Search

Abstract Position and Orientation System (POS) is the key sensor of remote-sensing system. It is a typically Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS) integrated measurement system, which provides high-precision position and attitude parameters. The gravity disturbance vector is one of the main error sources of high-precision SINS. The error propagation of POS is analyzed. It shows that the vertical component of the gravity disturbance vector is introduced into the POS horizontal channel through the vertical deflections, which leads to the error of the attitude measurement of the POS. In this article, a new real-time gravity compensation method is proposed, which includes the gravity disturbance as the error states of POS Kalman filter, and the accurate gravity disturbance model is constructed by a "Block-Time Variation" Markov Model (B-TV-MM) based on high-precision gravity map, whose resolution is enhanced by a new interpolation method based on Gaussian Process Regression (GPR). A flight experiment was conducted to evaluate the efficiency of the proposed method and the results showed that the proposed method performs better compared with other real-time gravity compensation methods. [ABSTRACT FROM AUTHOR]

The Indian Regional Navigation Satellite System (IRNSS) has recently (as of May 2016) become operational. The system has been developed with the objective of offering positioning, navigation, and timing (PNT) to users in its two service areas, covering the Indian landmass and the Indian Ocean, respectively. It is the goal of this contribution to provide further insight into the full-constellation L5 pseudorange single-point positioning (SPP) capabilities of the system. A detailed dilution of precision (DOP) analysis of its two service areas, including the identification, in location and time, of poor receiver-satellite geometries is provided. It is hereby demonstrated how the impact of some of these poor receiver-satellite geometries can be mitigated by means of height-constraining. An overview and analysis of the SPP precision is also provided including easy-to-use representative day-averaged values for a grid of locations covering the two service areas. [ABSTRACT FROM AUTHOR]

The proposed scheme of construction of an integrated navigation complex of an unmanned aerial vehicle, the basis of which are two subsystems -- inertial and course-air. For each subsystem, implementation of algorithms for the complex processing of navigation information from the corresponding navigation sensors and onboard equipment of the Global Positioning System on the basis of a non-invariant compensation circuit is envisaged. The logic of using navigation measurements from both subsystems reflects the adaptation of the navigation complex to the flight conditions of the aircraft. The adaptive navigational complex unmanned aerial vehicle will improve the accuracy of the determination of navigational parameters of the flight, reliability and noise immunity of the aircraft during the flight task. [ABSTRACT FROM AUTHOR]

A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm. [ABSTRACT FROM PUBLISHER]

China has developed an airborne gravimetry system based on SINS/DGPS named SGA-WZ, the first system in which a strapdown inertial navigation system (SINS) has been used for airborne gravimetry in China. This gravity measurement system consists of a strap-down inertial navigation system and a differential global positioning system (DGPS). In April 2010, a flight test was carried out in Shandong Province of China to test the accuracy of this system. The test was designed to assess the repeatability and accuracy of the system. Two repeated flights and six grid flights were made. The flying altitude was about 400 m. The average flying speed was about 60 m/s, which corresponds to a spatial resolution of 4.8 km when using 160-s cutoff low-pass filter. This paper describes the data processing of the system. The evaluation of the internal precision is based on repeated flights and differences in crossover points. Gravity results in this test from the repeated flight lines show that the repeatability of the repeat lines is 1.6 mGal with a spatial resolution of 4.8 km, and the internal precision of grid flight data is 3.2 mGal with a spatial resolution of 4.8 km. There are some systematic errors in the gravity results, which can be modeled using trigonometric function. After the systematic errors are compensated, the precision of grid flight data can be better than 1 mGal. [ABSTRACT FROM AUTHOR]

GPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Various forms of navigation are present in today's world, leading from satellite based navigation to several archaic forms of navigation like star gazing. Now, lots of technologies are available to achieve this but with certain limitations. For example, FOG based navigation provides accuracy with in 0.10-100 range which is not sufficient for various military applications. Therefore, there is a need to design a system which will have better accuracy and thus requires development of ring laser gyro-based inertial systems. This paper concentrates on the aided navigation system based on ring laser gyro of 0.01 deg/hr class and GPS - GLONASS to further enhance the capability of system in terms of accuracy. The usage of such systems not only provides accurate results momentarily but it also persists for longer duration with the aid of GPS - GLONASS for applications like aircraft, ship and long range missiles. The system provides accuracy of the level of 1 Nm/hr in pure navigation and 30 m with the aid of GPS - GLONASS. Apart from this, the availability of gyro-compass and baro-inertial algorithms further enhances the system capabilities and made them self dependent to the major extent. [ABSTRACT FROM AUTHOR]

The computational method of range estimate is presented to deal with the problem that the accuracy of position is too low to satisfy calculating the time-to-go because the pure INS has worked for long time without GPS. The messengers used by method include sight line angle from image seeker, relative position and altitude from INS. Under typical measuring errors, the influence of sampling frequency on estimation accuracy was analyzed. The engineering application scheme and time-to-go calculation strategy were given. The simulation results show that the accuracy of the range estimate is well enough to be used in engineering. [ABSTRACT FROM AUTHOR]

