Global Positioning System/Inertial Navigation System Research Articles

Gaussian Process for GPS Receiver Predictor and INS GPS Integration

Global Positioning System (GPS) has become important and necessary in daily life. It is possible to reach any destination using GPS, which is included in many lands and marine applications. In this work, GPS was applied to a real navigation boat, integrated with the inertial navigation system (INS) device, and installed on the boat. The navigational devices were linked to the (mission planer) program, through which the results of the navigation process were shown. The system can provide better navigation performance accuracy and reliability due to the integration between GPS and INS. The data extracted from the navigation devices are processed using the Gaussian process (GP) prediction algorithm, to perform the GPS synchronization with the INS and predict the GPS cut-off values for specified periods. The prediction results of the GP algorithm are effective for the cut-off GPS data as the apparent error amount of the algorithm is low. In addition the inertial navigation system provides the location, speed, and position of the boat, but it contains a cumulative error that increases over time. On the other hand, the GPS better accuracy with a lower data rate than the INS, so the integration system between INS/GPS is necessary. It must be developed to overcome the negatives in both systems. Two types of integration were introduced and implemented herein: loosely and tightly. From the results obtained, one can see that the tight system is better at improving errors.

Improving robustness of the MAV yaw angle estimation for low‐cost INS/GPS integration aided with tri‐axial magnetometer calibrated by rotating the ellipsoid model

This study designs a method to improve the robustness of the micro air vehicle (MAV) yaw angle estimation for low-cost inertial navigation system/global positioning system (INS/GPS) integration by using a tri-axial magnetometer. Observability properties of the integration navigation system are first analysed, indicating that all attitude angles have great observability with the aid of the heading measurement by a magnetometer, while the observability of yaw angle is low in traditional INS/GPS. To improve the robustness of yaw angle estimation, the authors calibrate the tri-axial magnetometer according to the rotating ellipsoid model and establish a fault detection and isolation mechanism to adjust the weight of magnetic-measurement adaptively and dynamically in integration navigation. The simulation and experiment results show that the proposed method can calibrate the tri-axial magnetometer properly and screen out the abnormal magnetic measurement for the robustness of yaw angle estimation, suggesting that this technique is a viable candidate for MAV navigation and control applications in complex magnetic fields.

Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an Interacting Multiple Model (IMM) navigation algorithm based on a Robust Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance. This algorithm has a two-level fusion structure. At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method. Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution. At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm. In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance. Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs.

Combination of iterated cubature Kalman filter and neural networks for GPS/INS during GPS outages

To improve the performance of the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation system, current research studies merely combine neural networks with nonlinear filter methods. Few studies focus on how to optimize the parameters of the neural network and how to further improve the small error accumulated into the next filter step due to the imprecise design of the filter when setting the initial parameters in the GPS/INS integrated system. In this article, a dual optimization method consisting of an iterated cubature Kalman filter-Feedforward Neural Network (ICKF-FNN) and a radial basis function-cubature Kalman filter (RBF-CKF) is proposed to compensate the position and velocity errors of the integrated system during GPS outages. The prominent advantages of the proposed method include the following. (i) The ICKF is designed to optimize the parameters of the introduced FNN adaptively and obtain an appropriate internal structure when GPS is available, which improves the accuracy of the training model. (ii) The RBF establishes the relationship between filter parameters and the optimal estimation errors, reducing the errors caused by inaccurate predicted observation during GPS outages. (iii) The proposed dual optimization method takes advantages over other combination algorithms under different moving conditions or even during long period of GPS outages, which shows its great stability. Experimental results show that the root mean squared error of the east position is reduced by 85.79% to 3.2187 m using the proposed strategy during turning movement and the east velocity error accumulation rate decreases by 92.69% during the long straight movement of 250 s. These results are from offline processing.

A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages.

In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment. An extreme learning machine based on a Fourier orthogonal basis function is introduced that considers the deterioration of the accuracy of the navigation system during GPS outages and has a higher positioning accuracy and faster learning speed than the typical neural network learning algorithm. In the end, a simulation and real road test are performed to verify the effectiveness of this algorithm. The results show that the accuracy of the fading filter based on a variable fading factor is clearly improved, and the proposed improved ELM algorithm can provide position corrections during GPS outages more effectively than the other algorithms (ELM and the traditional radial basis function neural network).

