Underwater Terrain-aided Navigation Research Articles

Research Advances and Prospects of Underwater Terrain-Aided Navigation

Research Advances and Prospects of Underwater Terrain-Aided Navigation

A contour-based path planning method for terrain-aided navigation systems with a single beam echo sounder

Underwater terrain-aided navigation (TAN) methods with a single beam echo sounder (SBES) have the potential to facilitate long-term underwater navigation for underwater vehicles. However, the limited bathymetric measurement of the SBES often makes the accuracy and robustness of TAN navigational results greatly affected by the complexity of the terrain. Therefore, effective path planning for SBES-TAN is essential. This paper proposes a contour-based path planning method for SBES-TAN, which can yield robust navigational results even with noisy SBES measurements. Moreover, both the density and smoothness of contour lines are used to quantify terrain complexity. Moreover, a fuzzy comprehensive evaluation-based theory is used to identify TAN-suitable areas. Path planning is accomplished using the rapid-exploration random tree* algorithm, while a Monte Carlo-based TAN estimate handles the challenges posed by noisy SBES measurements. The performance of the proposed method has been verified through simulation using field bathymetric data, by comparing it with state-of-the-art methods.

A robust particle filter for ambiguous updates of underwater terrain-aided navigation

This paper proposes a novel method to improve the robustness of particle filtering (PF) in relatively flat terrain that lack sufficient features for accurate positioning. In terrain -aided navigation (TAN), similar terrain profiles lead to multimodality in the posterior distribution. It not only reduces the accuracy of current position estimation, but also affects the subsequent estimation of PF, and even causes filter divergence. When no additional information is available, the historical estimation is employed to correct ambiguous updates caused by multimodal posteriors. First, a strategy for identifying ambiguous updates is proposed by analyzing the particle set distribution using the clustering method and covariance. Inspired by out-of-sequence measurement (OOSM), once the ambiguous updates are detected, the ambiguous estimates are corrected by introducing high-quality previous information. Moreover, an efficient solution is provided for the storage and computation requirements of OOSM within the PF framework. To verify the effectiveness of the proposed algorithm, simulation and experimental validation are designed. By comparing with PF, mixture particle filtering (MPF), and OOSMPF algorithms, the proposed algorithm demonstrates better estimation accuracy and robustness in terrain flat areas.

An outlier-robust Rao–Blackwellized particle filter for underwater terrain-aided navigation

Terrain-aided navigation (TAN) is one of the key techniques for autonomous navigation and positioning of AUVs. This paper develops a novel integrated navigation system that combines the inertial navigation system (INS) and TAN into one filter, in contrast to the conventional INS/TAN model. This method not only provides a position estimate for the carrier but also the INS error, which increases the filtering dimension significantly. Thus, the Rao-Blackwellized particle filter (RBPF) technique is introduced to address the “dimensional disaster” problem. A three-dimensional (3-D) model is established to estimate the horizontal position while compensating for the tidal inaccuracy by extending the tidal depth bias to the state space. Furthermore, the variable and complex underwater environment can potentially result in outlier interference in depth measurements, which significantly degrades the performance of the RBPF-based estimation algorithm. This paper thus presents a 3-D adaptive RBPF method based on the maximum correntropy criterion (MCC) to suppress the influence of measurement outliers on positioning precision. Simulations and experimental tests verify the effectiveness of the proposed algorithm. The results demonstrated that the 3-D model-based AMCCRBPF method can effectively improve robustness to outliers and provide an accurate estimate of the tidal depth bias.

Forward looking sonar applications for unmanned vehicles

Unmanned vehicles have the potential to greatly expand our ability to observe dynamic ocean environments. Whereas distributed acoustic sensors can monitor discrete measurement points in time and space, autonomous maritime platforms provide increased flexibility. Acoustic principles are a key component of this functionality and forward looking sonar (FLS) systems can acquire acoustically derived data products while enabling better informed autonomy behaviors based on enhanced situational and environmental awareness. This paper explores applications of forward look sonar technology when integrated with underwater vehicles. Utility of both interferometric and two-dimensional acoustic data products are examined to illustrate the impact on technologies associated with navigational drift mitigation and ocean observation. Results from sonar platform integration and in-water acquisition for development of an underwater terrain-aided navigation (UTAN) particle filter, an acoustic enabled simultaneous localization and mapping (SLAM) algorithm, obstacle avoidance techniques, and seabed observations are presented.

A robust fusion terrain-aided navigation method with a single beam echo sounder

The underwater terrain-aided navigation (TAN) method with a single beam echo sounder (SBES) can achieve long-term underwater navigation. However, due to the limited bathymetric measurements collected using a SBES, the accuracy and robustness of the TAN method are affected by a variety of factors, such as the richness of terrain features and the gradient of underwater terrain. Consequently, the SBES-TAN methods with gridded or contour priori maps cannot yield robust navigational results for long-range autonomous underwater vehicles (AUVs). To address this issue, this paper proposed a robust fusion TAN (RFTAN) method with a SBES on the basis of particle filter (PF) theory to locate a vehicle accurately. In the proposed method, both terrain positioning results provided by gridded-TAN and contour-TAN were fused to weigh a particle, with the fuzzy-theory-based fusion weight division (FWD) method. Playback experiments were conducted using 25-h-length field data collected from an at-sea experiment, and experimental results show that the proposed method can provide both robust and accurate long-term navigational results for AUVs.

