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Showing papers on "GPS/INS published in 2013"


Journal ArticleDOI
TL;DR: An optimization-based coarse alignment approach that uses GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae is proposed, and can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment.
Abstract: The in-flight alignment is a critical stage for airborne inertial navigation system/Global Positioning System (INS/GPS) applications. The alignment task is usually carried out by the Kalman filtering technique that necessitates a good initial attitude to obtain a satisfying performance. Due to the airborne dynamics, the in-flight alignment is much more difficult than the alignment on the ground. An optimization-based coarse alignment approach that uses GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae is proposed. Simulation and flight test results show that, with the GPS lever arm well handled, it is potentially able to yield the initial heading up to 1 deg accuracy in 10 s. It can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment. The approach can also be applied to other applications that require aligning the INS on the run.

191 citations


Journal ArticleDOI
24 Jul 2013-Sensors
TL;DR: A method is introduced that compensates for error terms of low-cost INS (MEMS grade) sensors by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques.
Abstract: Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

152 citations


Journal ArticleDOI
TL;DR: Comparison results indicate that the proposed model combined with STKF/WNN algorithms can effectively provide high accurate corrections to the standalone INS during GPS outages.

116 citations


Journal ArticleDOI
TL;DR: Random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability and illustrates a significant reduction in the positional error.
Abstract: This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise. In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24-56%.

111 citations


Journal ArticleDOI
15 Aug 2013-Sensors
TL;DR: The proposed method is described and applied in a real-time integrated system with two integration strategies, namely, loosely coupled and tightly coupled schemes, respectively, and aims to avoid the multipath problem.
Abstract: The integration of an Inertial Navigation System (INS) and the Global Positioning System (GPS) is common in mobile mapping and navigation applications to seamlessly determine the position, velocity, and orientation of the mobile platform. In most INS/GPS integrated architectures, the GPS is considered to be an accurate reference with which to correct for the systematic errors of the inertial sensors, which are composed of biases, scale factors and drift. However, the GPS receiver may produce abnormal pseudo-range errors mainly caused by ionospheric delay, tropospheric delay and the multipath effect. These errors degrade the overall position accuracy of an integrated system that uses conventional INS/GPS integration strategies such as loosely coupled (LC) and tightly coupled (TC) schemes. Conventional tightly coupled INS/GPS integration schemes apply the Klobuchar model and the Hopfield model to reduce pseudo-range delays caused by ionospheric delay and tropospheric delay, respectively, but do not address the multipath problem. However, the multipath effect (from reflected GPS signals) affects the position error far more significantly in a consumer-grade GPS receiver than in an expensive, geodetic-grade GPS receiver. To avoid this problem, a new integrated INS/GPS architecture is proposed. The proposed method is described and applied in a real-time integrated system with two integration strategies, namely, loosely coupled and tightly coupled schemes, respectively. To verify the effectiveness of the proposed method, field tests with various scenarios are conducted and the results are compared with a reliable reference system.

93 citations


Journal ArticleDOI
TL;DR: This paper focuses on the integration of inertial measurement unit (IMU) with two real-time kinematic global positioning system (GPS) units in an adaptive Kalman filter (KF) for driftless estimation of a vehicle's attitude and position in 3-D.
Abstract: This paper focuses on the integration of inertial measurement unit (IMU) with two real-time kinematic global positioning system (GPS) units in an adaptive Kalman filter (KF) for driftless estimation of a vehicle's attitude and position in 3-D. The observability analysis reveals that 1) integration of a single GPS with IMU does not constitute an observable system; and 2) integration of two GPS units with IMU results in a locally observable system provided that the line connecting two GPS antennas is not parallel with the vector of the measured acceleration, i.e., the sum of inertial and gravitational accelerations. The latter case makes it possible to compensate the error in the estimated orientation due to gyro drift and its bias without needing additional instrument for absolute orientation measurements, e.g., magnetic compass. Moreover, in order to cope with the fact that GPS systems sometimes lose their signal and receive inaccurate position data, the self-tuning filter estimates the covariance matrix associated with the GPS measurement noise. This allows the KF to incorporate GPS measurements in the data fusion process heavily only when the information received by GPS becomes reliably available. Finally, test results obtained from a mobile robot moving across uneven terrain demonstrate driftless 3-D pose estimation.

