Iterative Closest Contour Point Research Articles

A Novel Algorithm for Enhancing Terrain-Aided Navigation in Autonomous Underwater Vehicles

The position error in an inertial navigation system (INS) for autonomous underwater vehicles (AUVs) increases over time. Terrain-aided navigation can assist in correcting these INS position errors. To enhance the matching accuracy under large initial position errors, an improved terrain matching algorithm comprising terrain contour matching (TERCOM), particle swarm optimization (PSO), and iterative closest contour point (ICCP), named TERCOM-PSO-ICCP, is proposed. Initially, an enhanced TERCOM with an increased rotation angle is utilized to minimize heading errors and reduce the initial position error. The similarity extremum approach evaluates the initial matching outcomes, leading to an enhanced accuracy in the initial results. Next, artificial bee colony (ABC)-optimized PSO is employed for secondary matching to further reduce the initial position error and narrow the matching area. Finally, the ICCP, using the Mahalanobis distance as the objective function, is applied for the third matching, leveraging the ICCP’s fine search capabilities. The effective combination of these three algorithms significantly improves the terrain-aided navigation matching effect. Two tests show that the improved TERCOM-PSO-ICCP effectively reduces the matching error and corrects the position of the INS.

Underwater terrain-matching algorithm based on improved iterative closest contour point algorithm

Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ωAPSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ωAPSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs.

An INS/Geomagnetic Integrated Navigation Method for Coarse Estimation of Positioning Error and Search Area Adaption Applied to High-Speed Aircraft

The geomagnet aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft navigation. It can overcome the shortcomings of pure inertial navigation error accumulation over time and achieve long-term, autonomous, and all-weather navigation. The traditional iterative closest contour point (ICCP) algorithm, however, requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this article proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the aircraft flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this article can reach within 200 m.

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.

An innovative PSO-ICCP matching algorithm for geomagnetic navigation

Geomagnetic aided navigation is an effective approach to eliminate the cumulative error in inertial navigation system (INS). Iterative closest contour point (ICCP) geomagnetic matching algorithm is sensitive to initial error, which will degrade the navigation accuracy greatly. To solve the problem, ICCP and particle swarm optimization (PSO) are improved separately, and an innovative PSO-ICCP geomagnetic matching algorithm is proposed. Firstly, a multi-attribute decision-making problem is introduced to optimize the output of ICCP. Secondly, PSO and ICCP are combined to diminish the sensitive of ICCP to initial error with the global search capability of PSO. Finally, to improve the performance of PSO, a particle initialization strategy is adopted using sliding window and quad-tree. Experimental results verified that PSO-ICCP can effectively reduce INS error. When magnetic measurement noise is 15nT, the position error and heading error are reduced from 1318.83 m, 7.83° to 23.51 m, 2.08°, which is more accurate compared with state-of-art algorithm.

Improved Iterative Closest Contour Point Matching Navigation Algorithm Based on Geomagnetic Vector

The geomagnetic matching aided positioning system based on Iterative Closest Contour Point (ICCP) algorithm can suppress the accumulation error of the inertial navigation system and achieve the accurate positioning of the vehicle. Aiming at the problem that the ICCP algorithm is sensitive to heading error and easily mismatches in regions with similar geomagnetic general features, an improved ICCP matching algorithm based on geomagnetic vector is proposed. The ant colony algorithm is designed to improve the search strategy in a large probability range. The geomagnetic three-dimensional vector feature and the Hausdoff distance are employed as the objective function for multiple iterations, improving matching efficiency and accuracy. Simulation results show that compared with the traditional ICCP algorithm, the positioning error of the matching track, the heading error, and the matching time of the improved ICCP algorithm are reduced by 69.6%, 44.0% and 39.0%, respectively.

