Terrain-aided Navigation System Research Articles

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.

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.

Optimal selection and adaptability analysis of matching area for terrain aided navigation

The existing terrain aided navigation (TAN) has the disadvantage that the navigation performance is limited by the size of matching area and terrain features. Therefore, this study proposes a method of optimal selection and adaptability analysis of matching area for TAN. According to the heading angle of the carrier, inertial navigation system positioning error and performance index, the optimal matching area is selected in real time. First, the matching area selection and adaptability analysis are carried out, and the influence of the matching area on the positioning performance is analysed by using the Beijing Internal TAN II algorithm. Then, the model parameters of the optimal matching area are determined through theoretical calculation and simulation. Finally, it is compared with the typical matching area selecting method. The test results show that the optimal selection and adaptability analysis method can improve the computational efficiency of TAN system, and improve the positioning accuracy of TAN system.

Study on the Arctic Underwater Terrain-Aided Navigation Based on Fuzzy-Particle Filter

The ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation. Then, a Terrain-Aided Navigation (TAN) system based on the Particle Filter (PF) is constructed to estimate the state of AUV position by particles. Particles of a random distribution of fixed variance can effectively track targets. However, a fixed variance distribution is not well adapted to many different situations. To improve navigation accuracy and robustness, fuzzy logic is used to estimate the distribution variance of particles under the current terrain gradient dynamically. The simulation results show that our proposed Fuzzy-PF TAN system is robust under various current disturbance situations. The position error of our system is within a map resolution unit of 500 m.

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.

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.

Design of Rao–Blackwellized Point-Mass Filter With Application in Terrain Aided Navigation

This paper is devoted to the terrain aided navigation (TAN) systems based on the state estimation algorithms. In particular, the emphasis is laid on the design of the Bayesian Rao–Blackwellized point-mass filter for nonlinear state-space models of a specific structure typically used in the terrain aided navigation. The proposed filter preserves advantages of the point-mass filter, including the high estimation accuracy, robust initialization, deterministic nature of the algorithm, and predictable computational complexity, while the computational complexity is significantly reduced. The performance of the Rao–Blackwellized point-mass filter is illustrated using a simulated numerical example and a TAN system based on the real data.

A dynamic path planning method for terrain-aided navigation of autonomous underwater vehicles

Terrain-aided navigation (TAN) is one of the most effective approaches for solving the long-range navigation of autonomous underwater vehicles. However, the positioning accuracy of TAN can be greatly influenced by the terrain information of matching areas. A TAN system might also fail catastrophically when the seabed topography changes. To address these problems, a dynamic path planning method for TAN, which includes environment modelling, offline path planning, and online re-planning, is proposed in this paper. Terrain standard deviation is applied to represent terrain information in environment modelling, and the Mahalanobis distance and roulette approach are introduced in the offline path planning approach. Furthermore, the topographic changes are identified and handled via an online re-planning approach. Simulation experiments are conducted, and comparisons are made with the A* algorithm. According to the experimental results, the proposed method improves the positioning accuracy of TAN during the mission, and its ability to deal with seabed topographic changes is also verified.

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 real-time airborne terrain aided inertial navigation system and its performance analysis

The current terrain aided navigation system has a shortcoming that it depends on terrain feature, so we need to select the terrain matching area and plan the flight path according to the flight mission in advance. In this paper, we present a real-time airborne terrain aided inertial navigation system. The system can plan and select the matching area online according to the heading angle and estimated position errors during aircraft flight, and adopt the appropriate terrain-aided navigation algorithm for different terrain areas and flight conditions. So it can ensure the positioning accuracy and improve the ability of carrier to maneuver at the same time. In order to verify the performance of the system, a flight simulation experiment and a real flight test are carried out, and we conduct a research on the performance of matching area planning and real-time positioning. The tests results show that the system can improve the ability of carrier to maneuver and the performance of real-time positioning. In addition, the system has a higher positioning accuracy and reliability than traditional TERCOM and SITAN methods. The position error is less than 50 m in acquisition mode and less than 18 m in tracking mode with the terrain elevation map of 25 m-resolution.

An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles.

Terrain-aided navigation is a potentially powerful solution for obtaining submerged position fixes for autonomous underwater vehicles. The application of terrain-aided navigation with high-accuracy inertial navigation systems has demonstrated meter-level navigation accuracy in sea trials. However, available sensors may be limited depending on the type of the mission. Such limitations, especially for low-grade navigation sensors, not only degrade the accuracy of traditional navigation systems, but further impact the ability to successfully employ terrain-aided navigation. To address this problem, a tightly-coupled navigation is presented to successfully estimate the critical sensor errors by incorporating raw sensor data directly into an augmented navigation system. Furthermore, three-dimensional distance errors are calculated, providing measurement updates through the particle filter for absolute and bounded position error. The development of the terrain aided navigation system is elaborated for a vehicle equipped with a non-inertial-grade strapdown inertial navigation system, a 4-beam Doppler Velocity Log range sensor and a sonar altimeter. Using experimental data for navigation performance evaluation in areas with different terrain characteristics, the experiment results further show that the proposed method can be successfully applied to the low-cost AUVs and significantly improves navigation performance.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