92 citations


Journal ArticleDOI
TL;DR: A proposed automated baseline correction algorithm using only accelerometer data is analyzed and it is found that the error incurred from automated baseline corrections that rely on seismic data alone is complex and can be large in both the time and frequency domains of interest in seismological and engineering applications.
Abstract: [1] Computation of displacements from strong motion inertial sensors is to date an open problem. Two distinct methodologies have been proposed to solve it. One involves baseline corrections determined from the inertial data themselves and the other a combination with other geophysical sensors such as GPS. Here we analyze a proposed automated baseline correction algorithm using only accelerometer data and compare it to the results from the real-time combination of strong motion and GPS data. The analysis is performed on 48 collocated GPS and accelerometers in Japan that recorded the 2011 Mw 9.0 Tohoku-oki earthquake. We study the time and frequency domain behavior of both methodologies. We find that the error incurred from automated baseline corrections that rely on seismic data alone is complex and can be large in both the time and frequency domains of interest in seismological and engineering applications. The GPS/accelerometer combination has no such problems and can adequately recover broadband strong motion displacements for this event. The problems and ambiguities with baseline corrections and the success of the GPS/accelerometer combination lead us to advocate for instrument collocations as opposed to automated baseline correction algorithms for accelerometers.

89 citations


Journal ArticleDOI
TL;DR: The experimental results validate the observability analysis of the low-cost INS/GPS system and show that the proposed attitude estimation method can effectively improve the accuracy of the vehicle attitude estimation, suggesting that this technique is a viable candidate for many control applications used in cars.
Abstract: This paper presents a method using the Global Positioning System (GPS)-measured course angle to improve the accuracy of the vehicle attitude estimation for low-cost inertial navigation system/GPS (INS/GPS) integration. Observability properties of the error states in the low-cost integration navigation system are first analyzed, indicating that the attitude estimation is severely affected by vehicle maneuvers, particularly the yaw angle. The pitch and roll angles are strongly observed; hence, the observability of these two angles is nearly free of influence caused by vehicle maneuvers, and these two angles can be accurately estimated. To improve the yaw-angle estimation, we propose a cascaded Kalman filter to deal with the yaw angle separately with the aid of the GPS-measured course angle. Additionally, two switching rules are established to remove the influence caused by the sideslip angle and GPS noise. The experimental results validate the observability analysis of the low-cost INS/GPS system and show that the proposed attitude estimation method can effectively improve the accuracy of the vehicle attitude estimation, suggesting that this technique is a viable candidate for many control applications used in cars.

88 citations


Journal ArticleDOI
TL;DR: The proposed FASTUKF algorithm can be considered as an alternative approach for designing the ultra tightly coupled GPS/INS integrated navigation system.
Abstract: This paper conducts performance evaluation for the ultra-tight integration of Global positioning system (GPS) and inertial navigation system (INS) by use of the fuzzy adaptive strong tracking unscented Kalman filter (FASTUKF). An ultra-tight GPS/INS integration architecture involves fusion of the in-phase and quadrature components from the correlator of the GPS receiver with the INS data. These two components are highly nonlinearly related to the navigation states. The strong tracking unscented Kalman filter (STUKF) is based on the combination of an unscented Kalman filter (UKF) and strong tracking algorithm (STA) to perform the parameter adaptation task for various dynamic characteristics. The STA is basically a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in a traditional approach for selecting the softening factor through personal experience or computer simulation, the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor, leading to the FASTUKF. Two examples are provided for illustrating the effectiveness of the design and demonstrating effective improvement in navigation estimation accuracy and, therefore, the proposed FASTUKF algorithm can be considered as an alternative approach for designing the ultra tightly coupled GPS/INS integrated navigation system.

70 citations


Proceedings ArticleDOI
19 Aug 2013
TL;DR: It was shown that this new method of attitude estimation is effective in distinguishing the yaw and heading angles of the aircraft, properly regulating the attitude estimates with air data system measurements, and provding a reasonable estimate of the local wind field.
Abstract: A new attitude, heading, and wind estimation algorithm is proposed, which incorporates measurements from an air data system to properly relate predicted attitude information with aircraft velocity information. Experimental Unmanned Aerial Vehicle (UAV) flight data was used to validate the proposed approach. The experimental results demonstrated effective estimation of the roll, pitch, yaw, and heading angles, and provided a smoothed estimate of the angle of attack and sideslip angles. The wind estimation results were validated with respect to measurments provided by a local weather station. It was shown that this new method of attitude estimation is effective in distinguishing the yaw and heading angles of the aircraft, properly regulating the attitude estimates with air data system measurements, and provding a reasonable estimate of the local wind field.