Comparison of geomagnetic aided navigation algorithms for hypersonic vehicles

In this paper, we simulate, verify, and compare the performance of three classical geomagnetic matching aided navigation algorithms to assess their applicability to hypersonic vehicle navigation. Firstly, we introduce the various sources of the geomagnetic field. Secondly, we describe the principles and processes of the geomagnetic contour matching (MAGCOM) algorithm, iterative closest contour point (ICCP) algorithm, and Sandia inertial magnetic aided navigation (SIMAN) algorithm. Thirdly, we discuss the principles of inertial/geomagnetic integrated navigation, and propose the state and observation equations of integrated navigation. Finally, we perform a simulation of inertial/geomagnetic integrated navigation on the hypersonic boost-glide vehicle trajectory. The simulation results indicate that the real-time performance of the SIMAN algorithm can be optimized such that the matching accuracy is higher than that of the other two algorithms. At the same time, the SIMAN algorithm can achieve better stability, and though the amount of measurement noise can be larger, it can still achieve good positioning accuracy.

An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

The Iterated Closest Contour Point (ICCP) algorithm is widely used in geomagnetic navigation. In order to enhance the anti-interference performance of the ICCP, an improved algorithm is proposed. First, the principle of delta modulation is introduced to generate a geomagnetic matching sequence according to the magnetic fluctuations, this assists finding the optimal quantitative step and matching length; thus, the algorithm's accuracy and real-time performance are improved. Second, in order to solve the problem of geomagnetic matching under an interference environment, a Probability Data Association (PDA) algorithm based on regenerated measurements is adopted. The ideal magnetic value is regarded as a target, and the measured values within the confidence region are taken as the effective measurements of the target. Each of them will give an estimation of the vehicle's position. Considering the constraints of a vehicle's kinematic performance, its final position can be obtained by fusing all effective estimations with the PDA algorithm. Simulation and semi-physical experiments have verified the feasibility and effectiveness of the proposed algorithm. The Regenerated Measurements (RM)-PDA algorithm shows better performance and can be used in practical applications.

Gravity Aided Positioning Based on Real-Time ICCP With Optimized Matching Sequence Length

The existing real-time iterative closest contour point (ICCP) algorithm uses fixed matching sequence length, which is set according to human experience and cannot obtain the best positioning accuracy on all tracks. This paper proposes a real-time ICCP algorithm with the optimized matching sequence length (OMSL–ICCP) based on the analysis of the shortcomings of the existing real-time ICCP. The optimal matching sequence length of OMSL–ICCP under the current measured gravity anomaly sequence is obtained by the golden section search. The Hausdorff distance is utilized to obtain the search range of the closest contour point, which can effectively shrink the search range of the closest contour point. And the gravitation field algorithm is applied to further improve the positioning accuracy. In simulation tests with different gravity sensor measurement noises, different INS positioning errors and different gravity map resolutions, the difference of positioning performance between the existing real-time ICCP and the OMSL–ICCP is compared. Under the same test conditions, the simulation results show that the positioning accuracy of OMSL–ICCP is higher than the positioning accuracy obtained by the existing real-time ICCP algorithm with the optimal sequence length.

Improved ICCP Algorithm Considering Scale Error for Underwater Geomagnetic Aided Inertial Navigation

The iterative closest contour point (ICCP) matching algorithm has become more and more widely used in the underwater geomagnetic aided inertial navigation system (INS). In practical application, the traditional ICCP algorithm is sensitive to the initial positioning error of the INS and can only do rigid transformation for the INS track of the vehicle. Particularly when there exists scale error, the accuracy and stability of the traditional ICCP algorithm will be affected. To solve this problem, an improved algorithm based on affine transformation is proposed. Firstly, the fundamental of the ICCP is analyzed in detail, and an error analysis of the geomagnetic aided inertial navigation system is carried out, and then the rigid transformation is replaced with affine transformation to improve the performance of the ICCP. In contrast to the conventional approach, the proposed algorithm can solve the rotation, translation, and scaling parameters of the indicated track and the matching track, so it can significantly reduce the interference of the scale error. Experimental results confirm the effectiveness of the proposed algorithm.

