A Gyroscope Bias Estimation Algorithm Based on Map Specific Information

The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.

Due to the high demand of location-based services in buildings, various indoor positioning methods have been proposed. Among them, the Pedestrian Dead Reckoning (PDR) systems have received much attention because of the widespread deployment of smartphones and no requirement of infrastructure. In this paper, we propose a PDR system to track a pedestrian holding a smartphone while he/she walks in a building. To eliminate the inevitable accumulated error over time, we suggest a method of using location related information from environments. We named it ‘landmark’, which is defined as a specific area (or point) where the pedestrian passes by in a building such as a stair, a door, a corner or a crossroad. If the system detects a landmark during the walking period, then it will correct the position of the user with the pre-defined position information of the landmark. In this work, we propose one kind of landmark called the door landmark. We have developed a method to detect the landmarks only using the sensors embedded in smartphones. The door landmark can be detected by combining the features from the magnetic sensor and user’s walking behavior. Via a set of experiments, we have evaluated the performances of the landmark detection and the position tracking of the system.

A pedestrian dead-reckoning (PDR) system based on micro-inertial technology usually uses a sensor integrated with a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and is attached to the person's body (usually on the foot) to acquire the trajectory of the pedestrian. Although a variety of PDR system solutions have been proposed, the PDR solution for marking time and walking hybrid motions has not been formally proposed and solved. Therefore, in this paper, we propose the PDR system using a step-and-heading system scheme for marking time and walking hybrid motion. The main contributions include: 1) an adaptive gait partitioning method for walking and marking time hybrid motions is proposed; 2) a motion classification algorithm that uses a multi-layer perceptron to classify whether each step is walking or marking time is proposed; and 3) a new step length estimator based on several currently proposed models is proposed. Finally, an extended Kalman filtering algorithm combining heuristic heading reduction, flat-ground hypothesis, and cardinal heading aided inertial navigation techniques is used for heading estimation. We evaluate the effectiveness of the proposed algorithm through experiments, and the experimental results show that the average position error of the two groups of test experiments based on marking time and walking hybrid motion is 0.42% and 0.6%, respectively.

Indoor localization has become a popular topic in recent years. While self-contained pedestrian dead reckoning (PDR) systems can be conveniently implemented on a smartphone with built-in inertial sensors for indoor localization, the error of the estimated position for a PDR system can accumulate quickly and results in an unacceptable position accuracy. To address this issue, we propose the collaborative pedestrian dead reckoning (CPDR) system. The main idea of the CPDR system is when users are near to each other, we can leverage the proximity information to improve their estimated positions by means of the opportunistic Kalman filter. In addition, the backward correction scheme is used to improve the accuracy of user's trajectory. To evaluate the CPDR system, a prototype is implemented on Apple's iPhone 5. The experiment results show that the CPDR system achieves a better position accuracy than the raw PDR system.

To validate the self-calibration performance of a missile system's inertial navigation system (INS), an inertial error estimator (IEE) and a calibration procedure were developed and tested. The IEE was developed using a least squares fit of navigation-in-place data to calculate the accelerometer biases, the gyroscope biases, and the axis misalignments. It was found that the estimation error due to highly correlated regression functions decreases with navigation time. A tradeoff study was performed to select the optimum navigation-in-place time allowed for determining the estimation of the inertial errors. Once the optimum navigation time was determined, an INS calibration procedure incorporating the IEE was developed. The calibration procedure was validated by applying corrections for the inertial errors to the INS, and then navigating in place to show a reduced position error profile. Test results showing the outcome of the INS calibration procedure are included. >

A 5 inch diameter high accuracy borehole surveying instrument has been developed using a full 3 axis inertial navigation system (INS). It has the following features: 3 Ring Laser GyrosNo mechanical gimbalsReal-time surface readoutObtains position data directly without the need for intermediate depths stops.Requires no stopping for drift checksUses wireline velocities up to 600 ft/minReduces INS's typical drift error to less than 6 ft/hr. This tool achieves a survey accuracy that is close to that of the 10" INS tool, while at the same time offering a smaller diameter and continuous survey capability.

