Showing papers on "Inertial navigation system published in 2007"

A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

Abstract: In this paper, we present an extended Kalman filter (EKF)-based algorithm for real-time vision-aided inertial navigation. The primary contribution of this work is the derivation of a measurement model that is able to express the geometric constraints that arise when a static feature is observed from multiple camera poses. This measurement model does not require including the 3D feature position in the state vector of the EKF and is optimal, up to linearization errors. The vision-aided inertial navigation algorithm we propose has computational complexity only linear in the number of features, and is capable of high-precision pose estimation in large-scale real-world environments. The performance of the algorithm is demonstrated in extensive experimental results, involving a camera/IMU system localizing within an urban area.

An introduction to inertial navigation

Abstract: Until recently the weight and size of inertial sensors has prohibited their use in domains such as human motion capture. Recent improvements in the performance of small and lightweight micromachined electromechanical systems (MEMS) inertial sensors have made the application of inertial techniques to such problems possible. This has resulted in an increased interest in the topic of inertial navigation, however current introductions to the subject fail to sufficiently describe the error characteristics of inertial systems. We introduce inertial navigation, focusing on strapdown systems based on MEMS devices. A combination of measurement and simulation is used to explore the error characteristics of such systems. For a simple inertial navigation system (INS) based on the Xsens Mtx inertial measurement unit (IMU), we show that the average error in position grows to over 150 m after 60 seconds of operation. The propagation of orientation errors caused by noise perturbing gyroscope signals is identified as the critical cause of such drift. By simulation we examine the significance of individual noise processes perturbing the gyroscope signals, identifying white noise as the process which contributes most to the overall drift of the system. Sensor fusion and domain specific constraints can be used to reduce drift in INSs. For an example INS we show that sensor fusion using magnetometers can reduce the average error in position obtained by the system after 60 seconds from over 150 m to around 5 m. We conclude that whilst MEMS IMU technology is rapidly improving, it is not yet possible to build a MEMS based INS which gives sub-meter position accuracy for more than one minute of operation.

Map-Based Precision Vehicle Localization in Urban Environments

Abstract: Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy. Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings. We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments. Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps. Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap. By reducing the final map to the flat road surface, imprints of other vehicles are removed. The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution. To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map. As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude. Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10centimeter range. Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic. The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads.

A new multi-position calibration method for MEMS inertial navigation systems

Abstract: The Global Positioning System (GPS) is a worldwide navigation system that requires a clear line of sight to the orbiting satellites For land vehicle navigation, a clear line of sight cannot be maintained all the time as the vehicle can travel through tunnels, under bridges, forest canopies or within urban canyons In such situations, the augmentation of GPS with other systems is necessary for continuous navigation Inertial sensors can determine the motion of a body with respect to an inertial frame of reference Traditionally, inertial systems are bulky, expensive and controlled by government regulations Micro-electro mechanical systems (MEMS) inertial sensors are compact, small, inexpensive and most importantly, not controlled by governmental agencies due to their large error characteristics Consequently, these sensors are the perfect candidate for integrated civilian navigation applications with GPS However, these sensors need to be calibrated to remove the major part of the deterministic sensor errors before they can be used to accurately and reliably bridge GPS signal gaps A new multi-position calibration method was designed for MEMS of high to medium quality The method does not require special aligned mounting and has been adapted to compensate for the primary sensor errors, including the important scale factor and non-orthogonality errors of the gyroscopes A turntable was used to provide a strong rotation rate signal as reference for the estimation of these errors Two different quality MEMS IMUs were tested in the study The calibration results were first compared directly to those from traditional calibration methods, eg six-position and rate test Then the calibrated parameters were applied in three datasets of GPS/INS field tests to evaluate their accuracy indirectly by comparing the position drifts during short-term GPS signal outages

Non-GPS navigation for security personnel and first responders

Abstract: This paper introduces a “Personal Dead-reckoning” (PDR) navigation system for walking persons. The system is useful for monitoring the position of emergency responders inside buildings, where GPS is unavailable. The PDR system uses a six-axes Inertial Measurement Unit attached to the user’s boot. The system’s strength lies in the use of a technique known as “Zero Velocity Update” (ZUPT) that virtually eliminates the ill-effects of drift in the accelerometers. It works very well with different gaits, as well as on stairs, slopes, and generally on 3-dimensional terrain. This paper explains the PDR and presents extensive experimental results, which illustrate the utility and practicality of the system.

Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization

Abstract: We present a position estimation scheme for cars based on the integration of global positioning system (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. The data also shows GPS and vehicle sensor integration through a bicycle model compares favorably with position estimation by fusing GPS and inertial navigation system (INS) through a kinematic model.

Relative Pose Calibration Between Visual and Inertial Sensors

Abstract: This paper proposes an approach to calibrate off-the-shelf cameras and inertial sensors to have a useful integrated system to be used in static and dynamic situations. When both sensors are integrated in a system their relative pose needs to be determined. The rotation between the camera and the inertial sensor can be estimated, concurrently with camera calibration, by having both sensors observe the vertical direction in several poses. The camera relies on a vertical chequered planar target and the inertial sensor on gravity to obtain a vertical reference. Depending on the setup and system motion, the translation between the two sensors can also be important. Using a simple passive turntable and static images, the translation can be estimated. The system needs to be placed in several poses and adjusted to turn about the inertial sensor centre, so that the lever arm to the camera can be determined. Simulation and real data results are presented to show the validity and simple requirements of the proposed methods.

Calibration and data fusion solution for the miniature attitude and heading reference system

Abstract: Development, calibration and alignment of a miniature magnetic and inertial measurement unit, which is used as an attitude and heading reference system, are presented. Several guidelines were followed during the design process to make the magnetic and inertial measurement unit suitable for various kinds of applications, thus the system is designed both as small as possible but still modular, consisting of three inertial sensor units, a magnetic sensor unit and a control unit. Complete calibration and alignment procedure is described and an adaptive Kalman filter concept for fusing various sensors’ attitude and heading data is introduced and discussed. The characteristics of the magnetic and inertial measurement unit as an attitude and heading reference system are evaluated. The algorithm showed remarkable performance in the orientation determination as the average root mean square error was less than 1.2° over the entire appliable operating range.

High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

Abstract: User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks

Abstract: In this paper we describe an extended Kalman filter algorithm for estimating the pose and velocity of a spacecraft during entry, descent, and landing. The proposed estimator combines measurements of rotational velocity and acceleration from an inertial measurement unit (IMU) with observations of a priori mapped landmarks, such as craters or other visual features, that exist on the surface of a planet. The tight coupling of inertial sensory information with visual cues results in accurate, robust state estimates available at a high bandwidth. The dimensions of the landing uncertainty ellipses achieved by the proposed algorithm are three orders of magnitude smaller than those possible when relying exclusively on IMU integration. Extensive experimental and simulation results are presented, which demonstrate the applicability of the algorithm on real-world data and analyze the dependence of its accuracy on several system design parameters. © 2007 Wiley Periodicals, Inc.

Introduction to Modern Navigation Systems

Abstract: Vectors and Matrices Coordinate Transformation between Orthonormal Frames Forms of the Transformation Matrix Earth and Navigation The Inertial Navigation System Equations Implementation Air Data Computer Polar Navigation Alignment Attitude and Heading Reference System Inertially Aided System Appendices.

Omnidirectional Pedestrian Navigation for First Responders

Abstract: It might be assumed that dead reckoning approaches to pedestrian navigation could address the needs of first responders, including fire fighters. However, conventional PDR approaches with body-mounted motion sensors can fail when used with the irregular walking patterns typical of urban search and rescue missions. In this paper, a technique using shoe-mounted sensors and inertial mechanization equations to directly calculate the displacement of the feet between footfalls is described. Zero-velocity updates (ZUPTs) at foot standstills limit the drift that would otherwise occur with inexpensive IMUs. We show that the technique is fairly accurate in terms of distance travelled and can handle many arbitrary manoevers, such as tight turns, side/back stepping and stair climbing.