Magnetic Navigation and Tracking of Underwater Vehicles

This paper proposes novel methods with the potential to improve the performance of navigation and tracking systems in underwater environments. The work relies on well-established methods of potential field inversion and introduces a new analytic formulation designed to stabilize the solution of the inverse problem in real-time applications. The navigation method proposed exploits the terrain information associated with geomagnetic field anomalies, without the need of a priori maps. The procedure can also be applied to track a moving vehicle based on its associated disturbance of the environmental magnetic field. We envision the integration of theses methods in terrain-aided navigation systems, simultaneous localization and mapping algorithms, and tracking applications.

간섭계 레이더 고도계를 활용한 지형참조항법의 성능 분석

The paper experimentally verifies the performance of Terrain-Aided Navigation (TAN) using an interferometric radio altimeter, which is recently used due to its accuracy. First, we propose a TAN system that utilizes an interferometric radio altimeter as a measurement system. Second, we implement extended Kalman filter, unscented Kalman filter, and particle filter to evaluate the performance of TAN according to the selection of filters and the difference of environments.

6DoF SLAM aided GNSS/INS Navigation in GNSS Denied and Unknown Environments

This paper presents the results of augmenting 6DoF Simultaneous Localisation and Mapping (SLAM) with GNSS/INS navigation system. SLAM algorithm is a feature based terrain aided navigation system that has the capability for online map building, and simultaneously utilising the generated map to constrain the errors in the on-board Inertial Navigation System (INS). In this paper, indirect SLAM is developed based on error analysis and then is integrated to GNSS/INS fusion filter. If GNSS information is available, the system performs feature- based mapping using the GNSS/INS solution. If GNSS is not available, the previously and/or newly generated map is now used to estimate the INS errors. Simulation results will be presented which shows that the system can provide reliable and accurate navigation solutions in GNSS denied environments for an extended period of time.

Autonomous airborne navigation in unknown terrain environments

We address the issue of autonomous navigation, that is, the ability for a navigation system to provide information about the states of a vehicle without the need for a priori infrastructure such as GPS, beacons, or a map. The algorithm is known as simultaneous localisation and mapping (SLAM) and it is a terrain aided navigation system (TANS) which has the capability for online map building, and simultaneously utilising the generated map to bound the errors in the navigation solution. Since the algorithm does not require any a priori terrain information or initial knowledge of the vehicle location, it presents a powerful navigation augmentation system or more importantly, it can be implemented as an independent navigation system. Results are first provided using computer simulation which analyses the effect of the spatial density of landmarks as well as the quality of observation and inertial navigation data, and then finally the real time implementation of the algorithm on an unmanned aerial vehicle (UAV).

Decentralized approach to unmanned aerial vehicle navigation: Without the use of the global positioning system and preloaded maps

In this paper the issue of autonomous navigation is addressed, i.e. the ability for a navigation system to provide information about the states of a vehicle without the need for a priori infrastructures such as the global positioning system (GPS), beacons or preloaded maps of the area of interest. The algorithm applied is known as simultaneous localization and mapping (SLAM). It is a terrain-aided navigation system that has the capability for on-line map building, while simultaneously utilizing the generated map to bound the errors in the navigation solution. As no a priori terrain information nor initial knowledge of the vehicle location is required, this algorithm presents a powerful navigation augmentation system. More importantly, it can be implemented as an independent navigation system. This paper also describes a decentralized SLAM algorithm that allows multiple vehicles to acquire a joint three-dimensional map via a decentralized information fusion network. The key idea behind this decentralized SLAM is to represent the map in information form (negative log likelihood) for communication. Experimental results are provided using computer simulation to demonstrate the single-vehicle and multi-vehicle SLAMs without the use of GPS and preloaded maps.

Improvement of strapdown inertial navigation using PDAF

A new application of PDAF (probabilistic data association filter) for improving the accuracy of autonomous strapdown inertial navigation systems (SINS) is presented. The proposed method is a terrain-aided navigation (TAN) algorithm based on landmark detection combined with a classical SINS. It is shown via a set of simulations that the method can improve significantly the precision of autonomous navigation if the landmark spatial density and quality of landmark detectors are good enough. This new concept of navigation called PDANF (probabilistic data association navigation filter) can be integrated with a relatively low cost in existing operational TAN systems.

Terrain Aided Navigation System Combines with Moving Colour Maps

A terrain‐aided navigation system, developed by Sandia National Laboratories about 10 years ago to guide a weapon to its target, has found a new use. It has been combined with computer‐generated moving colour maps so that operators of military land vehicles can continuously fix their location by simply watching a cockpit TV screen that displays the map.