Design and testing of a control system associated with the automatic feeding boat for farming Chinese river crabs

In order to address the issues of nonuniform feeding and high labor cost plaguing the process of farming Chinese river crabs, the present study proposes a multifunctional automatic river crabs feeding boat based on Advanced RISC Machine (ARM) and Global Positioning System / Inertial Navigation System (GPS/INS) integrated navigation. This paper proposes a new calculation method based on real time point insertion. This method calculates the current target position of the boat in the real time according to the position of the boat and the turning points of the current route. A new turning and route-switching strategy is also presented in this paper to improve the ship's operational efficiency and prevent the ship from veering off the target route due to its high speed. Considering the boat's unique movement characteristics including non-linearity, large delay and underdamped nature, a route-speed dual-loop control algorithm is designed based on fuzzy Proportion Integration Differentiation (PID) method. Through analyzing the bait distribution associated with the feeding machine, the present study proposes an inner-spiral-based full coverage traversal method and a travel distance optimization model so as to improve the uniformity of the automatic feeding. Results show that the speed overshoot is no more than 5% and the steady-state error can be kept within 3%. Compared with the finite point method, the real time point insertion method decreases the peak route deviation errors by 82.82% and 84.14% while turning and going straight.

Methods for monitoring and controlling multi-rotor micro-UAVs position and orientation based on LabVIEW

Abstract: The multi-rotor micro-UAV has become an important platform for assessing crop information promptly given its high flexibility, compact size, low cost, and high spatial resolution. However, considering the limits of the stability of the micro-UAV control system and the precision of automatic navigation systems, how to timely adjust the position and attitude of UAVs to ensure the target within the scope of monitoring is one of the key techniques which determines whether micro-UAVs can be widely used in precision agriculture as a remote sensing platform. In this study, the integrated navigation system of INS/GPS (Inertial Navigation System/Global Positioning System) and EKF (Extended Kalman Filter) was adopted as the navigation system and fusion algorithm for simulation analysis respectively, to monitor the position and attitude of UAVs more accurately and thus improve the estimation accuracy and control precision. An autonomous flight experiment was designed and carried out, and experimental data collected by commercially available UAVs. LabVIEW was used to analyze and process all experimental data and outputted flight state graphs, which reflected the optimization effect of EKF algorithm and control precision visually. Keywords: Multi-rotor UAV, navigation system, position and attitude estimation, data fusion, LabVIEW DOI: 10.33440/j.ijpaa.20180101.0009 Citation: Pei S Y, Wang S Z, Zhang H H, Zhu H.  Methods for monitoring and controlling multi-rotor micro-UAVs position and orientation based on LabVIEW.  Int J Precis Agric Aviat, 2018; 1(1): 51–58.

INS/GPS Integrated Smoothing Algorithm for Synthetic Aperture Radar Motion Compensation Using an Extended Kalman Filter with a Position Damping Loop

In this study, we propose a real time inertial navigation system/global positioning system (INS/GPS) integrated smoothing algorithm based on an extended Kalman filter (EKF) and a position damping loop (PDL) for synthetic aperture radar (SAR). Integrated navigation algorithms usually induce discontinuities due to error correction update by the Kalman filter, which are as detrimental to the performance of SAR as the relative position error. The proposed smoothing algorithm suppresses these discontinuities and also reduces the relative position error in real time. An EKF estimates the navigation errors and sensor biases, and all the errors except for the position error are corrected directly and instantly. A PDL activated during SAR operation period imposes damping effects on the position error estimates, where the estimated position error is corrected smoothly and gradually, which contributes to the real time smoothing and small relative position errors. The residual errors are re-estimated by the EKF to maintain the estimation performance and the stability of the overall loop. The performance improvements were confirmed by Monte Carlo simulations. The simulation results showed that the discontinuities were reduced by 99.8% and the relative position error by 48% compared with a conventional EKF without a smoothing loop, thereby satisfying the basic performance requirements for SAR operation. The proposed algorithm may be applicable to low cost SAR systems which use a conventional INS/GPS without changing their hardware configurations.

