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Showing papers on "GPS/INS published in 2005"


Journal ArticleDOI
TL;DR: Uncertainties in attitude, gyro bias, and GPS antenna lever arm were shown to determine unobservable errors in the position, velocity, and accelerometer bias, proving that all the errors can be made observable by maneuvering.
Abstract: Observability properties of errors in an integrated navigation system are studied with a control-theoretic approach in this paper. A navigation system with a low-grade inertial measurement unit and an accurate single-antenna Global Positioning System (GPS) measurement system is considered for observability analysis. Uncertainties in attitude, gyro bias, and GPS antenna lever arm were shown to determine unobservable errors in the position, velocity, and accelerometer bias. It was proved that all the errors can be made observable by maneuvering. Acceleration changes improve the estimates of attitude and gyro bias. Changes in angular velocity enhance the lever arm estimate. However, both the motions of translation and constant angular velocity have no influence on the estimation of the lever arm. A covariance simulation with an extended Kalman filter was performed to confirm the observability analysis.

205 citations


Proceedings ArticleDOI
29 Jul 2005
TL;DR: In this paper, an autonomous vehicle navigation method by integrating the measurements of IMU, GPS, and digital compass is presented, where two steps are adopted to overcome the low precision of the sensors.
Abstract: Autonomous vehicle navigation with standard IMU and differential GPS has been widely used for aviation and military applications. Our research interesting is focused on using some low-cost off-the-shelf sensors, such as strap-down IMU, inexpensive single GPS receiver. In this paper, we present an autonomous vehicle navigation method by integrating the measurements of IMU, GPS, and digital compass. Two steps are adopted to overcome the low precision of the sensors. The first is to establish sophisticated dynamics models which consider Earth self rotation, measurement bias, and system noise. The second is to use a sigma Kalman filter for the system state estimation, which has higher accuracy compared with the extended Kalman filter. The method was evaluated by experimenting on a land vehicle equipped with IMU, GPS, and digital compass.

172 citations


Journal ArticleDOI
TL;DR: This paper aims to introduce a multi-sensor system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN) utilizing radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks.
Abstract: Most of the present navigation systems rely on Kalman filtering to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In general, INS/GPS integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and growth of position errors with time for INS. Present Kalman filtering INS/GPS integration techniques have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. This paper aims to introduce a multi-sensor system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). Although being able to improve the positioning accuracy, the complexity associated with both the architecture of multi-layer perceptron networks and its online training algorithms limit the real-time capabilities of this technique. This article, therefore, suggests the use of an alternative ANN architecture. This architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. The INS and GPS data are first processed using wavelet multi-resolution analysis (WRMA) before being applied to the RBF network. The WMRA is used to compare the INS and GPS position outputs at different resolution levels. The RBF-ANN module is then trained to predict the INS position errors and provide accurate positioning of the moving platform. Field-test results have demonstrated that substantial improvement in INS/GPS positioning accuracy could be obtained by applying the combined WRMA and RBF-ANN modules.

139 citations


Proceedings ArticleDOI
15 Aug 2005
TL;DR: Simulation and experimental results are shown to compare the performance of the sigma-point filter with a standard EKF approach, which shows faster convergence from inaccurate initial conditions in position/attitude estimation problems.
Abstract: A sigma-point Kalman filter is derived for integrating GPS measurements with inertial measurements from gyros and accelerometers to determine both the position and the attitude of a moving vehicle. Sigma-point filters use a carefully selected set of sample points to more accurately map the probability distribution than the linearization of the standard extended Kalman filter (KKF), leading to faster convergence from inaccurate initial conditions in position/attitude estimation problems. The filter formulation is based on standard inertial navigation equations. The global attitude parameterization is given by a quaternion, while a generalized three-dimensional attitude representation is used to define the local attitude error. A multiplicative quaternion-error approach is used to guarantee that quaternion normalization is maintained in the filter. Simulation and experimental results are shown to compare the performance of the sigma-point filter with a standard EKF approach.