Cascaded Kalman Filters for Accurate Estimation of Multiple Biases, Dead-Reckoning Navigation, and Full State Feedback Control of Ground Vehicles

Abstract: This paper develops a cascaded estimation algorithm for estimating all of the biases and states for full state feedback and dead reckoning of a farm tractor through short global positioning system (GPS) outages First, a conventional (one stage) estimation scheme is presented The single state estimation scheme is shown to have degraded performance in bias state estimation and dead-reckoning due to vehicle model errors However, the states for position and velocity are not highly coupled to the tractor dynamic states, allowing for separation of the estimators Therefore, the state estimation algorithms are divided into two cascaded estimators in order to prevent the errors in the vehicle model from corrupting the navigation states A dead reckoning (or navigation) estimator estimates all of the inertial sensor biases while GPS is available When GPS is not available, the dead reckoning estimator integrates rate measurements to provide position and heading estimates in order to maintain continuous control of the vehicle through these GPS outages A second estimator is then used to estimate the additional states needed for full state feedback control algorithms Bias estimates from the dead reckoning estimator are used to correct the sensor measurement used in the second estimator An extended kalman filter (EKF) is utilized for each of the estimators Results are given, showing that the cascaded estimation technique provides better estimation of the vehicle states over a conventional estimation scheme, especially during a GPS outage Results are also given which verify the ability of the estimation algorithm to estimate all of the system biases and provide continuous control of the tractor through a short GPS outage

Simulation of an Inertial Acoustic Navigation System With Range Aiding for an Autonomous Underwater Vehicle

Abstract: This paper presents an integrated navigation system for underwater vehicles to improve the performance of a conventional inertial acoustic navigation system by introducing range measurement. The integrated navigation system is based on a strapdown inertial navigation system (SDINS) accompanying range sensor, Doppler velocity log (DVL), magnetic compass, and depth sensor. Two measurement models of the range sensor are derived and augmented to the inertial acoustic navigation system, respectively. A multirate extended Kalman filter (EKF) is adopted to propagate the error covariance with the inertial sensors, where the filter updates the measurement errors and the error covariance and corrects the system states when the external measurements are available. This paper demonstrates the improvement on the robustness and convergence of the integrated navigation system with range aiding (RA). This paper used experimental data obtained from a rotating arm test with a fish model to simulate the navigational performance. Strong points of the navigation system are the elimination of initial position errors and the robustness on the dropout of acoustic signals. The convergence speed and conditions of the initial error removal are examined with Monte Carlo simulation. In addition, numerical simulations are conducted with the six-degrees-of-freedom (6-DOF) equations of motion of an autonomous underwater vehicle (AUV) in a boustrophedon survey mode to illustrate the effectiveness of the integrated navigation system.

Imaging and display system to aid helicopter landings in brownout conditions

Abstract: An imaging and display system provides helicopter pilots with an unobstructed display of a landing area in a brownout or whiteout condition by capturing a high resolution image of the landing area prior to obscuration. Using inertial navigation information from the aircraft or an independent system, the system transforms the image to a desired viewpoint and overlays a representation of the helicopter's current position relative to the landing area. The system thus greatly improves orientation and situational awareness, permitting safe and effective operation under zero visibility brownout conditions.

Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks

Abstract: Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique

Invariant observers for attitude and heading estimation from low-cost inertial and magnetic sensors

Abstract: In this paper we propose invariant nonlinear observers for attitude and heading estimation using directly the measurements from low-cost inertial and magnetic sensors. In particular we propose a simple and easy-to-tune observer where moreover the estimated attitude behaves well even in the presence of magnetic disturbances.

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Abstract: Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.

Design and Implementation of a Mechatronic, All-Accelerometer Inertial Measurement Unit

Abstract: This paper discusses the design, calibration, simulation, and experimental validation of a kinematically redundant inertial measurement unit that is based solely on accelerometers. The sensor unit comprises 12 accelerometers, two on each face of a cube. The location and direction of the sensors are determined so as to locally optimize the numerical conditioning of the system of governing kinematic equations. The orientational installation error of each sensor is identified by off-line iterative processing of the gravitational acceleration measurements made at a number of known orientations of the unit, thus allowing subsequent calibration. Furthermore, a novel procedure is developed through which the acceleration measurements can be used to directly determine the body angular velocity; this results in a major accuracy improvement over similar works whereby the angular velocity is obtained via integrating the angular acceleration. Experimental results are presented to validate the methodology, design, and implementation.