Approach for Detecting Soft Faults in GPS/INS Integrated Navigation based on LS-SVM and AIME

In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

An Enhanced GPS/INS Integrated Navigation System with GPS Observation Expansion

In Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation, the low sampling rate of GPS receivers reduces the observability of state variables. GPS observation expansion is proposed to enhance the GPS/INS integrated navigation system. During the process of observation expansion, the state variables are updated by the same GPS information repeatedly. According to uncertainty theory, the probability density function of GPS observation information is analysed to demonstrate the feasibility of GPS observation expansion. The formula and calculation method of an adaptive filter algorithm are presented to control the uncertainty of GPS observation expansion. Furthermore, an experiment is performed to validate the new algorithm. The results indicate that compared with GPS/INS integrated navigation without observation expansion, the enhanced GPS/INS integrated navigation system can improve the position, velocity and attitude accuracy significantly, especially while a land vehicle is in slow motion. At the same time, the adaptive filter factor is introduced into the new algorithm, which can control the uncertainty caused by the expanded GPS observation.

An Improved Method for Transforming GPS/INS Attitude to National Map Projection Frame

Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems play a very important role in modern photogrammetry and laser scanning by virtue of their capability of direct measurement of high-precision position and attitude data in the WGS 84 datum. In practice, as georeferencing is often conducted in national coordinates, there is a need to transform GPS/INS data to the required national map projection frame first. This letter presents an improved coordinate-transformation-based method for the GPS/INS attitude transformation by taking the datum scale distortion and the length distortion into account. Experimental results show that the transformation errors of our improved method are on the order of magnitude of 1 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> °, which can be safely ignored in aerial photogrammetric processing, whereas the maximum error of the previous coordinate-transformation-based method can be up to several 0.001°.

Application of Street Tracking Algorithm in an INS/GPS Integrated Navigation System

ABSTRACTMost inertial navigation system/global positioning system (INS/GPS) navigation systems for vehicles estimate position, velocity, and heading information from the GPS, and dead reckoning such as INS using a Kalman filter. However, it may suffer from INS noise, GPS noise and, especially, GPS outage. Map-matching is an additional facility to reduce errors caused by GPS outage. Usually, it has to trade-off between the performance and the complexity. In this paper, we have proposed a new map-matching algorithm called street tracking algorithm (STA) to not only correct the position error, but also estimate the velocity and the heading of the navigation system. When the GPS signal is lost, the Kalman filter is put in prediction mode and the STA is enabled. The raw acceleration of inertial measurement unit measured in body frame is also brought directly to STA as an input. The experimental result has shown that when GPS signal is lost, we can control the position error at around 2.5 m, and the velocity error at around 3.2 m/s. The proposed map-matching algorithm STA can be applied in various kinds of environment due to its simplicity and flexibility.

In-motion Alignment of a Low-cost GPS/INS under Large Heading Error

This paper presents a new type of Global Positioning System/Inertial Navigation System (GPS/INS) providing higher navigation accuracy under large initial heading error. The mechanization introduced is applicable to low cost GPS/INS systems and enhances the performance when the heading error is large. The proposed approach has the capability to decrease large heading errors very quickly and can start the strapdown navigation computations under poor heading accuracy without any special alignment procedure. Although the design is applicable to land, sea and aerial vehicles, a land vehicle is used for the performance tests. The test is conducted around a closed path and the proposed system is compared to a GPS/INS system based on small attitude error assumption. The performance of both systems is given in this paper.

Decoupled Observability Analyses of Error States in INS/GPS Integration

The observability of error states in Inertial Navigation System/Global Positioning System (INS/GPS) integration is of great importance. Rank tests or null space tests of the observability matrix have been adopted by previous works, however, for a time-varying system with a high-dimension error state vector, it is very difficult to analyse the observability matrix by these traditional methods. In this paper, the decoupled observability analysis method is proposed for an 18-dimensional INS/GPS integration system. By reducing the dimension of coupling error states, several six-dimensional decoupled observability sub-matrices are obtained, which make the observability analyses easier. The observability results of error states are obtained by the proposed method. Covariance simulation with an Extended Kalman filter (EKF) and a flying test were performed which confirmed the theoretical results.

