Terrain-aided Navigation Research Articles

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 navigation using Bayesian statistics

The performance of terrain-aided navigation of aircraft depends on the size of the terrain gradient in the area. The point-mass filter (PMF) described in this work yields an approximate Bayesian solution that is well suited for the unstructured nonlinear estimation problem in terrain navigation. It recursively propagates a density function of the aircraft position. The shape of the point-mass density reflects the estimate quality; this information is crucial in navigation applications, where estimates from different sources often are fused in a central filter. Monte Carlo simulations show that the approximation can reach the optimal performance, and realistic simulations show that the navigation performance is very high compared with other algorithms and that the point-mass filter solves the recursive estimation problem for all the types of terrain covered in the test. The main advantages of the PMF is that it works for many kinds of nonlinearities and many kinds of noise and prior distributions. The mesh support and resolution are automatically adjusted and controlled using a few intuitive design parameters. The main disadvantage is that it cannot solve estimation problems of very high dimension since the computational complexity of the algorithm increases drastically with the dimension of the state space. The implementation used in this work shows real-time performance for 2D and in some cases 3D models, but higher state dimensions are usually intractable.

A Bayesian Approach to Terrain-Aided Navigation

The terrain-aided navigation problem is a highly nonlinear estimation problem with application to aircraft navigation and missile guidance. In this work the Bayesian approach is used to estimate the aircraft position. With a quantization of the state space an implementable algorithm is found. Problems with low excitation, rough terrain and parallel position hypothesis are handled in a reliable way. The algorithm is evaluated using simulations on real terrain databases.

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.

Improvement of terrain-aided navigation via trajectory optimization

This paper proposes a novel method for improving the accuracy of terrain-aided (TA) navigation algorithms. The problem is designing airframe trajectories in a priori mission planning, so that navigation errors which propagate along with these algorithms are minimal. We analyze the terrain via information theory and the FFT (fast Fourier transform-spatial) algorithm; then we map it using a probabilistic variable, the entropy. The A* optimization algorithm is then applied to produce minimum-error TA-navigation trajectories. Finally, a posteriori superposition of the airframe maneuver constraints renders a suboptimal but implementable solution. The proposed method has been applied to terrains with different information-content levels. Its efficiency, as reflected in an entropy-based cost function, is demonstrated along optimal versus straight-line trajectories, down towards required destinations. >

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 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.

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.

Nonlinear Kalman filtering techniques for terrain-aided navigation

The application of nonlinear Kalman filtering techniques to the continuous updating of an inertial navigation system using individual radar terrain-clearance measurements has been investigated. During this investigation, three different approaches for handling the highly nonlinear terrain measurement function were developed and their performance was established. These were 1) a simple first-order extended Kalman filter using local derivatives of the terrain surface, 2) a modified stochastic linearization technique which adaptively fits a least squares plane to the terrain surface and treats the associated fit error as an additional noise source, 3) a parallel Kalman filter technique utilizing a bank of reduced-order filters that was especially important in applications with large initial position uncertainties. Theoretical and simulation results are presented.