This paper develops an improved hybrid calibration scheme for the strapdown inertial navigation system (SINS) that combines the advantages of an optimal rotational norm calibration method and an improved system-level calibration method. To accurately determine the scale factors and misalignment error of gyros triad, the optimal rotation norm calibration method is applied. Similarly, the improved system-level calibration method based on the 24-dimensional error state Kalman filter (ESKF) plays an important role in accelerometer calibration and gyro biases calibration. The clockwise and counterclockwise multi-cycles rotation scheme is designed to eliminate the effects of the Earth's rate and gyros biases, and then provide accurate rotational norm to completely determine the gyros triad scale factors and misalignment. In addition, the improved ESKF system-level calibration method is employed to estimate accelerometer parameters and gyros biases. Simulation test verify the validity of the proposed method, and the calibration experiences and the verification tests of the two high-precision optical gyros inertial measurement units (IMU) are carried out. For comparison, the traditional 30-dimensional system-level calibration is also performed. The attitude error of high maneuvering swing test indicate that the gyros calibration accuracy of hybrid calibration scheme is better than that of traditional system-level calibration scheme. Furthermore, the positioning error of pure inertial navigation solution in static and dynamic condition for two calibration methods indicate that the hybrid calibration scheme significantly improves the positioning accuracy, especially in the dynamic experiment, that is, the proposed scheme provides a more accurate calibration of the IMU.

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.

In strapdown inertial navigation system and global navigation satellite system-based airborne gravimetry, there is a circular problem between the navigation solution and the gravity vector estimation. On one hand, the gravity vector estimation depends on the navigation solution. On the other hand, the navigation solution requires a predetermined gravity model. The normal gravity, which differs from the actual gravity by an amount known as the gravity disturbance, is commonly used for the navigation solution, and this will result in errors of gravimetry. We reviewed this problem and found that an iterative method can be effective if there were no gyroscope biases in the inertial navigation system measurements. Iteratively, the differences between the estimated gravity vector and actual gravity vector can converge to constant values in this specific condition. If we know the gravity values at the endpoints of each survey line, these constant values can be compensated for by matching these endpoints. After such compensations, the repeatability of the estimated horizontal gravity components can be improved significantly. An application to real airborne data was performed to test the validity of the new method. The results yielded an internal accuracy in the horizontal component of approximately 3 mGal with a spatial resolution of 4.8 km.

In this paper, we proposed Advanced Heuristic Drift Elimination (AHDE) which can remove azimuth drift error in indoor environments. In Pedestrian Dead Reckoning (PDR) system, azimuth error is one of the main factors that cause estimated position error. In order to reduce azimuth error, several methods are used. Heuristic Drift Elimination (HDE) algorithm proposed by Johann Borenstein shows great strength in indoor environments. HDE assumes that generally walls and corridors are straight and either parallel or orthogonal to each other in man-made building. They called the typical directions of walls and corridors as the dominant directions. HDE is corrected if the computed azimuth angle matches the closest dominant direction. HDE also has limitation when the pedestrian walks in various directions because HDE can cause a new azimuth error by matching the closed dominant direction. To overcome these limitations, we propose AHDE which is based on INS-EKF-ZUPT (IEZ) by using foot-mounted IMU. The algorithm consists with the following two steps. First, it determines whether a pedestrian is walking straight forward or not. If a pedestrian is not walking straight forward, the algorithm estimates the biases of accelerometers and gyroscopes by Zero velocity UPdaTe (ZUPT) method. However if the pedestrian is walking straight forward, the algorithm determines whether the pedestrian is walking along the dominant direction or not. When it is determined that pedestrian is walking along the dominant direction, the algorithm corrects the computed azimuth angle to the closest dominant direction. When it is determined that the pedestrian is not walking along the dominant direction but walking straight with no change in azimuth, AHDE applies a correction to the gyro output which contains the bias error. Experimental results show that the accuracy of AHDE is improved compared to HDE and the algorithm is a powerful method which can reduce the azimuth error in complex motion.

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.

ABSTRACT: This paper compares the use of an inertial navigation system (INS) and a multiple GPS antenna system for attitude determination of an off-road vehicle. The INS system currently provides a less expensive attitude solution than the multiple GPS antenna system. Additionally, the INS system does not suffer from GPS errors resulting from multipath and antenna blockages. A Kalman filter incorporates the INS measurements with centimeter-level carrier-phase differential GPS (DGPS) measurements from one position antenna for complete position and attitude estimation of an off-road vehicle. The centimeter-level accuracy of DGPS allows for precise calibrations of the INS system for accurate attitude estimation. The paper shows that a low-cost INS system is capable of providing the attitude accuracy necessary for centimeter-level control of an off-road vehicle. Results of using the INS system in conjunction with DGPS position measurements to autonomously control a farm tractor are presented.