Failure modes and models for integrated GPS/INS systems

Abstract: GPS is the most widely used global navigation satellite system. By design, there is no provision for real time integrity information within the Standard Positioning Service (SPS). However, in safety critical sectors like aviation, stringent integrity performance requirements must be met. This can be achieved externally or at the receiver level through receiver autonomous integrity monitoring (RAIM). The latter is a cost effective method that relies on data consistency, and therefore requires redundant measurements. An external aid to provide this redundancy can be in the form of an Inertial Navigation System (INS). This should enable continued performance even during RAIM holes (when no redundant satellite measurements are available). However, due to the inclusion of an additional system and the coupling mechanism, integrity issues become more challenging. To develop an effective integrity monitoring capability, a good understanding of the potential failure modes of the integrated system is vital. In this paper potential failure modes of integrated GPS/INS systems are identified. This is followed by the specification of corresponding models that would be required to investigate the capability of existing integrity algorithms and to develop enhancements or new algorithms.

Real-time high accuracy position and orientation system

Abstract: A real-time high accuracy position and orientation system (RT-HAPOS) system for a vehicle, such as an aircraft, comprises a global navigation satellite system (GNSS) receiver disposed on the vehicle and an integrated inertial navigation (IIN) module disposed on the vehicle. The GNSS receiver generates GNSS position data indicating approximate positions of the vehicle during a data acquisition period in which the vehicle is moving. The IIN module executes a real-time kinematic (RTK) algorithm during the data acquisition period to generate output position data indicating positions of the vehicle at a greater precision than the GNSS position data, based on the GNSS position data, inertial measurement data acquired on the vehicle during the data acquisition period, and a set of virtual reference station (VRS) observables received during the data acquisition period from a remote source external to the vehicle, where the VRS observables are based on the GNSS position data.

Fusion of Low-Cost Imaging and Inertial Sensors for Navigation

Abstract: : Aircraft navigation information (position, velocity, and attitude) can be determined using optical measurements from imaging sensors combined with an inertial navigation system. This can be accomplished by tracking the locations of optical features in multiple images and using the resulting geometry to estimate and remove inertial errors. A critical factor governing the performance of image-aided inertial navigation systems is the robustness of the feature tracking algorithm. Previous research has shown the strength of rigorously coupling the image and inertial sensors at the measurement level using a tactical-grade inertial sensor. While the tactical-grade inertial sensor is a reasonable choice for larger platforms, the greater physical size and cost of the sensor limits its use in smaller, low-cost platforms. In this paper, an image-aided inertial navigation algorithm is implemented using a multi-dimensional stochastic feature tracker. In contrast to previous research, the algorithms are specifically evaluated for operation using lowcost, CMOS imagers and MEMS inertial sensors. The performance of the resulting image-aided inertial navigation system is evaluated using Monte Carlo simulation and experimental data and compared to the performance using more expensive inertial sensors.

An Instrumentation System Applied to Formation Flight

Abstract: As part of a NASA dryden autonomous formation flight program for improved drag reduction of multiple F/A-18 aircraft, a new instrument, the formation flight instrumentation system (FFIS), for the precise estimation of the relative position, velocity, and attitude between two moving aircraft without the aid of ground-based instruments, was developed. The FFIS uses a global position system (GPS) receiver and an inertial navigation sensor (INS) instrumentation package on each aircraft combined with a wireless communication system for sharing measurements between vehicles. An extended Kalman filter structure blends the outputs of each GPS/INS in a distributed manner so as to maximize the accuracy of the relative state estimates. Differential carrier phase GPS measurements are used to provide high accuracy relative range measurements to the filtering algorithm. A multiple hypothesis Wald test for estimating the integer ambiguity between the two moving vehicles was developed as part of this project. The FFIS was tested in a hardware-in-the-loop simulation (HIL Sim) before being tested in actual F-18 flight tests. Test results validated the FFIS performance. Flight test results showed that the Wald test accurately estimates the integer ambiguity and that relative range estimates using least squares provide accurate position estimates with a mean of approximately 7 cm and a standard deviation of 13 cm