127 citations


Proceedings ArticleDOI
05 Dec 2005
TL;DR: The traveled distance estimated by inertial dead-reckoning is compared with the estimate produced by GPS in experimental conditions where GPS can be used as a reference source for accurate absolute positioning.
Abstract: In this paper, we develop a system for which applications in the field of personal navigation are planned. In the current version, the system embodies a Global Positioning System (GPS) receiver and an inertial measurement unit (IMU), composed of two dual-axis accelerometers and one single-axis gyro. The IMU is positioned at a subject's foot instep, and it is intended to produce estimates of some gait parameters, including stride length, stride time, and walking speed. Data from GPS and IMU are managed by a DSP-based control box. The computations performed by the DSP processor allow to detect subsequent foot contacts by a threshold-based method applied to gyro signal, and to reconstruct the trajectory of the foot instep by numerical strapdown integration. Features of human walking dynamics are incorporated in the algorithm to enhance the estimation accuracy against errors due to sensor noise and integration drift. All computations are performed by the DSP processor in real-time conditions. The foot sensor performance is assessed during outdoor level walking trials. The traveled distance estimated by inertial dead-reckoning is compared with the estimate produced by GPS in experimental conditions where GPS can be used as a reference source for accurate absolute positioning. Results show the remarkable accuracy achieved by foot inertial sensing.

93 citations


Journal ArticleDOI
TL;DR: It is shown that the proposed receiver is a blind receiver that does not require the knowledge of the satellite positions and utilizes this inherent self-coherence property to excise interferers whose temporal structures are different from those of the GPS signals.
Abstract: In this correspondence, we present a self-coherence anti-jamming Global Positioning System (GPS) receiver that relies on the unique structure of the coarse/acquisition (C/A) code of the GPS signals. Since the C/A-code is repeated 20 times within each navigation symbol, the GPS signal exhibits strong self-coherence between chip-rate samples separated by integer multiples of the spreading gain. The proposed receiver utilizes this inherent self-coherence property to excise interferers whose temporal structures are different from those of the GPS signals. It is shown that the proposed receiver is a blind receiver that does not require the knowledge of the satellite positions.

90 citations


Journal ArticleDOI
TL;DR: A new data filtering method, based on the Vondrak filter and the technique of cross-validation, is developed for separating signals from noise in data series, and applied to mitigate GPS multipath effects in applications such as deformation monitoring.
Abstract: Multipath disturbance is one of the most important error sources in high-accuracy global positioning system (GPS) positioning and navigation. A new data filtering method, based on the Vondrak filter and the technique of cross-validation, is developed for separating signals from noise in data series, and applied to mitigate GPS multipath effects in applications such as deformation monitoring. Both simulated data series and real GPS observations are used to test the proposed method. It is shown that the method can be used to successfully separate signals from noise at different noise levels, and for varying signal frequencies as long as the noise level is lower than the magnitude of the signals. A multipath model can be derived, based on the current-day GPS observations, with the proposed method and used to remove multipath errors in subsequent days of GPS observations when taking advantage of the sidereal day-to-day repeating characteristics of GPS multipath signals. Tests have shown that the reduction in the root mean square (RMS) values of the GPS errors ranges from 20% to 40% when the method is applied.

89 citations


16 Sep 2005
TL;DR: In this article, the performance of EKF-based and sigma-point Kalman filter-based tightly coupled GPS/INS systems is compared in numerical simulations, including situations with less than four satellites in view, and the simulation results were confirmed by post-processing of raw GPS and inertial sensor data that was recorded during a test drive.
Abstract: In this paper, the fusion of GPS pseudorange and deltarange measurements with inertial sensor data is adressed. For many years, extended Kalman filters (EKF) have been applied for this task with great success. However, from a theoretical point of view, the EKF is a sub-optimal choice: The system dynamics model, which is given by the inertial navigation strapdown equations, as well as the pseudorange and deltarange measurement models are nonlinear. The EKF approximates the propagation of Gaussian random vectors through these nonlinear equations by a linear transformation. This allows to capture the variance-covariance matrix of the propagated Gaussian random vectors with first order accuracy only. The family of sigma-point Kalman filters (SPKF) offers an approximation of variance-covariance matrix which is accurate to at least second order. Therefore, the performance of EKF-based and SPKFbased tightly coupled GPS/INS systems is compared in numerical simulations. Different inertial sensor grades from MEMS to FOG and a variety of scenarios are investigated, including situations with less than four satellites in view. Additionally, the simulation results were confirmed by the post-processing of raw GPS and inertial sensor data that was recorded during a test drive. It was found that except for specific situations without practical relevance, EKF and SPKF offer an identical performance. This is due to the fact that for tightly coupled - as well as loosely coupled - GPS/INS integration the higher-order transformation terms are negligible, which is shown analytically.

