Gravity-aided Inertial Navigation Research Articles

Observation-Differenced Point Mass Filter in Gravity-Aided Inertial Navigation

Observation-Differenced Point Mass Filter in Gravity-Aided Inertial Navigation

Adaptive Gravity-Aided Inertial Navigation Based on Characteristic Analysis of Marine Gravity Anomaly from Satellite Altimetry

Adaptive Gravity-Aided Inertial Navigation Based on Characteristic Analysis of Marine Gravity Anomaly from Satellite Altimetry

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

The gravity-aided inertial navigation system (GAINS) can achieve precise positioning of underwater vehicles in a gravity-suitable area. However, there are generally shortcomings in the existing intelligent suitable area selection methods in terms of selecting gravity feature parameters and learning parameters. In this paper, an intelligent suitable area selection method is proposed based on an improved support vector machine by the genetic algorithm (GA-SVM) to address the aforementioned problems. Firstly, the genetic algorithm (GA) is utilized to independently pick out the optimal feature subset of 15 existing gravity feature parameters and obtain the optimal support vector machine (SVM) learning parameters while eliminating irrelevant redundant features, thus improving the classifier’s performance and generalization ability. Then, the SVM classifier is trained according to the optimal information output by GA, and the accuracy of the test set is 95.5%. Finally, the classifier is utilized to distinguish suitable and unsuitable areas in the application region to evaluate the proposed method’s performance. The TERCOM experiment in the suitable area resulted in an average positioning error of less than 170 m.

Dimension-Expanded-Based Matching Method With Siamese Convolutional Neural Networks for Gravity-Aided Navigation

Matching algorithm is the key technique of the gravity-aided inertial navigation system. With the development of artificial intelligence, many neural network-based matching methods have been extensively studied. The pattern recognition-based matching methods transforms the matching problem as pattern recognition, which cannot be used directly on datasets where the neural networks have not been trained. To improve the accuracy of navigation and positioning, it is necessary to extract muti-dimensional gravity features from the limited navigation information. In this paper, the sequence of the gravity anomaly value is expanded to two-dimensional feature map containing time series features by Gramian Angular Fields method, which preserves the numerical information of the one-dimensional sequence and extracts the correlation relationship between each element. In addition, to reduce the influence of gravity measurement instrument error on the position precision of gravity matching algorithm, affine transformation is performed on INS trajectory and a Siamese convolutional neural network model is proposed to compare the measured gravity database with the gravity anomaly value in the pre-stored gravity background map and get the matching position. Simulation results and practical tests show that the proposed method can obtain a more precise location result compared with the traditional matching algorithm.

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Gravity-aided inertial navigation system (GAINS) is an important development in autonomous underwater vehicle (AUV) navigation. An effective path planning algorithm plays an important role in the performance of navigation in long-term underwater missions. By combining the gravity information obtained at each position with the error information from the INS, the posterior Cramér-Rao bound (PCRB) of GAINS is derived in this paper. The PCRB is the estimated lower bound of position variance for navigation along the planned trajectory. And the sum of PCRB is used as the minimum cost from the initial state to the current state in the state space, and the position error prediction variance of inertial navigation system (INS) is used as the minimum estimated cost of the path from the current state to the goal state in the A* algorithm. Thus, a path planning method with optimal navigation accuracy is proposed. According to simulation results, traveling along the path planned by the proposed method can rapidly improve the positioning accuracy while consuming just slightly more distance. Even when measuring noise changes, the planned path can still maintain optimal positioning accuracy, and high positioning accuracy is possible for any trajectory located within a certain range of the planned path.

Gravity Matching Algorithm Based on Correlation Filter

The gravity-matching algorithm is one of the key technologies in the gravity-aided inertial navigation system (GAINS). The matching accuracy determines the correction accuracy of the inertial navigation system (INS). However, the initial position error of the INS and gravity measurement error lead to a decrease in matching accuracy. In this article, the characteristics of gravity measurement error and inertial navigation information are made full use of to reduce the impact on matching. A gravity-matching algorithm based on a correlation filter (CF) is proposed, which includes preprocessing, CF, and mismatch detection. Meanwhile, the shape of the INS trajectory is used as a constraint to reduce the mismatch caused by the measurement error to improve the matching accuracy. Moreover, two matching strategies are given, including normal matching and sliding window matching. Experimental results show that the proposed method can effectively improve the matching accuracy under initial position error and gravity measurement error.

An Improved Particle Filter Based on Gravity Measurement Feature in Gravity-Aided Inertial Navigation System

The existing gravity matching algorithms are affected by the initial position error of the inertial navigation system (INS), the gravity measurement error, and the similarity of the gravity background map. Aiming at the above problems, an improved particle filter based on the gravity measurement feature (IPFBGMF) is proposed in this article. In the IPFBGMF, both the value and change characteristic of gravity measurements are considered, and a novel position acquisition method based on the gravity measurement feature is proposed, which can reduce the influence of the initial position error of INS. In addition, a new concept called direction measurement using the heading angle of INS is proposed to optimize the weight of particles in the PF. The PF with direction measurement can reduce the influence of the gravity measurement error and the similarity of the gravity background map. Furthermore, the robustness of the improved PF with the precise position is proven. Finally, a navigation strategy is designed to apply the proposed algorithms. Simulations show that IPFBGMF has the highest positioning accuracy compared with the traditional gravity matching algorithms.