The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the momentMEMSgyroscopes have poor characteristics and it's necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given [ABSTRACT FROM AUTHOR]

For airborne surveys, the GPS/INS system has become the primary source for aerial image georeferencing. However, alternative automated georeferencing is required to serve as a backup for georeferencing when GPS/INS-based georeferencing is not feasible. High-resolution satellite images (HRSI) have been globally available with better spatial resolution and increasing positional accuracy. Therefore, HRSI has high potential as a ground control source for aerial image georeferencing which usually requires accurate 3D ground control points. Unfortunately, single imagery, which is often used as a reference, contains relief displacement due to objects on the ground introducing positional errors, unless it is not true ortho-rectified which is costly and time consuming. Therefore, in this study, a stereo HBSI-based automated georeferencing approach is proposed. The use of stereo images can avoid the impact of relief displacement and requires no external height information. The proposed method is based on a multi-scale image matching approach utilizing a combination of SIFT (Scale-Invariant Feature Transform) and RANSAC (RANdom SAmple Consensus). In the georeferencing step, the bundle adjustment with outlier removal of Baarda's data snooping was utilized. Experimental results for a strip of aerial images with stereo Ikonos images showed its potential as a backup system for automated georeferencing. [ABSTRACT FROM AUTHOR]

This paper provides an extensive overview of existing vehicle dynamic state estimators that utilise the in-car Inertial Navigation Sensors and Global Positioning System. The different approaches are categorised, the techniques are summarised and the limitations and advantages of each approach are provided. Recommendations for future research are also given. The review is intended to be fairly comprehensive, but with an emphasis on vehicle sideslip estimation. [ABSTRACT FROM AUTHOR]

Currently, the concept of multisensor system integration is implemented in land-vehicle navigation (LVN) applications. The most common LVN multisensor configuration incorporates an integrated Inertial Navigation System/Global Positioning System (INS/GPS) system based on the Kalman filter (KF). For LVN, the demand is directed toward low-cost inertial sensors such as microelectromechanical systems (MEMS). Due to the combined problem of frequent GPS signal loss during navigation in urban centers and the rapid time-growing inertial navigation errors when the INS is operated in stand-alone mode, some methodologies should be applied to improve the LVN accuracy in these cases. One of these approaches is to apply smoothing algorithms such as the Rauch–Tung–Striebel smoother (RTSS), which uses only the output of the forward KF. In this paper, the development of the two-filter smoother (TFS) algorithm and its implementation in LVN applications is introduced. Two different LVN INS/GPS data sets that include tactical-grade and MEMS inertial measuring units are utilized to validate the TFS algorithm and to compare its performance with the RTSS. [ABSTRACT FROM PUBLISHER]

Air navigation has evolved over the last hundred years from an art into a mature science. Airplane accidents due to navigational errors are practically nonexistent. In large measure this advance has been due to radio aids to navigation which operate under all weather conditions. However in remote areas and in hostile airspaces, the radio aids may not be available and a fully self contained navigational device is required. Navigation by stars (celestial navigation) is feasible under such conditions. But the inertial navigation system (INS) is the preferred choice. In recent times, a navigational instrument based on radio communication with specially designed satellites in low Earth orbits called the global positioning system (GPS) has become popular. This article deals with the principles of these various methods of air navigation. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method for estimating the attitude of airborne electro-optical cameras with respect to the onboard autonomous navigation unit. The procedure is based on the use of attitude measurements under static conditions taken by an inertial unit and carrier-phase differential Global Positioning System to obtain accurate camera position estimates in the aircraft body reference frame, while image analysis allows line-of-sight unit vectors in the camera based reference frame to be computed. The method has been applied to the alignment of the visible and infrared cameras installed onboard the experimental aircraft of the Italian Aerospace Research Center and adopted for in-flight obstacle detection and collision avoidance. Results show an angular uncertainty on the order of 0.1° (rms). [ABSTRACT FROM AUTHOR]

The accurate location of a vehicle in the road is one of the most important challenges in the automotive field. The need for accurate positioning affects several in-vehicle systems like navigators, lane departure warning systems, collision warning and other related sectors such as digital cartography suppliers. The aim of this paper is to evaluate high precision positioning systems that are able to supply an on-the-centimetre accuracy source to develop on-the-lane positioning systems and to be used in future applications as an information source for autonomous vehicles that circulate at high speeds on public roads. In this paper we have performed some on-road experiments, testing several GPS-based systems: Autonomous GPS; RTK Differential GPS with a proprietary GPS base station; RTK Differential GPS connected to the public GPS base station network of the National Geographic Institute of Spain via vehicle-to-infrastructure GPRS communications; and GPS combination with inertial measurement systems (INS) for position accuracy maintenance in degraded satellite signal reception areas. In these tests we show the validity and the comparison of these positioning systems, allowing us to navigate, in some cases, on public roads at speeds near 120 km/h and up to 100 km from the start position without any significant accuracy reduction. [ABSTRACT FROM AUTHOR]