72 citations


Journal ArticleDOI
TL;DR: A description of the design, operation, and test results of a miniature, low-cost, integrated GPS/inertial navigation system that uses commercial off-the-shelf micro-electro-mechanical system (MEMS) accelerometers and gyroscopes.
Abstract: A description of the design, operation, and test results of a miniature, low-cost, integrated GPS/inertial navigation system that uses commercial off-the-shelf micro-electro-mechanical system (MEMS) accelerometers and gyroscopes. The MEMS inertial measurement unit (EMU) is packaged in a small size and provides the raw EMU data through a serial interface to a processor board where the inertial navigation solution and integrated GPS/inertial Kalman filter is generated. The GPS/inertial software integration is performed using NAVSYS' modular InterNav software product. This allows integration with different low-cost GPS chip sets or receivers and also allows the integrated GPS/inertial navigation solution to be embedded as an application on a customer's host computer. This modular object-oriented architecture facilitates integration of the miniature MEMS GPS/INS navigation system for embedded navigation applications and is designed to handle the large errors characteristic of a low-grade MEMS IMU. Test results are presented showing the performance of the integrated MEMS GPS/inertial navigation system. Data is provided showing the position, velocity, and attitude accuracy when operating with GPS aiding and also for periods where GPS dropouts occur and alternative navigation update sources are used to bound the MEMS inertial navigation error growth.

60 citations


01 Jan 2005
TL;DR: In this thesis a terrain positioning method for underwater vehicles called the correlation method is presented and the well known problem with multiple terrain positions is discussed and the proposed method achieves this bound asymptotically.
Abstract: In this thesis a terrain positioning method for underwater vehicles called the correlation method is presented. Using the method the vehicle can determine its absolute position with the help of a s ...

58 citations


Journal ArticleDOI
TL;DR: In this paper, a projection particle filter for an exponential family of densities is proposed, and it is shown that under certain conditions the approximated conditional density of the state converges to the true conditional density.

Journal ArticleDOI
TL;DR: The use of map matching and height aiding is described, and the effect of different terrain resolutions (Ordnance Survey 1:50,000 and 1:10,000 scale DTMs) on plan position and elevation accuracy for vehicle tracking is examined, and a higher-order interpolant is shown to slightly improve performance.

Patent
02 Mar 2005
TL;DR: In this paper, a Kalman filter is used to generate the corrective feedback as a function of at least one of GPS/DGPS information, sensor information, user input, terrain correlation information, signal-of-opportunity information, and/or position information output by a motion classifier.
Abstract: A navigation system includes an inertial navigation unit. The navigation system also includes a Kalman filter that generates corrective feedback for use by the inertial navigation unit. The Kalman filter generates the corrective feedback as a function of at least one of GPS/DGPS information, sensor information, user input, terrain correlation information, signal-of-opportunity information, and/or position information output by a motion classifier.

Journal ArticleDOI
TL;DR: Wavelet techniques are applied for de-noising the inertial measurements to minimize the undesirable effects of sensor noise and other disturbances and results showed that the positioning performance using de- noised data improves by 34%–63%.
Abstract: The integration of the Global Positioning System (DGPS) with an Inertial Navigation System (INS) has been implemented for several years. In an integrated INS/DGPS system, the DGPS provides positions while the INS provides attitudes. In case of DGPS outages (signal blockages), the INS is used for positioning until the DGPS signals are available again. One of the major issues that limit the INS accuracy, as a stand-alone navigation system, is the level of sensor noise. The problem with inertial data is that the required signal is buried into a large window of high frequency noise. If such noise component could be removed, the overall inertial navigation accuracy is expected to improve considerably. The INS sensor outputs contain actual vehicle motion and sensor noise. Therefore, the resulting position errors are proportional to the existing sensor noise and vehicle vibrations. In this paper, wavelet techniques are applied for de-noising the inertial measurements to minimize the undesirable effects of sensor noise and other disturbances. To test the efficiency of inertial data de-noising, two road vehicle INS/DGPS data sets are utilized. Compared to the obtained position errors using the original inertial measurements, the results showed that the positioning performance using de-noised data improves by 34%–63%.

