Terrain Referenced Navigation Research Articles

Modified interactive multiple model particle filter for terrain referenced navigation with classification error minimisation strategy

AbstractThe authors propose an innovative solution to address challenges in terrain‐referenced navigation (TRN). The suggested solution is the interactive multiple‐model particle filter with a classification error minimisation strategy (IMM‐CPF) based on decision theory. TRN is a technique that estimates position by comparing measured terrain altitude to the digital elevation model and critically depends on obtaining accurate altitude measurements. However, these measurements can be easily contaminated to not only from sensor errors but also from vegetation effects. The TRN measurement noise model is characterised as a multi‐modal density, and it reveals an overlap between two density functions, with the mixture weight parameter varying based on surface environmental conditions. This variability can potentially degrade estimation accuracy. The proposed approach integrates truncated likelihoods into the mode estimation process to enhance mode estimation capability using a classification error minimisation strategy. The proposed strategy is based on decision theory and has been modified to be suited in the IMMPF form. The effectiveness of the proposed IMM‐CPF method is verified through simulations conducted under diverse surface conditions, demonstrating significant improvements in estimation accuracy compared to conventional algorithms. Furthermore, the significance of this method is presented in terms of computational cost and robustness.

The Terprom® Digital Terrain System (DTS)

Developed specifically for tactical military aircraft operations, TERPROM® is the world leading Digital Terrain System, offering a range of capabilities that can provide both safety and tactical benefits to the aircraft. The system is an established and well-proven product that has been in production since 1991. To date it has been selected by fourteen Nations worldwide for use on numerous platforms. The number of military aircraft equipped with TERPROM® is now over 5000 and growing. TERPROM® provides the following functions: Terrain Referenced Navigation (TRN), Predictive Ground Collision Avoidance System (PGCAS), Advanced Terrain Avoidance Cueing (ATAC), Obstruction and Wires Warning and Cueing (OWWC), Database Terrain Following (DBTF), Air to Ground Ranging Functions, Terrain Awareness Display (TAD). At the core of the system is the Terrain Referenced Navigation (TRN) capability which navigates by combining aircraft navigation data (e.g. inertial and, or GPS) and radar altimeter measurements of height above ground level (AGI), with terrain heights alms from the stored map data, to produce a navigation solution referenced to the terrain map data. This navigation solution then allows the other TERPROM® functions to use the map data to provide their safety and tactical capabilities. Three variants of TERPROM® have been developed to satisfy the airborne platform market: - Fast Jet, Military Transport, Helicopter. TERPROM® is a living product that has and continues to receive significant internal investment to increase its capability, functionality and performance to support future opportunities. Current areas of development include: Use of higher resolution map data which will increase the performance of TERPROM® and offer enhanced capability to platforms flying nap of the earth, enhanced capability in urban areas and could also reduce any platform reliance on GPS. Integration with active forward looking sensors to provide additional information to the Navigation, Situational Awareness and Ranging functions and also to provide new capabilities such as real time mapping of unmapped terrain features and obstructions.

CYBERNETIC SECURITY OF ALTERNATIVE OFFLINE NAVIGATION FROM THE STANDPOINT OF SPLINE TECHNOLOGY

The issues of cybernetic security are considered in the aspect of effective proposals of alternatives to the satellite system in order to be able to promptly reorient to a backup positioning system in case of any technical problems. The assessment of the use of computer-specific competencies in the field of information technologies of the marine fleet as a secondary factor of end-to-end cybernetic security management is given. Information processing in a duplicate analogue of the positioning system is based on the technology of spline functions in order to extract the advantages of piecewise approximation for practical navigation purposes. The functionality of the navigator is analyzed within the framework of the «augmented reality technology» of the bridge of the future with the possibility of observational fixation of the look of the watch officer based on improved virtual professional scenarios against the background of the flow of typical navigation information. In addition to alternatives to the traditional positioning system, the requirements of space all-weather, system noise immunity and round-the-clock use in emergency situations, military conflicts and man-made disasters are formulated. In the circumstances of uncertainty of satellite systems, the problems of their vulnerability are theoretically leveled due to the intended use of navigation equipment based on other physical principles of operation. Correlation-extreme navigation through natural geophysical fields, the innovative e-LORAN project and the means of celestial navigation automated on the basis of computing resources of the onboard computer are highlighted as promising variants of the autonomous positioning principle. The research carried out in this work is combined with the results of experiments within the framework of the magnetic navigation project on the practical implementation of autonomous aviation positioning. Validation of the characteristics of the isotropic field as an informative standard of correlation-extreme navigation is confirmed by practical implementations of various fragments of geophysical fields in the form of three-dimensional visualizations of spline synthesis. The accuracy of geolocation with «terrain-referenced navigation» by extreme indicators is analysed. The forecast of achievability of the predictability effect of a mobile object location in a satellite-based environment is given with optimal motion control using predictive modeling, provided that an accurate assessment of the uncertainty of the navigation system is foreseen. The algorithms tested on the methods of spline functions to ensure authoritative positioning are performed as an intellectual support for the ship’s management staff in an emergency situation.