A New Geomagnetic Matching Navigation Method Based on Multidimensional Vector Elements of Earth’s Magnetic Field

At present, most of the geomagnetic navigation methods are based on the single geomagnetic scalar characteristics, and the iterative closest contour point (ICCP) algorithm is the most extensively utilized. But when there are several contour lines with the same scalar value in the matching area, or the scalar feature in this area is not obvious, navigation accuracy will be seriously affected. In this letter, a new geomagnetic navigation method based on vector matching algorithm [vector ICCP (VICCP)] is proposed, combining the searching principle of trusted points sets and tracks with the matching principle of geomagnetic vector correlation restriction. Consequently, navigation results of it will have greater accuracy, more reliable validity, and practicability compared with the traditional ICCP algorithm. The performance of the matching and the correction methods is analyzed by simulation and experiment. In simulation, the position error of VICCP is less than ICCP under the conditions of nonobvious scalar geomagnetic features, which are, respectively, reduced from 1340.0 to 72.8 m, from 1267.7 to 33.3 m, and from 14115.7 to 36.9 m. And the conclusion is also verified in the experiment. In addition, VICCP algorithm is not sensitive to initial position. Thus, the proposed VICCP algorithm can effectively improve the performance of geomagnetic navigation.

Underwater terrain-aided navigation system based on combination matching algorithm

Considering that the terrain-aided navigation (TAN) system based on iterated closest contour point (ICCP) algorithm diverges easily when the indicative track of strapdown inertial navigation system (SINS) is large, Kalman filter is adopted in the traditional ICCP algorithm, difference between matching result and SINS output is used as the measurement of Kalman filter, then the cumulative error of the SINS is corrected in time by filter feedback correction, and the indicative track used in ICCP is improved. The mathematic model of the autonomous underwater vehicle (AUV) integrated into the navigation system and the observation model of TAN is built. Proper matching point number is designated by comparing the simulation results of matching time and matching precision. Simulation experiments are carried out according to the ICCP algorithm and the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy and stability are improved with the proposed combinational algorithm in case that proper matching point number is engaged. It will be shown that the integrated navigation system is effective in prohibiting the divergence of the indicative track and can meet the requirements of underwater, long-term and high precision of the navigation system for autonomous underwater vehicles.

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.

Multipath Parallel ICCP Underwater Terrain Matching Algorithm Based on Multibeam Bathymetric Data

Using the underwater terrain for assisted navigation is a popular research direction for autonomous underwater vehicle navigation technology. The iterated closest contour point (ICCP) algorithm is one of the classic matching algorithms in underwater terrain assisted navigation. However, the traditional ICCP algorithm is not suitable for the case of large initial error of inertial navigation system (INS) because it easily leads to a total match failure. This failure brings a great challenge to an underwater terrain matching system. In this paper, the improved ICCP underwater terrain matching algorithm, a multipath parallel ICCP algorithm, is proposed. The improved algorithm is based on multibeam bathymetric data acquired by the multibeam echosounder that is one of the main instruments of water depth measurement. The data points at the center and both sides of the edge of the swath sounding data are chosen to comprise three paths that are fully utilized by the improved algorithm. Computer simulation is used to evaluate the proposed algorithm in two terrain differences areas. Simulation results verify the effectiveness of multipath parallel ICCP algorithm. The proposed algorithm can effectively solve the problem of matching divergence caused by large initial errors of INS and obtain higher location accuracy.

An Adaptive △M-ICCP Geomagnetic Matching Algorithm

In this paper, a novel method is proposed to generate the matching sequence of an ICCP algorithm for aircraft geomagnetic-aided navigation based on the M coding principle. The length of the matching sequence and the selection of the matching points directly affects the performance of the Iterated Closest Contour Point (ICCP) algorithm. This study proposes an adaptive geomagnetic matching method, ΔM-ICCP, to solve the problem of selecting suitable matching lengths, and matching points, when a vehicle is moving in a highly dynamic environment. First, the △M coding principle is adopted to select the matching points based on the information of the magnetic field, the resolution of the magnetic map, and the accuracy of the magnetic sensor. Then, the problem of selecting parameters for the △M-ICCP algorithm is turned into an optimisation problem, which can be solved by a Binary Particle Swarm Optimisation (BPSO) algorithm. Finally, the algorithm is verified through simulation experiments. The proposed algorithm can provide a basis to determine the matching length of the △M-ICCP algorithm and adaptively adjust the algorithm's parameters according to different trajectories. The algorithm is applicable even in the areas where the fluctuations of Earth's magnetic field are not significant.

