Geomagnetism aided inertial navigation system

Zero velocity update (ZUPT) is an inexpensive and effective method for error control of inertial navigation system (INS). Detecting the stopping time interval is important for ZUPT methods. The ZUPT method based on Kalman filter has a higher degree of precision compared with the ZUPT method based on curve fitting and the ZUPT method based on maximum likelihood estimation, but the modeling of Kalman filter is critical for the performance of INS. In this paper, three kinds of zero-velocity detectors and a linear discrete Kalman filter model are applied in ZUPT for INS. Simulation results show that the three kinds of zero-velocity detectors are effective, and the ZUPT method based on the linear discrete Kalman filter model makes the positioning accuracy of INS significantly improved.

A Fuzzy Kalman Filter Optimized Using a Genetic Algorithm for Accurate Navigation of an Autonomous Underwater Vehicle

An expanded form of the third-order Markov undulation model is developed which includes undulation of the geoid, vertical deflections, gravity anomaly, and the anomalous gravity gradients. First, the complete set of autocorrelation and cross-correlation functions, as specified by the autocorrelation of undulation, is obtained. Then, a time-domain approach is used to synthesize the corresponding linear system driven by white noise that represents the anomalous gravity field behavior along a straight horizontal track. The resulting nine-state Markov model provides a shaping filter for applying covariance analysis and optimal estimation techniques to inertial navigation and gravity gradiometry systems analysis. ONTINUED mechanical refinement has brought inertial navigation technology to the stage where unmodeled variations in the Earth's gravity field have become significant and sometimes dominant error sources.1 At the same time, inertial navigation system analysts have been facing the task of developing error models to evaluate the impact of gravity uncertainties on navigation system performance. Ad- ditionally, the development of gravity gradiometers has made it necessary to model gravity-gradient signals to specify sensor requirements and analyze gradiometer-aided inertial navigation or gradiometry survey performance. To apply the powerful tools of linear system theory and Kalman filtering, it has become common to consider gravity uncertainties random variables, and to model them as Markov processes. Following the first treatment of vertical deflections as a first-order Markov process,2 a succession of higher order Markov models was proposed culminating with the third- order Markov undulation model3'4 which is fully self- consistent; that is, the auto- and cross-correlation functions of undulation, vertical deflections, and gravity anomaly obey and are derived according to the mathematical relationships of physical geodesy. The motivation for using Markov models for gravity un- certainties is that they can be cast in the form of linear dif- ferential equations driven by white noise. This allows use of the model as a shaping filter in covariance analysis of linear dynamic systems, Kalman filters, and optimal smoothing algorithms. The expanded third-order Markov undulation model described in this paper is derived from the original version, but it has three differences. First, it was found that the original model could easily be extended to include all the anomalous gravity gradients by continued differentiation of auto- and cross-correlation functions. Second, the exact cross-correlation function between undulation and gravity anomaly contains modified Bessel functions.4 Modified Bessel functions are solutions to a class of differential equations whose coefficients are functions of the independent variable. To obtain a Markov shaping filter, it is necessary to substitute an approximate function which is the solution to a linear differential equation with constant coefficients. While the chosen exponential form did not approximate the exact Bessel function as well as at first hoped possible, it provides a suitable representation of the un- dulation-gravity anomaly cross-correlation.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Inertial Navigation Systems (INS) have an unbounded type of error characteristics. In order to overcome this problem, aiding systems are used to fuse information obtained via additional sensors with the INS solutions. Terrain Aided Navigation (TAN) is an integrated solution method which exploits Digital Terrain Elevation Maps (DTEM) and on board altimeter data to aid INS in case of GNSS outage. In TAN applications, a position measurement is formed by comparing the terrain profiles, which are obtained by using on board altimeters, with a preloaded DTEM. In general, this position measurement is formed by finding the DTEM points which create minimum-mean-square error with the terrain profile that is measured with on board altimeters. Error on the formed position measurement varies with the terrain profile over which the airborne platform flies. This position measurement is used to keep INS errors bounded with various sensor fusion techniques, two of which are Extended Kalman Filtering (EKF) and Gaussian Sum Filtering (GSF). However, error on the position measurement may cause these techniques to produce very erroneous navigation data or to behave in a divergent manner. Probabilistic Measurement Method (PMM) offered in this paper results with more probable position measurements which have less errors. PMM method is used with an EKF structure and takes advantage of position error covariances that are readily available within the EKF. Joint Probability Density Function (JPDF) of horizontal error components are formed and used while the terrain profile is compared with DTEM in order to form a position measurement. In this paper, performances of EKF with conventional positioning, GSF, and EKF with PMM are investigated and evaluated for 10 different flight paths. Flight paths are chosen to have different terrain profiles and the performance evaluation is realized by comparing the mean horizontal and vertical position errors.