64 citations


Journal ArticleDOI
08 Nov 2013-Sensors
TL;DR: Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors, and could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain.
Abstract: Low-cost GPS receivers provide geodetic positioning information using the NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a quantization grid of some decimeters in size, the dimensions of which vary depending on the point of the terrestrial surface. The aim of this study is to reduce the quantization errors of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model equations were employed to particularize the filter, which was tuned by applying Monte Carlo techniques to eighteen straight trajectories, to select the covariance matrices that produced the lowest Root Mean Square Error in these trajectories. Filter performance was tested by using straight tractor paths, which were either simulated or real trajectories acquired by a GPS receiver. The results show that the filter can reduce the quantization error in distance by around 43%. Moreover, it reduces the standard deviation of the heading by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors. It could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain.

Proceedings ArticleDOI
30 Sep 2013
TL;DR: SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS to improve the localization accuracy when the GPS signal is weak.
Abstract: We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.

Journal ArticleDOI
TL;DR: A low-cost method to determine the vehicle attitude for vehicle-mounted satellite-communication (satcom)-on-the-move (SOTM) using a micro inertial measurement unit (MIMU) and a two-antenna global positioning system (GPS).
Abstract: In this paper, we develop a low-cost method to determine the vehicle attitude for vehicle-mounted satellite-communication (satcom)-on-the-move (SOTM) using a micro inertial measurement unit (MIMU) and a two-antenna global positioning system (GPS). An adaptive Euler-angle-based unscented Kalman filter (UKF) is utilized to fuse these sensors to guard against the effects induced by GPS outages and vehicle accelerations. When the two-antenna GPS can provide the vehicle yaw angle, the vehicle accelerations that introduce large errors to the accelerometer-measured gravitational acceleration can be corrected by the GPS-measured velocity and sideslip angle. When the two-antenna GPS fails to provide the yaw angle and needed information, the yaw angle is estimated only by integrating gyroscopes. The estimation of the pitch and roll angles is adaptively controlled by the vehicle acceleration detection rules. These rules make full use of the gyroscope output and the filtering results, which are more compatible with vehicle use than conventional accelerometer norm-based rule. The proposed method is verified with driving tests, suggesting that this technique is a viable candidate for low-cost SOTM.

Journal ArticleDOI
28 Oct 2013-Sensors
TL;DR: A novel parameter calibration method is proposed and an iterative implementation of the method is designed to reduce the error caused by INS initial alignment and a simplified INS/DVL integration scheme is employed.
Abstract: Since the drifts of Inertial Navigation System (INS) solutions are inevitable and also grow over time, a Doppler Velocity Log (DVL) is used to aid the INS to restrain its error growth. Therefore, INS/DVL integration is a common approach for Autonomous Underwater Vehicle (AUV) navigation. The parameters including the scale factor of DVL and misalignments between INS and DVL are key factors which limit the accuracy of the INS/DVL integration. In this paper, a novel parameter calibration method is proposed. An iterative implementation of the method is designed to reduce the error caused by INS initial alignment. Furthermore, a simplified INS/DVL integration scheme is employed. The proposed method is evaluated with both river trial and sea trial data sets. Using 0.03°/h(1σ) ring laser gyroscopes, 5 × 10−5 g(1σ) quartz accelerometers and DVL with accuracy 0.5% V ± 0.5 cm/s, INS/DVL integrated navigation can reach an accuracy of about 1‰ of distance travelled (CEP) in a river trial and 2‰ of distance travelled (CEP) in a sea trial.