A Filtered-Marine Map-Based Matching Method for Gravity-Aided Navigation of Underwater Vehicles

Gravity-aided inertial navigation system (INS) has been widely applied in the application of autonomous underwater vehicle. Due to the uneven distribution of the gravitational field, the matching performance varies unsteadily when underwater vehicles pass through real marine environment. In this article, a filtered marine map-matching method based on sliding window iterated closest contour point (ICCP) algorithm is proposed. The filtered marine-map records the information of the contour line, which is extracted from the gravity anomaly value. The trajectory can be represented by different vectors between the contour lines corresponding to the sampled gravity points. In addition, a model-free matching area selection method is proposed. A multilayer support vector classifier is established by hierarchical classification method. The genetic algorithm is adopted to optimize the penalty factor and kernel function parameters. The marine experimental results indicate that the proposed matching area selection method can fully excavate the gravity information and provide directional guidance. The proposed filtered marine map-matching algorithm effectively improved the matching accuracy for underwater vehicles.

The Quantification Method of Matching Capability of Areas in Gravity-Aided Inertial Navigation

Gravity-aided inertial navigation system (GAINS) is one of the essential research fields in underwater navigation technology, and one of the key problems to be solved is the selection of suitable matching areas. Distinctive from previous studies, the concept of Matching Capability of Areas (MCA) is defined from the perspective of the whole system, not just the 2-D image characteristics. The key factors affecting the MCA are analyzed, including heading, matching length, inertial navigation system (INS) initial position error, gravimeter error, map error, and INS drift. Taking these experimental conditions as a prior information, the method based on conditional probability to calculate the MCA is proposed, which can estimate the probability of matching within the required error limits in the given digital gravity map (DGM). After implementing a long-distance marine experiment, the validity of the MCA is verified and it is more comprehensive and effective than traditional characteristic parameters.

Gravity-Matching Algorithm Based on K-Nearest Neighbor.

The gravity-aided inertial navigation system is a technique using geophysical information, which has broad application prospects, and the gravity-map-matching algorithm is one of its key technologies. A novel gravity-matching algorithm based on the K-Nearest neighbor is proposed in this paper to enhance the anti-noise capability of the gravity-matching algorithm, improve the accuracy of gravity-aided navigation, and reduce the application threshold of the matching algorithm. This algorithm selects K sample labels by the Euclidean distance between sample datum and measurement, and then creatively determines the weight of each label from its spatial position using the weighted average of labels and the constraint conditions of sailing speed to obtain the continuous navigation results by gravity matching. The simulation experiments of post processing are designed to demonstrate the efficiency. The experimental results show that the algorithm reduces the INS positioning error effectively, and the position error in both longitude and latitude directions is less than 800 m. The computing time can meet the requirements of real-time navigation, and the average running time of the KNN algorithm at each matching point is 5.87s. This algorithm shows better stability and anti-noise capability in the continuously matching process.

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter-support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter-support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

A co‐occurrence matrix‐based matching area selection algorithm for underwater gravity‐aided inertial navigation

The matching area selection algorithm is one of the key technologies for underwater gravity-aided inertial navigation system, which directly affects the positioning accuracy and matching rate of underwater navigation. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field. However, the traditional algorithms are difficult to reflect the spatial relation characteristic of gravity field, which always miss some latent matching areas with obvious change of gravity field. In order to solve this problem, the matching area selection algorithm based on co-occurrence matrix is proposed. The proposed algorithm establishes gravity anomaly co-occurrence matrix and extracts spatial relation characteristic parameters to reflect the gravity field. The comprehensive spatial characteristic parameter is built by entropy and is used to select the matching area by maximization of inter-class variance. The experimental results show that the proposed algorithm can select more effective matching areas than the traditional algorithms.

A Delaunay Triangulation-Based Matching Area Selection Algorithm for Underwater Gravity-Aided Inertial Navigation

The matching area selection algorithm is one of the key technologies for underwater gravity-aided inertial navigation system. Positioning accuracy and matching rate of gravity matching can be improved in high quality matching area. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field, which is difficult to reflect the spatial characteristics of gravity field. In order to explore more vector characteristics and spatial relation characteristics of gravity field and find more potential matching areas, the matching area selection algorithm based on Delaunay triangulation is proposed in this article. The proposed algorithm establishes the three-dimensional model and extracts spatial feature parameters to analysis gravity field. The comprehensive characteristics matrix is used to select matching area by feature extraction and cluster analysis. The experimental results show that the proposed algorithm can extract more characteristics of gravity field, and select more effective matching areas than the traditional algorithms.