Patent
01 Feb 2005
TL;DR: In this paper, a three-dimensional and a two-dimensional GPS unit periodically provide coordinate positions of points (14, 16, 18) on a work machine and are combined with a Kalman filter to produce a point of reference on the work machine.
Abstract: A three-dimensional and a two-dimensional GPS unit periodically provide coordinate positions of points (14, 16, 18) on a work machine (10) and are combined with a Kalman filter to produce a point of reference on the work machine. The point of reference is improved by combining the position with an inertial position derived from accelerometer data in another Kalman filter. Additionally, the two-dimensional GPS unit provides a heading that is combined with an inertial orientation derived by the angular rate from a gyroscope in another Kalman filter to produce a precise orientation estimate. Inclinometers (26) provide pitch and roll of the work machine. With the point of reference, orientation estimate, pitch, roll and known geometry of the work machine, the invention calculates the location and orientation of machine components (14, 16, 18) and continuously tracks its movement throughout the mine. Additionally, the accelerometers and gyroscope provide position and orientation when the GPS units are inoperable.

16 Sep 2005
TL;DR: This paper will quantify timing biases between the different legacy and modernized GPS and Galileo signals broadcast on L1 and their dependencies on factors like user receiver filter bandwidth, filter transfer function, and delay-locked loop (DLL) correlator spacing.
Abstract: : GPS timing and navigation user solutions are based on pseudorange measurements made by correlating user receiver-generated replica signals with the signals broadcast by the GPS satellites. Any bias resulting from this correlation process within the user receiver tends to be common across all receiver channels when the signal characteristics are identical (code type, modulation type, and bandwidth). Such common biases will cancel in the user navigation solution and appear as a fixed bias for timing solutions. New GPS signals and the future addition of the Galileo system are somewhat different from the legacy signals broadcast by GPS today and new ways of accounting for biases will be needed. This paper will quantify timing biases between the different legacy and modernized GPS and Galileo signals broadcast on L1 and their dependencies on factors like user receiver filter bandwidth, filter transfer function, and delay-locked loop (DLL) correlator spacing.

26 Jan 2005

Proceedings ArticleDOI
29 Jul 2005
TL;DR: In this article, a real-time implementation of a Kalman filter based estimator is presented, which is fused with vehicle sensors's data to achieve decimeter-level accuracy position and degree-level heading estimation for the car.
Abstract: Real time implementation of a Kalman filter based estimator is presented. Differential GPS data is fused with vehicle sensors's data to achieve decimeter-level accuracy position and degree-level heading estimation for the car. Vehicles sensors consist of wheel speed, steering angle and yaw rate. A dynamic bicycle model is utilized as the process model. Due to its nonlinearities we use an extended Kalman filter. GPS noise is not white Gaussian in practice. Different kinds of GPS noise are discussed. Experimental results on the performance of the filter are presented.

Journal ArticleDOI
TL;DR: In this paper, the authors compared four different approaches for using these terrain contour navigation (TCN) fixes to calibrate the INS and showed that the single hypothesis IGMAP technique offers the best balance between accuracy, robustness and processing efficiency.
Abstract: The benefits of integrated INS/GPS systems are well known. However, the knowledge required to jam GPS is becoming public and the hardware to achieve this is basic. When GPS data are unavailable and a low grade INS is used, navigation accuracy quickly degrades to an unacceptable level. The addition of one or more terrain referenced navigation (TRN) systems to an integrated INS/GPS navigation system enables the INS to be calibrated during GPS outages, increasing the robustness of the overall navigation solution. TRN techniques are compared and integration architectures are reviewed. For the initial studies of INS/GPS/TRN integration, radar altimeter based terrain contour navigation (TCN) with a batch processing algorithm is used in conjunction with a centralised integration filter. Four different approaches for using these TCN fixes to calibrate the INS are compared. These are a best fix method, a weighted fix method using a probabilistic data association filter (PDAF) and single and multi-hypothesis versions of the Iterative Gaussian Mixture Approximation of the Posterior (IGMAP) method. Simulation results are presented showing that the single hypothesis IGMAP technique offers the best balance between accuracy, robustness and processing efficiency.