Journal ArticleDOI
23 Aug 2013-Sensors
TL;DR: By applying data fusion and map matching, the study easily accomplished mapping of a GPS/INS trajectory onto the road network and indicates the potential of low-cost MEMS IMUs in navigation applications.
Abstract: This paper presents an evaluation of the map-matching scheme of an integrated GPS/INS system in urban areas. Data fusion using a Kalman filter and map matching are effective approaches to improve the performance of navigation system applications based on GPS/MEMS IMUs. The study considers the curve-to-curve matching algorithm after Kalman filtering to correct mismatch and eliminate redundancy. By applying data fusion and map matching, the study easily accomplished mapping of a GPS/INS trajectory onto the road network. The results demonstrate the effectiveness of the algorithms in controlling the INS drift error and indicate the potential of low-cost MEMS IMUs in navigation applications.

Journal ArticleDOI
TL;DR: The hierarchically distributed architecture based on information filtering is developed to estimate the relative attitude, position and velocity between the leader and follower in formation to show that INS/VisNav/GPS relative navigation system has higher accuracy.

Journal ArticleDOI
25 Jun 2013-Sensors
TL;DR: A robust solution to solve the problem of rapidness and accuracy of the proposed self-alignment method and the good de-noising performance of the novel prefilter named hidden Markov model based Kalman filter (HMM-KF).
Abstract: Strapdown inertial navigation systems (INS) need an alignment process to determine the initial attitude matrix between the body frame and the navigation frame. The conventional alignment process is to compute the initial attitude matrix using the gravity and Earth rotational rate measurements. However, under mooring conditions, the inertial measurement unit (IMU) employed in a ship's strapdown INS often suffers from both the intrinsic sensor noise components and the external disturbance components caused by the motions of the sea waves and wind waves, so a rapid and precise alignment of a ship's strapdown INS without any auxiliary information is hard to achieve. A robust solution is given in this paper to solve this problem. The inertial frame based alignment method is utilized to adapt the mooring condition, most of the periodical low-frequency external disturbance components could be removed by the mathematical integration and averaging characteristic of this method. A novel prefilter named hidden Markov model based Kalman filter (HMM-KF) is proposed to remove the relatively high-frequency error components. Different from the digital filters, the HMM-KF barely cause time-delay problem. The turntable, mooring and sea experiments favorably validate the rapidness and accuracy of the proposed self-alignment method and the good de-noising performance of HMM-KF.

Journal ArticleDOI
TL;DR: Optical angle encoder calibration methods using accelerometers are proposed, on the basis of navigation error and accuracy requirement analyses for a single-axis RINS, and the test results show that the accuracy of calibration methods proposed is higher than 4 arcsec (1σ).
Abstract: By rotating a strapdown inertial navigation system (INS) over one or more axes, a number of error sources originating from the employed sensors cancel out during the integration process. Rotary angle accuracy has an effect on the performance of rotational INS (RINS). The application of existing calibration methods based on gyroscope measurements is restricted by the structure of the inertial measurement unit (IMU) and scale factor stability of the gyroscope. The multireadhead method has problems in miniaturization and cost. Hence, optical angle encoder calibration methods using accelerometers are proposed, on the basis of navigation error and accuracy requirement analyses for a single-axis RINS. The test results show that the accuracy of calibration methods proposed is higher than 4 arcsec (1σ).

Proceedings ArticleDOI
02 May 2013
TL;DR: In this paper, the position of an AUV was estimated by fusion of the data of two sensors: Doppler velocity log (DVL) and inertial navigation system (INS).
Abstract: In this paper, the position of an autonomous underwater vehicle (AUV) has been estimated by fusion of the data of two sensors: Doppler velocity log (DVL) and inertial navigation system (INS). Two different filters have been used in order to estimate the position of AUV, namely, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The approach of EKF is based on linearization of system state space equation around the instantaneous values of the state variables. The UKF is based on the nonlinear transformation of selected points of a Gaussian distribution of the state variables. These two filters are implemented, using nonlinear kinetic model of a sample AUV, and the results of the position estimation of the two filters are compared. The results show that despite the linearization approximations, the EKF results are closer to the real path of the vehicle than the UKF estimates.

