbals of Rotational Inertial Navigation System

In inertial navigation system (INS)/doppler velocity log (DVL) integrated navigation, DVL commonly provides the 3-D velocity of the vehicle to the navigation filter. Theoretically, this INS/DVL fusion is the loosely coupled approach, in which DVL requires sufficient beam measurements (at least three) to calculate the 3-D velocity. However, in cases that DVL only has limited (fewer than three) beam measurements, which is possible in the underwater environment, the loosely coupled navigation system can no longer work, and only leaves the INS to work alone. Therefore, the navigation error will accumulate with time. In contrast to the loosely coupled approach, the tightly coupled approach directly uses DVL beam measurements without transforming them to 3-D velocity. In this paper, a method based on the tightly coupled approach for INS/DVL integrated navigation with limited DVL beam measurements is proposed, in which depth updates from a pressure sensor generally equipped by most autonomous underwater vehicles (AUVs) are applied. Simulation results verify that the proposed method is efficient in most kinds of tasks of the AUV.

INS/DVL/PS Tightly Coupled Underwater Navigation Method With Limited DVL Measurements

Gravity matching algorithm is a key technique of gravity aided navigation for underwater vehicles. The reliability of traditional single point matching algorithm can be easily affected by environmental disturbance, which results in mismatching and decrease of navigation accuracy. Therefore, a particle filter (PF)-based matching algorithm with gravity sample vector is proposed. The correlation between adjacent sample points of inertial navigation system is considered in the vector matching algorithm in order to solve the mismatching problem. The current sampling point matching result is rectified by the vectors composed by the selected sampling points and matching point. The amount of selected sampling points is determined by the gravity field distribution and the real-time performance of the algorithm. A PF-based on Bayesian estimation is introduced in the proposed method to overcome the divergence disadvantage of the traditional point matching algorithm in some matching areas with obvious gravity variation. Simulation results prove that compared with the traditional methods, the proposed method is robust to the changes of gravity anomaly in the matching areas, with more accurate and reliable matching results.

A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation

In order to compensate errors of inertial measurement unit which is the core of rotational inertial navigation system, self-calibration is utilized as an effective way to reduce navigation error. Error model of navigation solution and initial alignment is used to establish the relationship between navigation errors and inertial measurement unit (IMU) errors. A second order damper is added to the vertical velocity channel to suppress the divergence and then the vertical velocity error can be regarded as an effective observation to estimate the error parameters. Since the accuracy of the self-calibration method is susceptible to the positioning error of gimbals, total least squares (TLS) method is utilized in identification of the error parameters. Experimental results show that all of the twenty-one error parameters can be estimated with the proposed rotation scheme. Compared to least squares (LS) method, TLS method can improve the position accuracy of 8 h by 46.2%.

Zhihong Deng

Papers

A new sampling method in particle filter based on Pearson correlation coefficient

A Self-Calibration Method for Nonorthogonal Angles Between Gimbals of Rotational Inertial Navigation System

INS/DVL/PS Tightly Coupled Underwater Navigation Method With Limited DVL Measurements

A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation

A multi-position self-calibration method for dual-axis rotational inertial navigation system