Proceedings ArticleDOI
15 Aug 2005
TL;DR: The performance of EKF and SPKF applied to tightly-coupled GPS/INS integration is compared in numerical simulations and it is found that except in specic situations without practical relevance, both approaches have an identical performance.
Abstract: This paper adresses the fusion of GPS measurements and inertial sensor data in tightly coupled GPS/INS navigation systems. Usually, an extended Kalman lter (EKF) is applied for this task. However, as the system dynamic model as well as the pseudorange and deltarange measurement models are nonlinear, the EKF is a sub-optimal choice from a theoretical point of view, as it approximates the propagation of mean and covariance of Gaussian random vectors through these nonlinear models by a linear transformation, which is accurate to rst-order only. The sigma-point Kalman lter (SPKF) family of algorithms achieve an accurate approximation to at least second-order. Therefore, the performance of EKF and SPKF applied to tightly-coupled GPS/INS integration is compared in numerical simulations. It is found that except in specic situations without practical relevance, both approaches oer an identical performance. This indicates that the SPKF may be an option considering the design of new systems, but a modication of existing EKF-based GPS/INS systems is neither required, nor appropriate to improve their performance.

Journal ArticleDOI
TL;DR: In this paper, the authors focused on three error modeling and compensation techniques that could potentially improve GPS/INS system performance on both land-based and airborne platforms: (1) extended gravity compensation, (2) IMU signal de-noising, and (3) stochastic modeling of IMU errors.
Abstract: Direct georeferencing, also referred to as direct platform orientation (DPO), is defined as direct measurement of the imaging sensor external orientation parameters (EOP), using positioning and orientation sensors, such as the Global Positioning System (GPS) and Inertial Navigation System (INS) or Inertial Measurement Unit (IMU). Imaging sensors, most frequently supported by the DPO technique, are digital cameras, lidar systems, multi-spectral or hyper-spectral scanners, or interferometric synthetic aperture radar (INSAR). While for scanning sensors the use of DPO is compulsory, frame digital cameras can also directly benefit from this modern technique of sensor orientation. With direct sensor orientation, the requirement for ground control, tie-point matching and aerotriangulation (AT) is significantly reduced, or even entirely eliminated, resulting in shorter times of data acquisition and processing, and streamlined and highly automated data workflow and quality control. Most of the time, the requirement for ground control points is limited to periodic system calibrations and quality control check. Direct georeferencing is considered a fundamental technology of conventional mobile mapping systems (MMS). Despite significant progress in GSP/INS-based direct georeferencing technology in the last decade, there is still room for improvement in terms of better accuracy and better tolerance to GPS outages. This paper is focused on three error modeling and compensation techniques that could potentially improve GPS/INS system's performance on both land-based and airborne platforms: (1) extended gravity compensation, (2) IMU signal de-noising, and (3) stochastic modeling of IMU errors.

Journal ArticleDOI
TL;DR: The results of simulation studies and field experiments indicate that the proposed procedure improves the performance of single-frequency ambiguity resolution in terms of both reliability and time-to-fix-ambiguity.
Abstract: In order to achieve a precise positioning solution from GPS, the carrier-phase measurements with correctly resolved integer ambiguities must be used. Based on the integration of GPS with pseudolites and Inertial Navigation Systems (INS), this paper proposes an effective procedure for single-frequency carrier-phase integer ambiguity resolution. With the inclusion of pseudolites and INS measurements, the proposed procedure can speed up the ambiguity resolution process and increase the reliability of the resolved ambiguities. In addition, a recently developed ambiguity validation test, and a stochastic modelling scheme (based on-line covariance matrix estimation) are adapted to enhance the quality of ambiguity resolution. The results of simulation studies and field experiments indicate that the proposed procedure indeed improves the performance of single-frequency ambiguity resolution in terms of both reliability and time-to-fix-ambiguity.