On the Observability of Loosely Coupled Global Positioning System/Inertial Navigation System Integrations With Five Degree of Freedom and Four Degree of Freedom Inertial Measurement Units

This article examines the observability of a modified loosely coupled global positioning system/inertial navigation system (GPS/INS) filter and analyzes the sideslip and attitude estimation capability of the filter. The modified filter is a loosely coupled integration which does not include a pitch rate gyro and which uses GPS course information as a measurement of heading when the vehicle is driving straight. Experimental tests are conducted which show that the modified filter has the same observability characteristics as a standard loosely coupled filter during turning events. The observability of a loosely coupled integration using only a four degree of freedom (DOF) inertial measurement unit (IMU) is also discussed and examined by experiment, as well as the sideslip and roll angle estimation performance. Finally, the error characteristics of the modified loosely coupled integration with the five DOF IMU when the filter is unobservable are studied. Monte Carlo simulations of long periods of straight driving with various sensor qualities are presented to show the worst case attitude errors when the filter is unobservable.

Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments

This paper presents a hierarchical simultaneous localization and mapping (SLAM) system for a small unmanned aerial vehicle (UAV) using the output of an inertial measurement unit (IMU) and the bearing-only observations from an onboard monocular camera. A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match. This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter (EKF) for attitude and velocity estimation. Then, another EKF is employed to estimate the position of the vehicle and the locations of the features in the map. Both simulations and experiments are carried out to test the performance of the proposed system. The result of the comparison with the referential global positioning system/inertial navigation system (GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.

Frequent Unscheduled Updates of the National Base Map Using the Land-Based Mobile Mapping System

This paper focuses on the use of the Land-based Mobile Mapping System (LMMS) for the unscheduled updates of a National Base Map, which has nationwide coverage and was made using aerial photogrammetry. The objectives of this research are to improve the weak points of LMMS surveying for its application to the updates of a National Base Map (NBM), which has rigorous accuracy and quality standards. For this, methods were suggested for the (1) improvement of the accuracy of the Global Positioning System/Inertial Navigation System (GPS/INS) in the long-term exposure of environments with poor GPS reception; (2) elimination of mutual deviations between LMMS data obtained in duplicate to meet resolution standards; (3) devising an effective way of mapping objects using LMMS data; and (4) analysis of updatable regions and map layers via LMMS. To verify the suggested methods, experiments and analyses were conducted using two LMMS devices in four target areas for unscheduled updates of the National Base Map.

Flight-Test Evaluation of Sensor Fusion Algorithms for Attitude Estimation

In this paper, several Global Positioning System/inertial navigation system (GPS/INS) algorithms are presented using both extended Kalman filter (EKF) and unscented Kalman filter (UKF), and evaluated with respect to performance and complexity. The contributions of this study are that attitude estimates are compared with independent measurements provided by a mechanical vertical gyroscope using 23 diverse sets of flight data, and that a fundamental difference between EKF and UKF with respect to linearization is evaluated.

Fault Detection Method Based on Varying-Length Scanning Model and Its Application to the Integrated GPS/INS Navigation System

A fault detection method based on varying-length scanning model is proposed.This method performs minus-operation on the time series data to be detected and then gets the corresponding difference minus sequence.The statistic characters of two neighboring difference minus sequences are affected due to the change point,so the proposed method estimates the value interval at next time through current difference minus sequence,and then realizes fault detection by estimating whether the change point exists at next time.Considering the time complexity of the algorithm,a scanning window size mechanism is designed to bring a balance between the detection speed and precision.The question to select the confidence interval and scanning window size is discussed by a simulation experiment.This fault detection method is applied to integrated GPS/INS(global positioning system / inertial navigation system) navigation system.The experiment shows that it can detect the change point in the data effectively and enhance the precision of filter.