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

GNSS observation outage occurs inevitably in various situations, e.g., automobile driving in urban environments. Although the ambiguity resolution (AR) significantly shortens the initialization time of precise point positioning (PPP), frequent re-initialization of PPP AR may be needed due to outages. The inertial navigation system (INS) can be used for acquiring highly accurate position information during GNSS outages. In the previous studies, the GPS PPP/INS with the ambiguity resolution model is proposed, and the experimental results confirm that the INS could facilitate the re-fixing of the interrupted GNSS ambiguity. Multiple GNSS can also be used to shorten the PPP convergence time. We compare the contribution of multi-GNSS and the INS to the ambiguity re-fixing during GNSS outage. Results show that the multi-GNSS, e.g., Galileo, GLONASS and BDS, without ambiguity resolution has little contribution to the re-fixing of GPS ambiguity in PPP/INS, while INS can provide highly accurate positioning results during the GNSS outages. The higher the accuracy of the predicted INS positioning, the lower the contribution of multi-GNSS to the re-fixing of the ambiguity in the PPP/INS. The finding is verified by a theoretical analysis, carborne experiment and shipborne experiment. Experiment results show that the re-fixing time is 43 s for GPS PPP and MEMS-grade INS coupled system in GNSS outage of 1 s. The re-fixing time of GPS is slightly changed when adding the multi-GNSS, e.g., Galileo, GLONASS and BDS, without the ambiguity resolution. However, by performing ambiguity resolution to the added GNSS system, the re-fixing time of PPP/INS is significantly reduced from 43 to 4 s. The reason is that, although the multi-GNSS provides little improvement on the accuracy of the ambiguity, it enlarges the number of the ambiguity parameter. The possibility of ambiguity resolution is increased due to the large candidate set of the ambiguity, which reduces re-fixing time.

In modern navigation applications, Inertial Navigation System (INS) is attractive for integrating with Global Positioning System (GPS). Although traditional GPS/INS integrated systems can bridge the GPS gaps, the navigation capability is strongly dependent on the performances of standalone INS. As an important complementary, a new terrestrial, Radio-Frequency (RF) based, distance measurement technology “Locata” can provide continuously time-synchronised ranging signals even in many GPS challenged environments e.g. poor satellite geometry, signal blockage in suburban, tunnels, high rise buildings canyon. This paper investigates the integration of Locata, GPS, and INS with a focus on the loosely-coupled triple integration algorithm. Firstly, the Conventional Kalman Filtering (CKF) based triple integration of Locata/GPS/INS architecture is described and briefly discussed. Secondly, to overcome the pitfalls of conventional Locata/GPS/INS integration algorithm, an Adaptive Dual Kalman Filtering (ADKF) algorithm is proposed and developed in three stages: (1) To enhance the reliability of position and velocity (PV) quantities generated from Locata/GPS integrated sensors, the 1st KF is additionally constructed to reliably estimate the PV solution before fusing INS sensor. (2) Combining the 15-state INS error model and the measurements which are the differences between PV solution from the 1st KF and INS sensor, the 2nd KF is subsequently employed to correct the INS navigation errors. (3) The final integration solution is reversely used as the feedback for precisely estimating the stochastic model (i.e. variance of dynamic and observation model noise) of the 1st KF. Finally, the real flight experiment is carried out to demonstrate the efficiency and validity of Locata/GPS/INS integration algorithms. The results show that: (1) Conventional GPS/INS integration performs well but its accuracy dramatically decreases when GPS signals are unavailable for a short period. (2) Augmented by Locata, GPS/INS produces tolerable results in whole experiment even without aiding of GPS. (3) By additionally operation of the 1st KF and adaptively estimating its stochastic model with feedback of integration solution, ADKF achieves more accurate and reliable position, velocity and attitude (PVA) solution than CKF.KeywordsINS errorLocata/GPS/INS integrationAdaptive Kalman filteringStochastic model