Proceedings ArticleDOI
08 Sep 2005
TL;DR: An extended Kalman filter for closed loop integration between the GPS and INS is derived and propagates and estimates the error states, which are fed back to the INS for correction of the internal navigation states.
Abstract: In this paper an approach for integration between GPS and inertial navigation systems (INS) is described. The continuous-time navigation and error equations for an earth-centered earth-fixed INS system are presented. Using zero order hold sampling, the set of equations is discretized. An extended Kalman filter for closed loop integration between the GPS and INS is derived. The filter propagates and estimates the error states, which are fed back to the INS for correction of the internal navigation states. The integration algorithm is implemented on a host PC, which receives the GPS and inertial measurements via the serial port from a tailor made hardware platform, which is briefly discussed. Using a battery operated PC the system is fully mobile and suitable for real-time vehicle navigation. Simulation results of the system are presented.

Journal ArticleDOI
TL;DR: The flight test results show that the proposed ADGPS/INS integrated navigation unit gives reliable navigation performance even when anomalous GPS data is provided and gives better navigation performance than a conventional GPS/INS unit.
Abstract: Due to their complementary features of GPS and INS, the GPS/INS integrated navigation system is increasingly being used for a variety of commercial and military applications. An attitude determination GPS (ADGPS) receiver, with multiple antennas, can be more effectively integrated with a low-cost IMU since the receiver gives not only position and velocity data but also attitude data. This paper proposes a low-cost attitude determination GPS/INS integrated navigation system. The proposed navigation system comprises an ADGPS receiver, a navigation computer unit (NCU), and a low-cost commercial MEMS IMU. The navigation software includes a fault detection and isolation (FDI) algorithm for integrity. In order to evaluate the performance of the proposed navigation system, two flight tests have been performed using a small aircraft. The first flight test confirmed the fundamental operation of the proposed navigation system and the effectiveness of the FDI algorithm. The second flight test evaluated the performance of the proposed navigation system and demonstrated the benefit of GPS attitude information in a high dynamic environment. The flight test results show that the proposed ADGPS/INS integrated navigation unit gives reliable navigation performance even when anomalous GPS data is provided and gives better navigation performance than a conventional GPS/INS unit.

Patent
05 Aug 2005
TL;DR: In this paper, a modular device, system and associated method, used to enhance the quality and output speed of any generic GPS engine, is provided, which consists of an inertial subsystem based on a solid state gyroscope having a plurality of accelerometers and a multiplicity of angular rate sensors designed to measure linear acceleration and rotation rates around a number of axes.
Abstract: A modular device, system and associated method, used to enhance the quality and output speed of any generic GPS engine is provided. The modular device comprises an inertial subsystem based on a solid state gyroscope having a plurality of accelerometers and a plurality of angular rate sensors designed to measure linear acceleration and rotation rates around a plurality of axes. The modular inertial device may be placed in the data stream between a standard GPS receiver and a guidance device to enhance the accuracy and increase the frequency of positional solutions. Thus, the modular inertial device accepts standard GPS NMEA input messages from the source GPS receiver, corrects and enhances the GPS data using computed internal roll and pitch information, and produces an improved, more accurate, NMEA format GPS output at preferably 2 times the positional solution rate using GPS alone. The positional solution frequency using the present invention may increase to as much as 5 times that obtained using GPS alone. Moreover, the modular inertial device may assist when the GPS signal is lost for various reasons. If used without GPS, the modular inertial device may be used to define, and adjust, a vehicle's orientation on a relative basis. The modular inertial device and architecturally partitioned system incorporated into an existing GPS system may be applied to navigation generally, including high-precision land-based vehicle positioning, aerial photography, crop dusting, and sonar depth mapping to name a few applications.

Journal ArticleDOI
TL;DR: A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence the filter gain, and tested in the developed INS/GPS integrated marine navigation system.
Abstract: A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.