Proceedings Article
09 Jul 2013
TL;DR: A loosely coupled indirect feedback Kalman filter integration for visual odometry and inertial navigation system that is based on error propagation model and takes into account different characteristics of individual sensors for optimum performance, reliability and robustness is proposed.
Abstract: Visual Odometry (VO) is the process of estimating the motion of a system using single or stereo cameras. Performance of VO is comparable to that of wheel odometers and GPS under certain conditions; therefore it is an accepted choice for integration with inertial navigation systems especially in GPS denied environments. In general, VO is integrated with the inertial sensors in a state estimation framework. Despite the various instances of estimation filters, the underlying concepts remain the same, an assumed kinematic model of the system is combined with measurements of the states of that system. The drawback of using kinematic models for state transition is that the state estimate will only be as good as the precision of the model used in the filter. A common approach in navigation community is to use an error propagation model of the navigation solution using inertial sensor instead of an assumed dynamical model. High rate IMU will trace the dynamic better than an assumed model. In this paper, we propose a loosely coupled indirect feedback Kalman filter integration for visual odometry and inertial navigation system that is based on error propagation model and takes into account different characteristics of individual sensors for optimum performance, reliability and robustness. Two measurement models are derived for the accumulated and incremental visual odometry measurements. A practical measurement model approach is proposed for the delta position and attitude change measurements that inherently includes delayed-state. The non-Gaussian, non-stationary and correlated error characteristics of VO, that is not suitable to model in a standard Kalman filter, is tackled with averaging the measurements over a Kalman period and utilizing a sigma-test within the filter.

Journal ArticleDOI
TL;DR: In this article, a sequential version of the GINS software has been implemented to achieve single epoch GPS positioning using 1 Hz data from a two week GPS campaign conducted in the Kerguelen Islands.

Proceedings ArticleDOI
28 May 2013
TL;DR: This paper presents a simple and easy to implement sensor data fusion algorithm, using a Kalman filter (KF) in a loosely coupled scheme, for estimation of the velocity and position of an object evolving in a three dimensional space.
Abstract: This paper presents a simple and easy to implement sensor data fusion algorithm, using a Kalman filter (KF) in a loosely coupled scheme, for estimation of the velocity and position of an object evolving in a three dimensional space. A global positioning system (GPS) provides the position measurement while the velocity measurement is taken from the optical flow sensor, finally, the inertial navigation system (INS) gives the acceleration, which is considered as the input of the system. Real time experimental results are shown to validate the proposed algorithm.

Journal ArticleDOI
TL;DR: This letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter.
Abstract: Instead of the extended Kalman filter, the unscented Kalman filter (UKF) has been used in nonlinear systems without initial accurate state estimates over the last decade because the UKF is robust against large initial estimation errors. However, in a multirate integrated system, such as an inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, it is difficult to implement a UKF-based navigation algorithm in a low-grade or mid-grade microcontroller, owing to a large computational burden. To overcome this problem, this letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter. The performance of the modified UKF is verified through numerical simulations.

Journal ArticleDOI
Quan Zhang, Xiaoji Niu1, Qijin Chen, Hongping Zhang, Chuang Shi 
TL;DR: The results show that Allan variance can give the levels of the navigation accuracy of the GNSS/INS systems on different time scales in an effective way, especially for the short-term accuracy.
Abstract: The integration of the Global Navigation Satellite System (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information using a data fusing algorithm such as a Kalman filter. There has been much interest in the absolute accuracy with dominant components of mid-term and long-term errors in current research works related to the GNSS/INS navigation systems, whereas few research works focus on the relative accuracy on different time scales. However, new applications of GNSS/INS integration, such as position and orientation system for mobile mapping systems and the GNSS/INS deeply-coupled receiver, require the relative accuracy on different time scales, especially that of the short-term accuracy. This paper raises the importance of the short-term accuracy of the GNSS/INS systems in certain applications. Current methods to evaluate the navigation accuracy are mainly to provide some statistical values that can represent the overall error level (e.g., RMS), but cannot show the relative accuracy. Allan variance is a method of representing root mean square (RMS) random drift error as a function of average time. An Allan variance analysis method is proposed in this paper to evaluate the relative accuracy on different time scales of GNSS/INS systems. The feasibility of the idea was verified by the road test results of different grades of GNSS/INS systems. The results show that Allan variance can give the levels of the navigation accuracy of the GNSS/INS systems on different time scales in an effective way, especially for the short-term accuracy.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a model-aided navigation method with two valid state prediction models, i.e., an Inertial Navigation System (INS) and a vehicle dynamics model.
Abstract: Model-aided navigation increases navigation accuracy by including a vehicle dynamics model into the filter structure. The commonly used Inertial Navigation System (INS) is hence supplemented by another prediction model for the system state. However, the standard Kalman filter only allows for a single system model to propagate the estimation. The main contribution of this paper is the improvement of an existing approach to estimation with two valid state prediction models. By unifying the models, computation time and state vector size are reduced. Furthermore, the question of how the models must be coupled to achieve optimality and preserve filter stability is addressed. In integrated aircraft navigation, an INS as well as a vehicle dynamics model are available. The presented method unifies these two models and shows superior computational performance compared to existing model-aided navigation methods and among best results. Furthermore, it is easy to implement and easy to extend with aiding sensors. Copyright © 2013 Institute of Navigation.