Lightweight Marginalized Particle Filtering With Enhanced Consistency for Terrain Referenced Navigation

Lightweight Marginalized Particle Filtering With Enhanced Consistency for Terrain Referenced Navigation

Error modelling method of extended Kalman filter‐based terrain referenced navigation system for integrity assurance under nominal conditions

Navigation safety is further emphasised for terrain referenced navigation (TRN) systems used in middle-range unmanned aerial vehicles (UAVs) than those in cruise missiles flying through mountainous areas, because middle-range UAVs may fly to regions and cities containing both combatants and civilians. For safety assurance of TRN systems, this paper proposes a method to compute protection levels (PLs) to assure the required level of integrity under nominal conditions. For obtaining PLs of TRN systems, all the components of the TRN measurement errors should be identified and overbounded. This study models every error component: the radar altimeter error, the vertical error in the terrain elevation database, the lateral offset induced error, and the interpolation error. Based on the characteristics of TRN systems using terrain information for obtaining navigation solutions, this study developed new models for the lateral offset induced error and the interpolation error as a function of terrain roughness by examining the effect of terrain roughness on the error sources. The simulation results demonstrate that the latitude and longitude PLs obtained by applying the proposed models can sufficiently overbound the navigation solution error for TRN systems under nominal conditions; especially the proposed method shows remarkable performance in regions with low terrain roughness.

Topographic SLAM Using a Single Terrain Altimeter in GNSS-Restricted Environment

In a Global Navigation Satellite System (GNSS)-restricted area, a mobile robot navigation system exploits surrounding environment information. For an aerial or underwater vehicle, undulating terrain of a land or seabed surface is a valuable information resource that leads to the development of terrain-referenced navigation (TRN) algorithms. However, due to the vast amount of a vehicle’s activity area, surveying all the regions to obtain a high-resolution terrain map is impractical and requires simultaneous localization and mapping (SLAM) as a highly desirable capability. This paper presents a topographic SLAM algorithm using only a single terrain altimeter, which is low-cost, computationally efficient, and sufficiently stable for long-term operation. The proposed rectangular panel map structure and update method enable robust and efficient SLAM. As terrain elevation changes are inherently nonlinear, an extended Kalman filter (EKF)-based SLAM filter is adopted. The feasibility and validity of the proposed algorithm are demonstrated through simulations using terrain elevation data from a real-world undersea environment.

Point-Mass Filtering With Boundary Flow and Its Application to Terrain Referenced Navigation

Point-mass filter (PMF) is a numerical Bayesian filtering algorithm that estimates the probability density of state variables using a deterministically defined grid on state space. An important factor that determines the performance of the PMF is how accurately one guesses the probabilistically significant region (called a nonnegligible region) where a grid will be placed. In this article, we introduce the concept of boundary to express the nonnegligible region and propose a method to define a grid tailored to the nonnegligible region of posterior density by transporting boundary according to log-homotopy induced flow. The proposed method ensures accurate estimation performance and robustness especially for uncertain prior information. Terrain referenced navigation (TRN) is composed of a nonlinear observation model defined by terrains and an uncertain initial position. Therefore, it is a major application of the PMF, which can handle nonlinearity and has a more robust characteristic than other numerical filters. In order to compare the proposed PMF with the conventional PMFs, this article applies the proposed method to TRN. Monte Carlo simulation results show that the proposed algorithm is more accurate and robust than the conventional PMFs.

Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

Terrain‐referenced navigation using a steerable‐laser measurement sensor

The benefits of GNSS have created dependencies on navigation in modern day aviation systems. Many of these systems operate with no backup navigation source. This makes the capabilities supported by precise navigation vulnerable. This paper investigates a contemporary approach to terrain-referenced navigation (TRN), used to preserve an aircraft’s navigation solution during periods of GNSS denial. Traditionally, TRN has been accomplished using a single measurement sensor pointed nadir to an aircraft. Although shown to be effective, this approach limits the achievable navigation accuracy by restricting the measurable terrain gradients to those below an aircraft. This paper explores an alternative approach to TRN that maximizes navigation information through optimal steering of a gimbaled laser. A Cramer-Rao lower bound analysis as well as a high-fidelity simulation establishes the utility of optimal steering while employing TRN. This original approach to TRN shows the order of magnitude improvements in navigation performance over existing methods.

A Pragmatic Approach to the Design of Advanced Precision Terrain-Aided Navigation for UAVs and Its Verification

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. We also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, an interferometric radar altimeter (IRA) is used to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real-time on board. Finally, the performance of the proposed system is verified through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

DESCENT TRAJECTORY MODELLING FOR THE LANDING SYSTEM PROTOTYPE

This paper gives another view on a method used for aircraft approach and landing phase of flight that enables replacement of standard glideslope. Proposed Landing System is based on Terrain Reference Navigation (TRN) using own created terrain elevation database, based on Radar Altimeter (RA) measurements compared to the overflown terrain. Simulations were performed on a chosen airport (KSC – Košice Airport) and aircraft (Boeing 737-800), where descend procedures was designed based on real airline data in compliance with Initial 4D Trajectory (i4D). Descend trajectory was modelled with EUROCONTROL Base of Aircraft DAta (BADA) performance model as a Continuous Descent Approach (CDA) from proposed merging point to the KSC RunWaY (RWY) threshold. This method was proposed to enhance pilot situational awareness in situations when standard Instrument Landing System (ILS) information could be lost or misleading and without the need of any ground station for successful navigation and guidance to the RWY threshold. Landing System prototype flight test were performed on full mission flight simulator.

Moment Matched Gaussian Kernel and Region Representative Likelihood for Performance Improvement of PMF-based TRN

This paper proposes two techniques to improve the performance of Point Mass Filter (PMF) based Terrain Referenced Navigation (TRN). The first is the kernel generation technique for diffusion operation of the probability in the time propagation phase of the PMF, which matches the moments of the Gaussian distribution considering the length of the kernel. The second is a technique for calculating the likelihood for a representative region of a grid instead of calculating the likelihood at a grid in the measurement update stage of the PMF. Applying the proposed methods, it is possible to solve the overconfidence tendency problem of the conventional PMF and improve the stability of the filter, especially when the grid resolution is limited to a certain level or more because of the calculation amount. TRN simulation based on an interferometric radar altimeter (IRA) is performed to verify the effectiveness of the proposed technique and its results are described.

Two Stage Particle Filter Based Terrain Referenced Navigation for Computational Efficiency

A novel particle filter (PF) based terrain referenced navigation (TRN) is presented for computational efficiency in this paper. TRN is a navigation method using terrain elevation information, mainly using a Bayesian filter such as a PF.PF based TRN has a disadvantage in that it requires a large number of particles to achieve a good estimation performance, which increases the computationalburden.To solve this disadvantage, two stage particle filter (TSPF) is proposed. TSPF consists of two stages and estimates the nonlinear state variables and the linear state variables separately. In the first stage, the nonlinear state variables are estimated by PF. Then, the linear state variables are estimated by a Kalman filter in the second stage considering a correlation between nonlinear and linear state variables. Since TSPF estimates only nonlinear state variables using PF and estimates linear state variables with only one Kalman filter, the computational efficiency can be greatly improved although the estimation performance is degraded a bit.In order to verify the proposed method, simulationsare performed to compare TSPF with PF andRao Blackwellized PF (RBPF)which has the same purpose as TSPF. The simulation results show similar estimation performance with fewer particles than with all state variables estimated using PF, resulting in less computational burden.