Proceedings ArticleDOI
05 Jun 2005
TL;DR: This study proposes to introduce a Metropolis-Hastings step to accept/reject the particles updated by the regularization process to prevent the degeneracy without introducing additional noise on the estimates.
Abstract: Hybridization techniques receive a renewed interest due to recent navigation systems such as Galileo. Hybridization takes advantage of the complementarity of different sensor types to increase navigation performance. This study focuses on the integration of the Global Positioning System (GPS) and the inertial navigation systems (INS). GPS allows to compensate for the long term drift of INS estimates, while aided INS provide a solution in case of GPS signal blocking. The GPS/INS coupling is performed by a nonlinear filtering approach whereby GPS measurements are used to correct INS estimates. However, a classical particle filter is bound to diverge due to the dynamics of the unknown parameters. Indeed, the state model has a small process noise and is exponentially unstable. The regularized particle filter presented in N. Oudjane et al. (2000) allows to overcome this limitation at the expense of an increased estimation variance. This study proposes to introduce a Metropolis-Hastings step to accept/reject the particles updated by the regularization process. The method is shown to prevent the degeneracy without introducing additional noise on the estimates.

Patent
14 Jan 2005
TL;DR: In this paper, a system and method for generating a navigation solution in high interference and dynamic environments using Global Positioning System (GPS) and inertial measurement unit (IMU) data is described.
Abstract: A system and method for generating a navigation solution in high interference and dynamic environments using Global Positioning System (GPS) and inertial measurement unit (IMU) data is described. The system includes an Advanced Tightly Coupled (ATC) tracking processor for a multi-satellite tracking system. The ATC accepts early, late, and on-time I and Q data from the GPS signal tracker and outputs vehicle-to-satellite range and range rate residual measurements to a navigation Kalman filter. The ATC includes nonlinear discriminators which transform I and Q data into linear residual measurements corrupted by unbiased, additive, and white noise. It also includes an amplitude estimator configured to operate in rapidly changing, high power noise; a measurement noise variance estimator; and a linear residual smoothing filter for input to the navigation filter.

Proceedings ArticleDOI
06 Jun 2005
TL;DR: This paper investigates the use of LiDAR within an indoor corridor environment (i.e. hallway) to update IMU measurements and shows how this combination is effective in providing accurate state estimates while removing sensor errors due to noise and bias.
Abstract: Autonomous capability requires reliable and robust navigation solutions in multiple environments. GPS has become an effective tool but is not suitable for all environments. Laser scanners are quickly making their presence known in the navigation field and are proven to have a variety of uses. This paper investigates the use of LiDAR within an indoor corridor environment (i.e. hallway) to update IMU measurements. The LiDAR is combined with an IMU in a Kalman filter to produce estimates of vehicle velocity, heading, lateral error, and sensor biases. It is shown how this combination is effective in providing accurate state estimates while removing sensor errors due to noise and bias.

Journal ArticleDOI
TL;DR: Simulations show that the bias due to aiding doppler offsets resulting in a bias in the tracking loop can be appropriately modelled and removed and could be effectively addressed by appropriate modelling.
Abstract: Tracking dynamics on the GPS signal is still a big challenge to the receiver designer as the operating conditions are becoming more volatile. Optimizing the stand-alone system for dynamics generally degrades the accuracy of measurements. Therefore, an inertial navigation system (INS) is integrated with GPS to address this issue. Doppler derived from INS can be used to aid the carrier tracking loop for improving the performance under dynamic conditions. However, the derived doppler does not truly reflect the GPS signal doppler due to errors in inertial sensors. As the tracking loop bandwidth is reduced significantly in ultra-tightly integrated systems, any offsets in the aiding doppler creates undesired correlations in the tracking loop resulting in sub-optimal performance of the loop. The paper addresses this issue and also provides a mitigating mechanism to reduce the effects of incorrect estimates of the doppler. It is shown that doppler offsets resulting in a bias in the tracking loop can be appropriately modelled and removed. Mathematical algorithms pertaining to this are provided and the results are summarized. Simulations show that the bias due to aiding doppler offsets could be effectively addressed by appropriate modelling.