Journal ArticleDOI
TL;DR: Simulation results indicate that delay-based navigation can reduce the computational complexity by about 20% compared with general augmented Vision/INS (inertial navigation system) navigation, with almost the same estimate accuracy.
Abstract: This paper presents a vision-aided inertial navigation system for small unmanned aerial vehicles (UAVs) in GPS-denied environments. During visual estimation, image features in consecutive frames are detected and matched to estimate the motion of the vehicle with a homography-based approach. Afterwards, the visual measurement is fused with the output of an inertial measurement unit (IMU) by an indirect extended Kalman filter (EKF). A delay-based approach for the measurement update is developed to introduce the visual measurement into the fusion without state augmentation. This method supposes that the estimated error state is stable and invariant during the second half of one visual calculation period. Simulation results indicate that delay-based navigation can reduce the computational complexity by about 20% compared with general augmented Vision/INS (inertial navigation system) navigation, with almost the same estimate accuracy. Real experiments were also carried out to test the performance of the proposed navigation system by comparison with the augmented filter method and a referential GPS/INS navigation.

Journal ArticleDOI
TL;DR: In this article, an interval filtering algorithm is applied to the uncertain integrated system, where the system parameters uncertainties are described by intervals and the IAKF algorithm has the same structure as the standard extended Kalman filtering algorithm.
Abstract: For a nonlinear integrated GPS/IMU system with an uncertain dynamic model, the standard extended Kalman filtering algorithm is no longer applicable. In this research, an interval filtering algorithm is applied to the uncertain integrated system. The system parameters uncertainties are described by intervals. The IAKF algorithm is established for the uncertain integrated system. The IAKF algorithm has the same structure as the standard extended Kalman filtering algorithm. The testing results indicate that the IAKF algorithm is effective for the uncertain nonlinear integrated system, and it can be used to test the chosen parameters of an integrated GPS/IMU system. Thus, the IAKF algorithm has good potential in real-time applications for nonlinear integrated systems with parameter and noise uncertainties.

Journal ArticleDOI
TL;DR: This research work presents new position update architecture (NPUA) which consists of various artificial intelligence neural networks (AINN) that integrate both GPS and INS to overcome the drawbacks of the Kalman filter.
Abstract: An aircraft system mainly relies on a Global Positioning System GPS to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence because of ephemeric error, satellite clock error, multipath error, and signal jamming. To overcome these drawbacks, generally a GPS is integrated with an Inertial Navigation System INS mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman filter KF to provide a robust navigation solution. In the KF approach, the error models of both INS and GPS are required; this leads to the complexity of the system. This research work presents new position update architecture NPUA which consists of various artificial intelligence neural networks AINN that integrate both GPS and INS to overcome the drawbacks of the Kalman filter. The various AINNs that include both static and dynamic networks described for the system are radial basis function neural network RBFNN, backpropagation neural network BPN, forward-only counter propagation neural network FCPN, full counter propagation neural network Full CPN, adaptive resonance theory-counter propagation neural network ART-CPN, constructive neural network CNN, higher-order neural networks HONN, and input-delayed neural networks IDNN to predict the INS position error during GPS absence, resulting in different performances. The performances of the different AINNs are analyzed in terms of root mean square error RMSE, performance index PI, number of epochs, and execution time ET.

Journal ArticleDOI
TL;DR: The paper demonstrates the possibility of extending the utilization range of the GPS-based navigation system to serve as sensor for formation acquisition and coarse formation keeping and achieves an unprecedented degree of realism using a high-fidelity simulation environment with hardware-in-the-loop capabilities.

Journal ArticleDOI
TL;DR: In this article, a low-cost navigation system that fuses the measurements of the inertial navigation system (INS) and the global positioning system (GPS) receiver is developed.