Matching error of the iterative closest contour point algorithm for terrain-aided navigation

The algorithm used for terrain-aided navigation (TAN) is crucial for high performance of such a totally autonomous and long-duration underwater vehicle navigation technique. This paper presents results of an investigation into the matching errors of the revised iterative closest contour point (ICCP) algorithm for underwater TAN. In particular the quantitative relationship of the matching errors with terrain features is studied in this paper. Among 10 terrain factors, 6 of them have been shown to have the most influence on the accuracy of the revised ICCP algorithm. Three statistical methods, including multiple regression, logistic regression, and discriminant analysis, are applied to mathematically derive the different relationships between the terrain factors and the matching errors. Each formula uses no more than three terrain factors to fit the matching errors. This paper also studies the effect of other factors, including the initial error of the inertial navigation system (INS), the INS accuracy, the map resolution, the vehicle speed, the matching path length, and the sonar accuracy, on the matching errors.

An autonomous underwater vehicle positioning matching method based on iterative closest contour point algorithm and affine transformation

An improved autonomous underwater vehicle federal integrated navigation method based on two-stage position matching technology is proposed in this study to solve the problem of positioning divergence caused by the defects of error accumulation in an inertial navigation system. The improved two-stage position matching method includes the acquisition and preprocessing of the strap-down inertial navigation system and indicates track sequences and elevation measurement sequences. The first-stage position matching is based on iterative closest contour point, while the second-stage position matching is based on an affine algorithm. Simulation results indicate that the proposed algorithm solves the problem of the limitation of the rigid transformation of traditional iterative closest contour point and effectively resolves the matching failure caused by the large initial position error of long-time navigation.

Study on Initial Gravity Map Matching Technique Based on Triangle Constraint Model

In this paper, a gravity map-matching algorithm is proposed based on a triangle constraint model. A high-accuracy triangle constraint model is constructed by using a short time and high-accuracy-featured inertial navigation system. In this paper, the principle of the gravity map-matching algorithm based on the triangle constraint model and a triangle matching parameter-parsing method are first introduced in detail. It is verified by test that the method is sensitive to the initial error value. By comparison to the commonly used Iterative Closest Contour Point (ICCP) and Sandia Inertial Terrain Aided Navigation (SITAN) algorithms respectively, the results show that this method is perfect in real-time performance and reliability, and its advantages are more obvious especially with a large initial error.

Integration Matching Algorithm on Gravity Aided Underwater Navigation

Marine gravity aided INS navigation, in which Gravity anomaly and gravity gradient of earth gravity field from sensors sensitive to gravity have been supplementary information aided to correct positioning error accumulated with time to achieve precise navigation and graphic trace, has been a fervent and frontier issue in underwater passive navigation technology in application to Autonomous Underwater Vehicle (AUV). Core theoretical technology of gravity aided INS navigation was matching algorithm, in which Terrain Contour Matching (TERCOM), Sandia Inertial Terrain Aided Navigation (SITAN) and Iterative Closest Contour Point (ICCP) were three typical representatives based on gravity reference map. However, these algorithms more or less had some limitations in certain conditions such as a relatively larger INS positioning error. Aiming at the application limitation that positioning error from INS were commonly large when after a long voyage AUVs entered into the region in which gravity could be used to matching, above three algorithms were integrated to use on base of expounding their algorithm models, computation progress and advantages and disadvantages analysis, then integration theory, algorithm design and model foundation were presented to meet practice application demand better.

Underwater map-matching aided inertial navigation system based on multi-geophysical information

In order to achieve long-term covert precise navigation for an underwater vehicle, the shortcomings of various underwater navigation methods used are analyzed. Given the low navigation precision of underwater map-matching aided inertial navigation based on single-geophysical information, a model of an underwater map-matching aided inertial navigation system based on multi-geophysical information (gravity, topography and geomagnetism) is put forward, and the key technologies of map-matching based on multi-geophysical information are analyzed. Iterative closest contour point (ICCP) map-matching algorithm and data fusion based on Dempster-Shafer (D-S) evidence theory are applied to navigation simulation. Simulation results show that accumulation of errors with increasing of time and distance are restrained and fusion of multi-map-matching is superior to any single-map-matching, which can effectively determine the best match of underwater vehicle position and improve the accuracy of underwater vehicle navigation.