지형 대조 항법을 위한 면역 입자 필터 설계

지형 대조 항법을 위한 면역 입자 필터 설계

CRLB Analysis for a Robust TRN Based on a Combination of RNN and PF

Terrain-referenced navigation (TRN) is a technology that estimates the position of an aircraft by comparing the terrain elevation on a digital elevation model (DEM) and the altitude measured by an altimeter. The particle filter (PF)-based TRN has been widely used for unmanned aerial vehicles (UAVs) operating at a high altitude. Even though TRN performs well in rough and unique terrains, its performance degrades on flat and repetitive terrain. For this study, a robust PF-based TRN was designed, which uses a recurrent neural network (RNN)-based deep learning method to function on flat and repetitive terrains. Noise covariances and the measurement model of the PF were also trained. An RNN is an artificial neural network that recognizes patterns from a time-series. Due to the difficulty in theoretically verifying that the model generated by the deep learning method was designed optimally, we performed a Cramer–Rao lower bound (CRLB) analysis to evaluate how close the proposed method was to the optimal design. We also used a perturbation error model of an interferometric radar altimeter (IRA) as the measurement noise covariance needed for an accurate CRLB analysis.

확률적 일괄처리 지형대조항법 알고리즘

확률적 일괄처리 지형대조항법 알고리즘

Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine

A high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN). However, the higher the resolution of the DEM, the bigger the memory size needed for storing it. It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles. In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function. The TRN results using the proposed method are compared with existing studies on various DEM fitting methods. This study verifies that the proposed method obtains improved fitting accuracy and TRN performance over existing DEM fitting methods such as bilinear interpolation, SVM for regression, and bi-spline neural network, without the DEM storage space.

Precise Bathymetry as a Step Towards Producing Bathymetric Electronic Navigational Charts for Comparative (Terrain Reference) Navigation

Bathymetric Electronic Navigational Charts (bENCs) contain only bathymetry data and can be used in applications such as underwater positioning, dredging and piloting. According to International Hydrographic Organization (IHO) standard S-57, Electronic Navigational Charts (ENCs) contain depth information with pure density of depth contours. Typical depth contours encoded by Hydrographic Offices are limited to 2, 5, 10 and 20 m. Availability of more depth contours in bENCs would allow the visualisation of a safety contour which is closer to users' specific needs, especially in restricted waters such as ports, lakes and rivers. Another problem is non – Global Navigation Satellite System (GNSS) Unmanned Underwater Vehicle (UUV) navigation. bENCs could be used as reference data for UUV comparative navigation. This is called terrain reference navigation. This article presents the results from bathymetric data processing that was performed to convert data contained in bENCs into a reference for underwater comparative navigation. We use data obtained using a multibeam echo sounder to produce depth data with a horizontal spacing of 0·10 m that is suitable for use in restricted waters. The experimental data was collected in and around the Port of Gdansk, Poland.

Parameter Estimation of Radar Noise Model for Terrain Referenced Navigation Using a New EM Initialization Method

In this paper, we present an expectation-maximization (EM) initialization method for estimation of unknown parameters in a radar noise model. We deal with radar based terrain referenced navigation (TRN) over vegetated areas, where the radar noise was modeled as a double-mode Gaussian mixture with two unknowns. The known parameters are used for EM initialization along with innovation samples. Through TRN simulation, it is shown that the proposed EM initialization method outperforms random or k-means initialization methods in terms of both the parameter estimation error and the vehicle position error.

Accurate Measurement Calculation Method for Interferometric Radar Altimeter-Based Terrain Referenced Navigation.

In order to improve the performance of Terrain Referenced Navigation (TRN), an Interferometric Radar Altimeter (IRA) has been developed as a more accurate altimeter. The IRA outputs not only the relative distance (slant range, R) but also the cross-track angle (look angle, θ) of the closest point on the zero Doppler line by using the principle of interferometry and two or more antennas. To perform TRN using the IRA, the 3D relative position of the closest point should be calculated. There is a formula to calculate the relative position of the closest point using the Euler angles. However, in an actual flight environment in which the influence of wind exists, the angle of attack, the side slip angle and “the effective look angle” should be used rather than the Euler angles. In this paper, a new formula for calculating the relative position of the closest point is proposed and mathematically derived. The proposed formula was verified with real data from actual flight. The flight test results show that the positions of the closest points calculated using the conventional method and the proposed method are different because of the wind effect. The TRN simulation results indicate that the proposed formula calculates the closest points more accurately than the conventional formula.