Improved Particle Filter-Based Matching Method With Gravity Sample Vector for Underwater Gravity-Aided Navigation

Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness.

A Characteristic Parameter Matching Algorithm for Gravity-Aided Navigation of Underwater Vehicles

The gravity matching algorithm is a key of the gravity-aided inertial navigation system (INS). The traditional matching algorithm connects only the measured gravity anomaly data to the position of the carrier according to a certain method to correct the inertial error. The gravitational field characteristic parameters should also be considered in the matching algorithm to improve the matching accuracy and reduce the number of mismatching. Therefore, based on the vector matching algorithm, a characteristic parameter matching algorithm is proposed. This paper presents new methods to calculate the characteristic parameters and the range of particle filters to increase the accuracy of matching. The gravity anomaly value of each particle is calculated more accurately by the proposed method of continuous gravity anomaly. Due to the high short-time accuracy of inertial navigation, the final trajectory is obtained by rigid transformation of a series of positions indicated by the INS corresponding to the trajectory. Simulation results prove that when compared with the vector matching algorithm, the proposed method makes the matching results more accurate and more reliable.

Point mass filter based matching algorithm in gravity aided underwater navigation

Gravity-aided inertial navigation is a hot issue in the applications of underwater autonomous vehicle (UAV). Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital model and the resolution is 2' ×2', a filter model based on vehicle position is derived and the particularity of inertial navigation system (INS) output is employed to estimate a parameter in the system model. Meanwhile, the matching algorithm based on point mass filter (PMF) is applied and several optimal selection strategies are discussed. It is obtained that the point mass filter algorithm based on the deterministic resampling method has better practicability. The reliability and the accuracy of the algorithm are verified via simulation tests.

A Combined Matching Algorithm for Underwater Gravity-Aided Navigation

A matching algorithm is a key technique in the gravity-aided inertial navigation system (INS). A matching algorithm combined with an iterated closest contour point (ICCP) algorithm and a point mass filter (PMF) is proposed. The algorithm involves a two-step matching process. First, the PMF based on vehicle position variable can obtain in real-time an instructional position given in a large initial position error. In particular, since the gravity anomaly database is tabulated in the form of a digital model, the parameter in the filter model is estimated by INS short-term displacement. Then, the ICCP algorithm with a sliding window can be employed for further matching. Simulation tests indicate that compared with the conventional ICCP algorithm, the proposed algorithm can achieve better results.

A Robust Solution of Integrated SITAN with TERCOM Algorithm: Weight-Reducing Iteration Technique for Underwater Vehicles' Gravity-Aided Inertial Navigation System

The robustness of the Sandia Inertial Terrain-Aided Navigation (SITAN) algorithm has a pivotal influence on underwater vehicles' Gravity-Aided Inertial Navigation Systems (GAINS). An abrupt glitch of the accuracy of the GAINS will evolve into a disaster when vehicles pass through areas of smooth gravity. In order to resolve vulnerability issues of initial error and linearization error, we propose the Correlation SITAN algorithm with Weight-Reducing Iteration Technique (CSITAN + WRIT), in which the correlation process equals Terrain Contour Matching (TERCOM). The CSITAN algorithm is a Multipoint-based Extended Kalman Filtering (MEKF) method, which can work in real time. First, we need to derive the state equation and observation equation of the Multipoint-based SITAN algorithm based on the principle of the traditional SITAN algorithm. Then the accuracy of the state prediction can be improved, and the linearization error can be reduced by the correlation method based on TERCOM. Finally, the WRIT is utilized to reduce the possible influence of gross errors existing in the results of the MEKF and to extract a value with higher precision. The experimental results show that CSITAN + WRIT can achieve better accuracy and higher success rate of matching and can improve the possibilities of the occurrence of large matching errors than traditional SITAN methods in areas with smooth gravity. Copyright © 2017 Institute of Navigation

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.

Research on the Relative Positions-Constrained Pattern Matching Method for Underwater Gravity-Aided Inertial Navigation

A Relative Positions-Constrained pattern Matching (RPCM) method for underwater gravity-aided inertial navigation is presented in this paper. In this method the gravity patterns are constructed based on the relative positions of points in a trajectory, which are calculated by Inertial Navigation System (INS) indications. In these patterns the accumulated errors of INS indicated positions are cancelled and removed. Thus the new constructed gravity patterns are more accurate and reliable while the process of matching can be constrained, and the probability of mismatching also can be reduced. Two gravity anomaly maps in the South China Sea were chosen to construct a simulation test. Simulation results show that with this RPCM method, the shape of the trajectory in gravity-aided navigation is not as restricted as that in traditional Terrain Contour Matching (TERCOM) algorithms. Moreover, the performance included matching success rates and position accuracies are highly improved in the RPCM method, especially for the trajectories that are not in straight lines. Thus the proposed method is effective and suitable for practical navigation.