Research on matching area selection based on multiple features fusion of full tensor gravity gradient

The matching area selection is the foundation of gravity gradient aided navigation. In this paper, a gravity gradient matching area selection criterion is proposed, based on the principal component analysis (PCA) and analytic hierarchy process (AHP). Firstly, the features of gravity gradient are extracted and nine gravity gradient characteristic parameters are obtained. Secondly, combining PCA with AHP, a PA model is built and the nine characteristic parameters are fused based on it. At last, the gravity gradient matching area selection criterion is given. By using this criterion, gravity gradient area can be divided into matching area and non-matching area. The simulation results show that gravity gradient position effect in the selected matching area is superior to the matching area, and the matching rate is greater than 90%, the position error is less than a gravity gradient grid.

Matching area selection is the basis of the gravity gradient aided navigation. In this paper, a criterion for gravity gradient matching area selection is proposed based on the gravity gradient tensor matching location, feature extraction and analysis. Matching position experiments on the gravity gradient tensor map are tested by sliding window, and the optimal matching areas on the basis of the gravity gradient tensor maps were found. By means of the gravity gradient feature parameters extraction for the optimal matching areas and analyzing the performance impact of the gravity gradient feature parameters to matching accuracy, a criterion for gravity gradient matching area selection is obtained. Making use of the proposed matching area selection criterion and the average absolute deviation (MAD) matching algorithm, the gravity gradient aided positioning simulation results show that the effect of the matching navigation in the adaptation area is markedly superior to the effect in the non-adaption area, the position error is less than a grid, and matching rate is greater than 90%.

A new method of underwater passive navigation based on gravity gradient is proposed in this paper. In comparison with some other geophysical characteristics such as gravity or gravity anomaly, gravity gradient which is the second derivative of gravitational potential has better spatial resolution and more sensitive to terrain changes. Through it, the digitally stored gravity gradient maps and real-time gravity gradient measurements can be taken as input information, with gravity gradient linearization techniques and extended Kalman filter, the navigation errors of INS are estimated by using gravity gradient error, therefore the output in the inertial navigation system are corrected. Simulation test has been done and the results show that, the method is effective and efficient for the positioning precision improvement.

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most commonly used navigation systems. They both have unique advantages and disadvantages. GNSS is capable of generating precise navigation solutions while enough satellites are in view. However, the GNSS signals are sensitive to the environment. While the signals is attenuated, the GNSS receiver will fail to provide reliable navigation solutions. INS is an advanced navigation system built based on Newton's law. Due to the random noises contained in the Inertial Measurement Unit (IMU), the INS navigation solutions errors diverge over time. Therefore, effective integration of the two common systems can obtain better navigation results than any individual system. In this paper, for enhancing the GNSS/INS tightly integration system with low cost, multiple receivers were employed in the tight integration. Pseudo-range and Pseudo-range rates from the multiple receivers were employed to compose the measurement vector of the integration filter. In order to reduce the computation load, a measurement difference method was proposed. The state vector dimension could be reduced with the measurement difference scheme. Both simulation and field test were carried out for evaluating the performance of the proposed method. Since it was hard to obtain GNSS raw measurements from commercial receivers, self-developed DSP+FPGA (Digital Signal Processor, DSP; Field Programmable Gate Array, FPGA) based GNSS receivers were employed in the field test. The statistical analysis of the results showed that the positioning errors decreased with the receiver's amount increasing. In addition, a measurement difference method was proposed to reduce the state vector dimension for saving the computation load with the identical navigation solutions accuracy.

With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.

Gravity gradiometry can be employed to reduce the gravity modeling error in high precision inertial navigation systems (INSs) in free-inertial navigation. e beneĕt of a gravity gradient instrument is that the measurement is completely passive and not subject to interference from external sources, and gravity formulae or tables will no longer be needed to obtain the INS solution. In line with the inertial navigation paradigm, as long the initial gravity vector is known, the local acceleration of gravity g(x, z) can be estimated autonomously and accurately on the Ęy using onboard accelerometer measurements, provided that the acceleration measurements are very accurate, as is the case in cold atom interferometry-based instruments. e beneĕt of combining measurements from multiple INSs is also addressed, and a free-inertial aircra navigation scenario is presented.

