Mengyin Fu

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

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.

An Acoustic Communication Time Delays Compensation Approach for Master–Slave AUV Cooperative Navigation

Time delay is a major problem in the acoustic communication technology. Considering such a background, a new dynamic model is proposed for an improved error estimation algorithm in this paper. And error propagation equations are constructed for an inertial navigation system/Doppler velocity log integrated navigation system. The time delay problem is reconsidered for acoustic communication with an ultra-short baseline acoustic positioning system that uses a multi-autonomous underwater vehicle (AUV) cooperative navigation process in master-slave mode. The characteristics of time delays in acoustic communication are considered and the time delay is converted into a measurement bias within an observation equation for the slave AUV platform. Under the framework of a standard Kalman filter, an improved error estimation algorithm is presented to address the problem that occurs when random measurement bias exists in the dynamic linear system model. Based on the Monte Carlo method, the simulation results illustrate that compared with traditional methods, the error estimation algorithm proposed in this paper can effectively decrease positioning errors caused by time delays in acoustic communication.

Modeling for Fluid Transients in Liquid-Circular Angular Accelerometer

Liquid-circular angular accelerometer is generally designed with the circular tube, the fluid mass, and the porous transducer. With the consideration of the fluid compressibility, a novel theoretical model of the fluidic system of this sensor is developed based on the theory of fluid transients for the first time. Simulation and experiments are conducted to prove the validity of the proposed model and the model manifests satisfactory performance to calculate the natural frequency, the resonances, the bandwidth, and the low-frequency gain of the fluidic system. Moreover, the influences of several structure parameters are analyzed by using the proposed model. The wave speed in the fluid mass affects the bandwidth of the fluidic system grossly, while the radius of the circular tube has effects on both the gain and the bandwidth. Besides, the liquid resistance of the transducer and the cross-sectional area of the circular tube are found to exert analogous influences on the frequency response of the fluidic system.

The Gravity Matching Area Selection Criteria for Underwater Gravity-Aided Navigation Application Based on the Comprehensive Characteristic Parameter

Matching area selection is a key problem of the gravity matching navigation method, especially for underwater application. The gravitational profile varies when the vehicle gets into the matching area in different directions. Therefore, the matching navigation efficiency is also distinct due to the directionality of matching area. In this study, based on the fractal geometry theory, a characteristic parameter-isotropic coefficient, which can measure the directionality of gravity matching area-is proposed by analyzing the spectrum character of gravity anomaly sequence on 3-D surface. Instead of single parameter, the comprehensive characteristic parameter is built by combining traditional gravity parameters and isotropic coefficient through an analytic hierarchy process for matching area selection. Compared with the traditional method, simulation results prove that the matching area selected by the improved comprehensive characteristic parameter method not only has larger coverage, but also has better directivity.

An Improved TERCOM-Based Algorithm for Gravity-Aided Navigation

Gravity-aided inertial navigation is a leading issue in the application of autonomous underwater vehicle. An improved gravity matching algorithm, based on the principle of the terrain contour matching (TERCOM) algorithm, is proposed. The matching algorithm applies the shortest path algorithm to increase update frequency. In addition, the positioning error can be limited due to the novel correlation analysis method. Compared with existing algorithms, the improved TERCOM algorithm has better real-time performance, positioning accuracy, and reduced calculation burden. The reliability and the accuracy of the algorithm are verified via simulation tests.

A Mismatch Diagnostic Method for TERCOM-Based Underwater Gravity-Aided Navigation

Gravity matching algorithm is the pivotal technique in gravity-aided navigation, which is the main method in the application of underwater autonomous vehicle. TERCOM algorithm is one of the classical matching algorithms in this research field. Due to the characteristic of inertial navigation system (INS) location error, an affine or a rigid transformation relationship between actual vehicle trajectory and INS-indicated trajectory is assumed to be existed. In order to improve the accuracy of TERCOM algorithm, a mismatch diagnostic method based on the restricted spatial order constraints (RSOC) algorithm from image registration is assisted. RSOC strategy integrates spatial order constraints and decision criteria restrictions to screen outliers in a matched sequence. It is obtained that the mismatch diagnostic method can select the mismatched points accurately. Therefore, the matching algorithm with mismatch detection mechanism has better performance. The reliability and accuracy of the method are verified via simulation tests.

A Correction Method for DVL Measurement Errors by Attitude Dynamics

Due to the random wave motions as well as the vehicle’s movement pattern changes, the attitude dynamics (including heading, pitch, and roll motions) of the vehicle induced degrades the accuracy of doppler velocity log (DVL), which is a key element in deciding the precision of strapdown inertial navigation system (SINS)/DVL integrated navigation system. In this paper, the effects of heading, pitch and roll dynamics are first analyzed separately. Then, the general error model for DVL measurement errors caused by attitude dynamics is established. In the modeling process, the changes of angular position and angular rate between the DVL signal transmission and reception instant are considered. Subsequently, by utilizing the established error model, a correction method based on the real-time attitude information from SINS/DVL system is presented. Finally, the performance of the proposed method is examined through numerical simulations in which typical scenarios with a series of attitude dynamics are used. Simulation results show that the errors can be effectively corrected by using the correction method. This contributes to an improved positioning accuracy of SINS/DVL navigation.

A navigation map building algorithm using refined RBPF-SLAM

This paper proposes a map building algorithm for navigation task of autonomous vehicle. A local 2D grid map is designed to convey three types of information (free space, obstacle and unknown). A refined RBPF-SLAM algorithm is established to build a large scale, accurate navigation map. Moving obstacle information is excluded in the final map, which provides better performance in vehicles online localization. Two sets of experiments are carried out in two driving conditions, with one in urban traffic environment and the other in unstructured environment. The mapping and localization result testify the effectiveness of our algorithm.

The application of Square-Root Cubature Kalman Filter in the SINS/CNS integrated navigation system

The integrated navigation system can improve the navigation accuracy utilizing the redundant information. The Extended Kalman Filter (EKF) is commonly used in the integrated navigation system. It usually takes only one-order of Taylor expansion and needs to calculate the Jacobian matrix, which will affect the accuracy and numerical stability of the system significantly. Thus in this paper the application of Square-Root Cubature Kalman Filter(SCKF) method was proposed to solve the above problems in the SINS/CNS integrated navigation system. The simulation results show that the position, velocity and attitude errors are reduced effectively compared with the EKF method. The SCKF method is more suitable for the state estimation problems in integrated navigation system.

Cooperative navigation for multiple autonomous underwater vehicles with time delayed measurements

Dealing with time delayed measurements is a key problem of cooperative navigation for multiple autonomous underwater vehicles (MAUVs). In this work, in order to solve the invalidation of location due to the time delayed measurements, an algorithm which based on the augmented extended Kalman filter (AEKF) is proposed. The algorithm expands the variable dimension of slave AUV according to the time delayed measurements, and infers the fundamental equations of AEKF. Compared to the traditional EKF method, simulation results prove that AEKF has higher positioning accuracy.