An improved autonomous underwater vehicle federal integrated navigation method based on two-stage position matching technology is proposed in this study to solve the problem of positioning divergence caused by the defects of error accumulation in an inertial navigation system. The improved two-stage position matching method includes the acquisition and preprocessing of the strap-down inertial navigation system and indicates track sequences and elevation measurement sequences. The first-stage position matching is based on iterative closest contour point, while the second-stage position matching is based on an affine algorithm. Simulation results indicate that the proposed algorithm solves the problem of the limitation of the rigid transformation of traditional iterative closest contour point and effectively resolves the matching failure caused by the large initial position error of long-time navigation.

In underwater navigation, the global positioning system is unavailable; hence, other solutions must be pursued in the development of navigation systems. In this paper, filters for the position, linear velocity, and acceleration of underwater vehicles are derived by combining a long baseline acoustic positioning system with an inertial navigation system. A dynamic model is devised via state augmentation, including the bias of the pseudo-ranges, which accounts for the effect of the unknown offset between the emitters’ and receivers’ clocks. With this technique, the resulting dynamics are linear, in spite of the original nonlinear nature of the problem. The proposed solution, which includes the explicit dynamic estimation of the bias of the pseudo-ranges, is a linear Kalman filter. The observability of the system is assessed, which allows to establish globally exponentially stable error dynamics. The performance of the solution is evaluated with realistic simulation results, considering sensor noise and discrete-time measurements. Finally, the comparison with the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the Bayesian Cramer–Rao bound is presented, including Monte Carlo simulations. This comparison shows the goodness of the proposed solution, which converges for all initial conditions and exhibits performance comparable to the EKF and the UKF, whereas the EKF and UKF are shown to lack global convergence.

Aircraft distance-measuring-equipment (DME) data are used to update position, velocity, and wind measurements from inertial navigation systems (INS) measurements. Data from conventional single-channel DME sets, suitably calibrated, are shown to be adequate to resolve the Schuler oscillation and correct INS positions to better than 1-km accuracy. The satellite-based NAVSTAR global position system (GPS) is rapidly superseding other systems for external position reference. However, DME is reliable and very accurate and has been recorded on many research datasets. The principal limitation of the DME is that it is restricted to land-based navigation. The regression technique used does not necessitate multiple DME receivers or station switching and involves few restrictions on the collection of the data. However, the results improve when more than one station is used. Comparisons with other navigation systems (interferometer and loran) demonstrate the method's skill in resolving INS errors. Intercomparisons among several research aircraft flying in close formation support the method's usefulness in correcting biases in INS data.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

The starlight navigation system needs the horizontal attitude information provided by the inertial navigation system as its horizontal reference. When the zero position of the accelerometer changes in the undammed inertial navigation system in the critical condition, it is easy to cause the error oscillation and divergence, which directly affects the accuracy of the horizontal reference information of the auxiliary star sensor. Therefore, under the condition of ship-borne use, consider the combination of strapdown inertial navigation system, starlight navigation system and electronic log. The Kalman filter with velocity damping compensates the dynamic error of inertial navigation. The calibration and readjustment technology of the ship borne starlight/inertial combined system is based on the horizontal velocity damping, and the position error and misalignment angle are completed with the help of accurate reference information of starlight navigation.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