Navigation systems for ships mainly include inertial navigation system (INS), “BeiDou” satellite navigation system, Global Positioning System (GPS), and Doppler navigation system and so on. The characteristics of each system are different. In order to improve the accuracy and fault-tolerant ability of naval-ship navigation system, an integrated navigation algorithm based on Measurement-based adaptive Kalman filtering algorithm (MAKF), improved Suge-Husa algorithm and non-reset federated Kalman filter structure is proposed in this paper. The algorithm uses the short-term high precision of the inertial navigation system and the prior velocity information of the ship to adaptively estimate the measurement noise characteristics of the satellite navigation system. When the satellite navigation system is unable to locate, the INS and the log are combined with the help of the prior velocity information. The simulation results show that the algorithm can effectively exploit the characteristics of each navigation system, and can output high-precision navigation results even when the GPS/BeiDou satellite navigation system fails.

Beidou satellite navigation system (BDS) signal is blocked during dynamic positioning which can lead to poor geometry structure of visible satellites and serious multipath effect. In this contribution, the advantages of inertial navigation system (INS), autonomy and short-term accuracy, are used to speed up the BDS ambiguity resolution (AR). However, the accuracy of general INS cannot meet the fixed BDS original carrier ambiguity requirement. BDS can broadcast three frequency observations, so the virtual observations of long wavelength and short wavelength are formed by linear combination, which make the INS precision meet the fixed ambiguity requirement and achieve high precision positioning, respectively. The short wavelength observations ambiguity due to the relatively large noise is difficult to reliably fixed. To solve this problem, Empirical mode decomposition (EMD) can be used to decompose the error of the short wavelength observations into two parts: systematic error and random error. Systematic error is used to correct the short wavelength ambiguity float solution and reduce the ambiguity search space, to achieve a reliable fixed purpose, finally solving the baseline solution. The experimental results show that the algorithm is more fast and reliable than the traditional algorithms, and more suitable for dynamic positioning in the complex environment.

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.

Based on the geophysics technology, a gravity gradient-terrain matching submarine navigation approach is proposed in this paper. The submarine's current position obtained by matching the measured gravity gradient data to the prepared gravity gradient reference map is used to correct the inertial navigation system's accumulated error. Although the precision gradiometer is in use, there is no world-wide gravity gradient map. The ocean's gravity gradient data is even scarce. Therefore, a gravity gradient matching navigation system directly utilizing the gravity gradient reference map can't be realized. With the sensitivity of gravity gradient to terrain, the gravity gradient reference map can be computed from the local terrain elevation data and the preparing approach of the gravity gradient map is proposed in detail in the paper. Since the seabed terrain elevation map, especially highly accurate offing terrain elevation map has been pre-surveyed, the location can be actualized through matching the real-time measured gravity gradient data to the prepared gravity gradient reference map. Simulations show that the submarine navigation approach on gravity gradient-terrain matching is feasible and can be put into practice.

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.

Asteroid exploration missions demand more autonomous navigation system that are precise and in real-time. Visual-aided inertial navigation systems combine the advantages of autonomy of inertial navigation systems and accuracy of visual navigation systems. However, both increase the computational burden for the information processing. With the goal of decreasing the computational load, this paper proposes a low cost vision-aided inertial navigation method which can calculate increment position, velocity and attitude directly in inertial frame by pre-integrating IMU measurements. For visual information, specific combinations of the features are selected according to information entropy theory to reduce computation. Simulation results show that the standard deviations of the spacecraft’s estimated position error is less than 1 m, the velocity less than 0.002 m/s and the attitude angle less than 0.005 deg, compatible with precision landing on asteroids.

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

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.