Sandia Inertial Terrain-Aided Navigation Research Articles

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

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.

An Improved Approach of Terrain-Aided Navigation Based on RBPF

Rao-Blackwellized Particle Filter (RBPF) is suitable for solving the linear/nonlinear mixed Terrain-Aided Navigation (TAN) problem. But the Particle Filter (PF) part of RBPF is Standard Particle Filter (SPF), causing particle diversity reduction and even filters divergence under extreme conditions. To get a better estimation of the errors of INS, this paper proposes an improved approach called Regularized Rao-Blackwellized Particle Filter (RRBPF). After updating the nonlinear state and corresponding importance weights, RRBPF resamples from the Epanechnikov kernel and then get the resampled particles through a linear transition process. Theoretically, the resampling part of RRBPF is equivalent to resampling from the approximated continuous posterior probability density function. Shuttle Radar Topography Mission (SRTM) terrain data is used in simulations to investigate the performance of RRBPF. Results show that RRBPF can provide more accurate estimation of TAN and bear larger initial position error than Sandia Inertial Terrain Aided Navigation (SITAN).

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.

Terrain-aided navigation using the Viterbi algorithm

Without some form of correction, an inertial navigation system's (INS's) positional error grows without bound over time. Several techniques have been used to reduce the INS's positional error. One of these techniques, terrainaided navigation (TAN), has been widely researched over the last several decades with the research focusing on two methods: Sandia inertial terrain-aided navigation (SITAN), which is based on an extended Kalman filter (EKF), and terrain contour matching, which is based on correlation techniques. These methods are applied most successfully to fixed-wing aircraft flying over specific types of terrain; they perform poorly for low-velocity, highly maneuverable aircraft such as helicopters, especially when flying over flat or very rough terrain. In this paper, we introduce VATAN, a new TAN method based on the Viterbi algorithm (VA), and compare its performance to an implementation of SITAN based on a single EKF. Simulation results show that the VA in VATAN overcomes divergence problems associated with the EKF in SITAN and provides position estimates with smaller averagesquared error. These results show that VATAN has the potential to provide good performance for low-velocity aircraft flying over all types of terrain.

Terrain-Aided Navigation Test Results in the AFTI/F-16 Aircraft

Sandia National Laboratories has developed a low-level attack aircraft terrain-aided navigation algorithm for the Air Force Wright Aeronautical Laboratories, Avionics Laboratory. This algorithm is based on the Sandia Inertial Terrain-Aided Navigation (SITAN) approach to terrain-aided navigation that was initially formulated at Sandia in the mid-1970s. Extended Kalman filter theory is used to provide essentially continuous terrain-aided navigation through estimation of inertial navigation system errors from radar altimeter ground clearance measurements and on-board digital terrain elevation data (DTED). The SITAN algorithm is integrated into the Advanced Fighter Technology Integration (AFTI)/ F-16 aircraft, and called AFTI/SITAN to distinguish it from other SITAN designs. AFTI/SITAN performance, as determined by Air Force tracking radar during real-time testing in the AFTI/F-16 aircraft at Edwards Air Force Base from September 1986 through April 1987, is presented. Required performance is less than 100 m median horizontal radial error over 200 nmi trajectories that are flown over gently rolling terrain with available DTED. The flight data support the conclusions that AFTI/SITAN reliably determines aircraft position within an initial 0.5 nmi, CEP horizontal position error, and tracks aircraft horizontal position with an accuracy of 75 m median radial error using DTED supplied by the Defense Mapping Agency. Although accurate estimation of absolute altitude is not required for proper SITAN operation, altitude is estimated with less than 17 m root-mean-square error.

Terrain-aided navigation of an unpowered tactical missile using autopilot-grade sensors

This paper assesses the feasibility of applying a terrain-aided navigation concept, using nonlinear Kalman filtering techniques, to an unpowered tactical missile. The brief flight time and predictable missile flight path dynamics lead to the definition of a velocity-based strapdown navigation algorithm which utilizes the existing sensors. The navigation states are corrected by the error estimates of the Kalman filter which uses frequent radar altimeter measurements and terrain slope information (derived from an onboard digitized map) to provide essentially continuous position updating. Covariance analysis and Monte Carlo simulation techniques are applied to predict system performance. Results are obtained which indicate that the accuracy of this weapon/guidance combination may be sufficient to defeat airfields with area munitions. HIS work was initiated in 1981 to study the application of autonomous terrain-aided navigation to the U.S. Air Force GBU-15 weapon. The purpose was to assess the feasibility of applying the Sandia Inertial Terrain-Aided Navigation (SITAN) guidance concept to the GBU-15 weapon system, as an alternative to the existing guidance concept that employs manual target acquisition and an electro-optic al seeker for terminal guidance. This combination of the GBU- 15 weapon and the SITAN concept was perceived to be a cost- effective solution to the Air Force requirements for an airfield defeat system with all-weather and day/night capabilities. GBU-15 The GBU-15 missile is currently in production for the U.S. Air Force and is an outgrowth of the original smart bombs which were first used in the Vietnam conflict. It is an un- powered vehicle that can be launched below 1000 ft at ranges on the order of 5 miles. It was designed as a modular concept and is composed of a 2000 Ib bomb, a control section in the rear, and a television guidance unit on the front end. The control section also includes data link hardware that allows an operator in the launch aircraft to steer the weapon after launch by viewing the video seeker output. Thus it is the role of the man-in-the-loop to accomplish the target acquisition function by positioning cross-hairs on the desired target. Tracking can be done either manually by the operator or by switching to the automatic track circuitry in the missile which utilizes an edge (contrast) tracking scheme. The weapon has an analog autopilot and employs either acceleration or at- titude control, depending on the mission epoch. The autopilot sensors include a directional/vertical (D/V) gyro, a roll gyro, and two body-mounted accelerometers. All of these sensors are considered to be autopilot-grade devices—that is, their requirements were originally specified without any need to perform a navigation function.