In this study, the integrated navigation system, consisting of radio and inertial navigation system (INS) altimeters, is presented. INS and the radio altimeter have different benefits and drawbacks. The integration is achieved by using an indirect Kalman filter. Hereby, the error models of the navigators are used by the Kalman filter to estimate vertical channel parameters of the navigation system. In the open loop system, INS is the main source of information, and radio altimeter provides discrete aiding data to support the estimations. At the next step of the study, in case of abrupt faults, the performance of the integrated system is examined. The optimal Kalman filter reacts with abnormal estimates to this situation as expected. To recover such a possible malfunctioning, the robust Kalman filter algorithm is suggested.

Because of the costly price and the error accumulation of high precise Inertial Navigation Systems (INS) and the vulnerability of Global Navigation Satellite Systems (GNSS), the geomagnetic navigation technology, a passive autonomous navigation method, is paid attention again. Geomagnetic field is a natural spatial physical field, and is a function of position and time in near earth space. The navigation technology based on geomagnetic field is researched in a wide range of commercial and military applications. This paper presents the main features and the state-of-the-art of Geomagnetic Navigation System (GMNS). Geomagnetic field models and reference maps are described. Obtaining, modeling and updating accurate Anomaly Magnetic Field information is an important step for high precision geomagnetic navigation. In addition, the errors of geomagnetic measurement using strapdown magnetometers are analyzed. The precise geomagnetic data is obtained by means of magnetometer calibration and vehicle magnetic field compensation. According to the measurement data and reference map or model of geomagnetic field, the vehicle's position and attitude can be obtained using matching algorithm or state-estimating method. The tendency of geomagnetic navigation in near future is introduced at the end of this paper.

With the development of the intelligent vehicle navigation systems and vehicle networking systems, vehicle positioning and state estimation become very important. This paper investigates the integration of Global Positioning System (GPS), Inertial Navigation System (INS) and Dead Reckoning based on Anti-lock Braking System (ABSDR) positioning based on Kalman filter, and aims at achieving full-time vehicle positioning. In this paper, we use Kalman Filter to estimate INS positioning error based on GPS or ABSDR solution, take the INS solution compensated with estimated error as system output. This paper introduces INS positioning algorithm, dead reckoning method based on the ABS wheel speed and Kalman filtering method. After that, simulation analysis and experimental verification of the positioning algorithm are carried out. The results show that the system output high-precision high-frequency positioning information when GPS is available, and when GPS is unavailable, INS positioning errors can be compensated well with ABSDR introduced, achieving the full-time positioning of the vehicle.

This paper presents a method to extract heading information from the sky light polarization pattern and successfully implements the polarized light compass (PL-compass) under foliage environment with heavy occlusions for the first time. A classifier based on the primal support vector machine is trained to detect the sky area, and the random sample consensus algorithm is used to choose the inliers among the sky area. The sun vector is built upon the valid measurements. The attitude angles (Roll and Pitch) from the inertial navigation system (INS) are used to establish the Zenith direction when the platform tilts during traveling. The results show that the PL-compass can provide accurate heading angle (RMSE = 1.58°) by seeing only a tiny part of the skylight (minimum of 0.028%) in the dynamic vehicle test. The PL-compass is also integrated with the visual INS based on the indirect extended Kalman filter to improve the navigation accuracy. Each time a new measurement becomes available, the states estimated by the INS are used to check the consistency of the innovation and reject the outliers. The results show that the position errors of the integrated navigation system are greatly reduced with the help of the PL-compass. The estimated trajectory tracks the ground truth very well. The position error is 25.9 m (1.03% of the distance traveled) with heading errors less than 1° in the experiment.