Efficient Terrain-Aided Navigation System with Submap-Size Calculation and Embedded System

Abstract Underwater terrain-aided navigation (TAN) holds high potential for long-term underwater accurate navigation of autonomous underwater vehicles. TAN can locate a vehicle by calculating the similarity between an a priori map and a vehicle's real-time observation consisting of a set of bathymetric measurement points. However, the amount of measurement points in the real-time observation affects both positioning accuracy and computational consumption of the TAN system, making it challenging to calculate a suitable size of real-time observation in TAN. With a smooth seabed terrain, a small observation area leads to insufficient topographic features and finally an inaccurate matching result, while a large area with a mount of features results in high computational cost. This paper proposes a method to restrain the size of observation in TAN systems based on terrain entropy and difference of normals. Meanwhile, this paper implements the TAN algorithm into an embedded system architecture used by actual underwater vehicles that are already in service to reduce the power consumption of the TAN system. The effectiveness of the algorithm has been demonstrated through playback experiments based on a semi-physical simulation platform using a PC/104-embedded computer.

Particle filter underwater terrain-aided navigation based on gradient fitting

Inertial navigation systems (INSs) are widely used for autonomous underwater vehicle navigation, with excellent short-term precision. However, the positioning error of an INS accumulates with time. It is imperative to adopt other approaches to mitigate drift errors, especially for long-term sailing tasks. To that end, underwater terrain-based navigation (TBN) is effective in bounding the drift errors. In particular, the particle filtering (PF) method is extensively employed in TBN to tackle the highly nonlinear measurement equation. In our previous work, the statistical properties of the digital terrain model gradient were exploited and we proposed the use of three probability density functions to characterize the normalized data, i.e. Gaussian, gamma and Weibull distributions. In this paper, the likelihood was modified according to the gradient fitting results, i.e. an optimal distribution selection. Moreover, to prevent the measurement data with large gradients from generating particles with lower weights, a pre-screen procedure was proposed to stabilize particle filter sampling. As shown and demonstrated in simulations, our proposed improved PF method outperforms the comparative ones in terms of root mean square error and standard deviation, as well as accuracy, especially for long-term sailing tasks. In terms of computational cost, a smaller number of measurement data are employed and the proposed method is faster than the standard PF method.

Underwater Terrain-Aided Navigation Relocation Method in the Arctic

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

High-precision initialization and acceleration of particle filter convergence to improve the accuracy and stability of terrain aided navigation

Initial positioning errors and the low adaptability of a priori digital elevation maps result in large positioning uncertainty intervals in the initial stage of terrain-aided navigation (TAN). This produces pseudo-peaks and mismatches in the initial position likelihood function and renders the convergence of the particle filter (PF) slow and unstable, while even causing divergence. Thus, the occurrence of the “kidnapped robot problem” is highly probable during the initial stage of TAN and is a scenario frequently faced by deep-sea and ultra-long-range underwater vehicles. In this study, a PF initialization method based on non-linear multi-terrain aided fusion position (NLMTP) is proposed to improve the stability and accuracy of TAN. NLMTP uses the terrain-aided position (TAP) information during the initial stage of TAN to estimate the high-precision probability distribution of the starting position via backward smoothing. Accordingly, a PF initialization method for non-Gaussian prior distribution probability is proposed to improve the convergence speed of the PF during the initial stage of underwater TAN. Finally, a performance comparison of PF initialized via the NLMTP, TAP confidence interval, and TERCOM methods was performed using the survey data obtained via onboard sensors. The experimental results show that NLMTP initialization improves the convergence speed and positioning accuracy of PF in the initial TAN phase; this improvement is clear in the low terrain-adaptability area.

Fast Extraction of Local Underwater Terrain Features for Underwater Terrain-Aided Navigation

Terrain matching accuracy and real-time performance are affected by local underwater terrain features and structure of matching surface. To solve the extraction problem of local terrain features for underwater terrain-aided navigation (UTAN), real-time data model and selection method of beams are proposed. Then, an improved structure of terrain storage is constructed, and a fast interpolation strategy based on index is proposed, which can greatly improve the terrain interpolation–reconstruction speed. Finally, for the influences of tide, an elimination method of reference depth deviation is proposed, which can reduce the reference depth errors caused by tidal changes. As the simulation test shows, the proposed method can meet the requirements of real-time performance and effectiveness. Furthermore, the extraction time is considerably reduced, which makes the method suitable for the extraction of local terrain features for UTAN.

Path Planning of Underwater Terrain-Aided Navigation Based on Improved Artificial Potential Field Method

AbstractFor underwater terrain-aided navigation systems to collect enough terrain features, a path-planning method for terrain-aided navigation is proposed. First, a distribution model of terrain features is built based on underwater terrain Fisher information content. Then, terrain information and path length are taken as optimization targets, and considering the constraints of autonomous underwater vehicle (AUV) maneuverability, a path applicable to terrain-aided navigation can be provided. Simulation tests show that the terrain feature information in AUVs' sailing area obtained with the method is adequate; therefore, this method can effectively improve the performance of terrain-aided navigation systems.

Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering

To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle 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.

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.

Underwater terrain-aided navigation based on multibeam bathymetric sonar images

Underwater terrain-aided navigation is used to complement traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map. This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge corner pixels are then defined and used to construct an edge corner histogram, which it employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.

Entering Direction Analysis of Matching Area of Underwater Terrain Aided Navigation Based on Variation Function

In order to improve precision of underwater terrain aided navigation and make full use of the features of topographic information, an analysis method is proposed in this paper, in which the variation function is used as the measurement tool. We discuss what to explore relation between variation function and underwater terrain. Through fitting experimental variation function curve, the relationships of different directions of variation function to terrain information capacity are confirmed. Meanwhile, the analysis reveals that the matching area of underwater terrain aided navigation. The experimental results show that variation function reflects the spatial correlation of underwater terrain and the features of terrain. Then, the best entering direction of matching area is found. The certain theory proofs for selection about matching area, study of the terrain adaptation and route planning can be provided.