The meteorological mini unmanned aerial vehicle (M2AV) was used for measuring the meteorological wind. The wind is the vector difference between the aircraft speed relative to the earth (inertial velocity) and relative to the airflow (true airspeed). The latter was computed from five-hole-probe pressure measurements in combination with calibration–coefficient polynomials obtained during wind tunnel calibration. The aircraft inertial velocity, position, and attitude were calculated using a Kalman filter that combined data from a global positioning system (GPS) and an inertial navigation system (INS). The temporal (and spatial) resolution of the M2AV wind measurement is remarkably fine. An inertial subrange of locally isotropic turbulence can be measured up to 40 Hz (or 0.55 m at 22 m s-1 airspeed). The first M2AV wind estimation showed some systematic deviations compared to the expected values (like a constant mean wind in every flight direction). Therefore, an in-flight wind calibration technique was developed that corrects for the inaccuracy of the true heading, the constant offset of the pitch angle, and the underestimation of the true airspeed. The final adjusted wind measurements were verified during a field experiment at the measurement field of the German Meteorological Service, southeast of Berlin. The mean horizontal and vertical wind measured by the M2AV agreed well with simultaneous sodar and tower measurements. [ABSTRACT FROM AUTHOR]

This paper introduces a ?Personal Dead-reckoning? (PDR) navigation system for walking persons. The system is useful for monitoring the position of emergency responders inside buildings, where GPS is unavailable. The PDR system uses a six-axes Inertial Measurement Unit attached to the user's boot. The system's strength lies in the use of a technique known as Zero Velocity Update (ZUPT) that virtually eliminates the ill-effects of drift in the accelerometers. It works very well with different gaits, as well as on stairs, slopes, and generally on 3-dimensional terrain. The paper explains the PDR and presents extensive experimental results, which illustrate the utility and practicality of the system. Parts of this paper were presented at the 2006 International Joint Topical Meeting: ?Sharing Solutions for Emergencies and Hazardous Environments,? February 12?15, 2006, Salt Lake City, Utah, USA. [ABSTRACT FROM AUTHOR]

Integration of global positioning system (GPS) and inertial navigation system (INS) provides continuous positioning information of high accuracy due to the synergistic effect of both systems. While a Kalman filter is usually employed to fuse the GPS and INS measurements, this approach requires a priori knowledge on the stochastic and deterministic parameters of both systems. In practice, these unknown parameters are often determined by trial and error. We propose an expectation-maximization (EM) method here to estimate these unknowns in a maximum likelihood (ML) framework. In particular, we employ a delta operator model to approximate the continuous-time system instead of the conventional shift operator model. The proposed method achieves simultaneous positioning and unknown parameter estimation. To assess the performance of the proposed method, we derive the posterior Cramer-Rao hound (PCRB) of our model and compare the performance with adaptive Kalman filtering technique. Both real and simulated data are used to validate the effectiveness of the proposed EM-based method. [ABSTRACT FROM AUTHOR]

Although the ability to measure vertical eddy fluxes of gases from aircraft platforms represents an important capability to obtain spatially resolved data, accurate and reliable determination of the turbulent vertical velocity presents a great challenge. A nine-hole hemispherical probe known as the “Best Air Turbulence Probe” (often abbreviated as the “BAT Probe”) is frequently used in aircraft-based flux studies to sense the airflow angles and velocity relative to the aircraft. Instruments such as inertial navigation and global positioning systems allow the measured airflow to be converted into the three-dimensional wind velocity relative to the earth’s surface by taking into account the aircraft’s velocity and orientation. Calibration of the aircraft system has previously been performed primarily through in-flight experiments, where calibration coefficients were determined by performing various flight maneuvers. However, a rigorous test of the BAT Probe in a wind tunnel has not been previously undertaken. The authors summarize the results of a complement of low-speed wind tunnel tests and in-flight calibrations for the aircraft–BAT Probe combination. Two key factors are addressed in this paper: The first is the correction of systematic error arising from airflow measurements with a noncalibrated BAT Probe. The second is the instrumental precision in measuring the vertical component of wind from the integrated aircraft-based wind measurement system. The wind tunnel calibration allows one to ascertain the extent to which the BAT Probe airflow measurements depart from a commonly used theoretical potential flow model and to correct for systematic errors that would be present if only the potential flow model were used. The precision in the determined vertical winds was estimated by propagating the precision of the BAT Probe data (determined from the wind tunnel study) and the inertial measurement precision (determined from in-flight tests). The precision of the vertical wind measurement for spatial scales larger than approximately 2 m is independent of aircraft flight speed over the range of airspeeds studied, and the 1σ precision is approximately 0.03 m s-1. [ABSTRACT FROM AUTHOR]

The article focuses on the role of sigma-point Kalman filtering for integrated global positioning systems and inertial navigation. Sigma-point use a set of sample points to more accurately map the probability distribution than the linearization of the standard extended Kalman filter, leading to faster convergence from inaccurate initial conditions in position/attitude estimation problems. The filter formulation is based on standard inertial navigation equations.

This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments. [ABSTRACT FROM AUTHOR]