Small UAV Automation Using MEMS

Abstract: This paper presents a framework for the automation of a small UAV using a low cost sensor suite, MNAV, and an embedded computing platform, Stargate, which together provide a complete avionics package for aerial robotic applications. In order to provide a complete INS solution (i.e., attitude, velocity, position, and biases), an extended Kalman filter algorithm is developed and implemented in real-time. A devised control strategy utilizes multiple PID loops with a hierarchy enabling simple attitude stabilization to full waypoint navigation. The developed ground station unit, a laptop computer, communicates with the avionics package via 802.11b WiFi, displays the aircraft critical information, provides in-flight PID gain tunings, and uploads waypoints through a simple GUI. The system is installed in an off-the-shelf delta-wing R/C aircraft and demonstrates its performance for aerial robotic applications

Robust real-time tracking by fusing measurements from inertial and vision sensors

Abstract: The problem of estimating and predicting position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and vision. The sensor fusion approach described in this contribution is based on non-linear filtering of these complementary sensors. This way, accurate and robust pose estimates are available for the primary purpose of augmented reality applications, but with the secondary effect of reducing computation time and improving the performance in vision processing. A real-time implementation of a multi-rate extended Kalman filter is described, using a dynamic model with 22 states, where 12.5 Hz correspondences from vision and 100 Hz inertial measurements are processed. An example where an industrial robot is used to move the sensor unit is presented. The advantage with this configuration is that it provides ground truth for the pose, allowing for objective performance evaluation. The results show that we obtain an absolute accuracy of 2 cm in position and 1° in orientation.

Design Through Operation of an Image-Based Velocity Estimation System for Mars Landing

Abstract: During the Mars Exploration Rover (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combined measurements from a descent camera, a radar altimeter, and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm used altitude and attitude measurements to rectify images to a level ground plane. Feature selection and tracking were employed in the rectified images to compute the horizontal motion between images. Differences of consecutive motion estimates were then compared to inertial measurements to verify correct feature tracking. DIMES combined sensor data from multiple sources in a novel way to create a low-cost, robust, and computationally efficient velocity estimation solution, and DIMES was the first robotics vision system used to control a spacecraft during planetary landing. This paper presents the design and implementation of the DIMES algorithm, the assessment of the algorithm performance using a high fidelity Monte Carlo simulation, validation of performance using field test data and the detailed results from the two landings on Mars. DIMES was used successfully during both MER landings. In the case of Spirit, had DIMES not been used onboard, the total velocity would have been at the limits of the airbag capability. Fortunately, DIMES computed the actual steady state horizontal velocity and it was used by the thruster firing logic to reduce the total velocity prior to landing. For Opportunity, DIMES computed the correct velocity, and the velocity was small enough that the lander performed no action to remove it.

Sensor Fault Detection for UAVs using a Nonlinear Dynamic Model and the IMM-UKF Algorithm

Abstract: This paper presents a method of Fault Detection, Identification and Accommodation for inertial sensors in Unmanned Aerial Vehicles. A nonlinear model of the aircraft's dynamics replace the traditional inertial navigation equations and is used in conjunction with the Interacting Multiple Model and the Unscented Kalman Filter for improving state estimation in presence of inertial sensor faults. Performance comparisons are made between filters using the inertial navigation equations and the dynamic model for the fault-free conditions. It is shown that a matched UKF will result in adequate state estimation regardless of the failure mode and that the IMM-UKF algorithm is a step closer to achieving the same performance. The IMM-UKF is shown capable of maintaining stable state estimates in the presence of all single inertial sensor faults.

Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part I: A critical review

Abstract: This paper (the first part of two to be published in this journal) presents the process and results of a critical review of the integrated GPS and inertial navigation system (INS) architectures, the corresponding failure modes and the existing integrity monitoring methods. The paper concludes that tightly coupled GPS/INS systems have the highest potential for detecting slowly growing errors (SGEs). This is due to access to pseudorange measurements and a relatively simpler configuration compared to the other architectures. The second paper (Part II) takes this further and carries out a detailed characterisation of the performance of the current integrity algorithms for tightly coupled systems and develops a new algorithm that detects SGEs faster than the current methods.