Showing papers on "GPS/INS published in 2008"

A complementary filter for attitude estimation of a fixed-wing UAV

Abstract: This paper considers the question of using a nonlinear complementary filter for attitude estimation of fixed-wing unmanned aerial vehicle (UAV) given only measurements from a low-cost inertial measurement unit. A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation. The raw accelerometer output includes a component corresponding to airframe acceleration, occurring primarily when the aircraft turns, as well as the gravitational acceleration that is required for the filter. The airframe acceleration is estimated using a simple centripetal force model (based on additional airspeed measurements), augmented by a first order dynamic model for angle-of-attack, and used to obtain estimates of the gravitational direction independent of the airplane manoeuvres. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/INS that was available for the data set.

Foot mounted inertial system for pedestrian navigation

Abstract: This paper discusses algorithmic concepts, design and testing of a system based on a low-cost MEMS-based inertial measurement unit (IMU) and high-sensitivity global positioning system (HSGPS) receivers for seamless personal navigation in a GPS signal degraded environment. The system developed here is mounted on a pedestrian shoe/foot and uses measurements based on the dynamics experienced by the inertial sensors on the user's foot. The IMU measurements are processed through a conventional inertial navigation system (INS) algorithm and are then integrated with HSGPS receiver measurements and dynamics derived constraint measurements using a tightly coupled integration strategy. The ability of INS to bridge the navigation solution is evaluated through field tests conducted indoors and in severely signal degraded forest environments. The specific focus is on evaluating system performance under challenging GPS conditions.

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

Abstract: Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

Autonomous vehicle based in cooperative gps and inertial systems

Abstract: A system including Global Positioning Systems (GPS) and digital cartography is a good solution to carry out vehicle's guidance. However, it has inconveniences like high sensibility to multipath and interference when the GPS signal is blocked by external agents. Another system is mandatory to avoid this error. This paper presents a cooperative system based on GPS and Inertial Navigation Systems (INS) for automated vehicle position. The control system includes a decision unit to choose which value is the correct. In case GPS is working at top precision, it takes the control. On the other part, GPS signal can be lost and inertial control system guides the car in this occasion. A third possibility is contemplated: we receive the signal from GPS but the accuracy is over one meter. Now, position value is obtained by means of both systems. Experimental results analyze two situations: guidance in an urban area where GPS signal can be occluded by buildings or trees during short time intervals and the possibility of loss of the signal in long time to simulate the circulation in tunnels. Good results have been observed in tests and it demonstrates how a cooperative system improves the automated vehicle guidance.

An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications

Abstract: The Kalman filter (KF) has been implemented as the primary integration scheme of the global positioning system (GPS) and inertial navigation systems (INS) for many land vehicle navigation and positioning applications. However, it has been reported that KF-based techniques have certain limitations, which reflect on the position error accumulation during GPS signal outages. Therefore, this article exploits the idea of incorporating artificial neural networks to develop an alternative INS/GPS integration scheme, the intelligent navigator, for next generation land vehicle navigation and positioning applications. Real land vehicle test results demonstrated the capability of using stored navigation knowledge to provide real-time reliable positioning information for stand-alone INS-based navigation for up to 20min with errors less than 16m (as compared to 2.6km in the case of the KF). For relatively short GPS outages, the KF was superior to the intelligent navigator for up to 30s outages. In contrast, the intelligent navigator was superior to the KF when the length of GPS outages was extended to 90s. The average improvement of the intelligent navigator reached 60% in the latter scenario. The results presented in this article strongly indicate the potential of including the intelligent navigator as the core algorithm for INS/GPS integrated land vehicle navigation systems.

An integrated reduced inertial sensor system — RISS / GPS for land vehicle

Abstract: This paper demonstrates a low cost navigation solution that can efficiently work, in real-time, in denied GPS environment. It explores a reduced inertial sensor system (RISS) involving single-axis gyroscope and two-axis accelerometers together with a speed sensor to provide full navigation solution in denied GPS environments. With the assumption that the vehicle mostly stay in the horizontal plane, the vehicle speed obtained from the speed sensor are used together with the heading information obtained from the gyroscope to determine the velocities along the East and North directions. Consequently, the vehiclespsila longitude and latitude are determined. The position and velocity errors are estimated by Kalman filter (KF) relying on RISS dynamic error model and GPS position and velocity updates. The two accelerometers pointing towards the forward and transverse directions are used together with a reliable gravity model to determine the pitch and roll angles. This paper analyzes and discusses the merits and limitations of the proposed RISS system and its integration with GPS. The performance of the proposed method is examined by conducting road test experiment in a land vehicle.

Observability Measures and Their Application to GPS/INS

Abstract: In this paper, two observability measures are introduced for a discrete linear system. The degrees of observability of both the system and its subspaces can be examined with these measures. The measures are well conditioned to perturbation and applicable to multi-input/multi-output time-varying systems. The relations among observability, observability measures, error covariance, and the information matrix are presented. It is shown that the measures have direct connections with the singular value decomposition of the information matrix. In contrast to the error covariance, the measures are determined by the system model and independent of the initial error covariance. An example of the observability analysis of the Global Positioning System/inertial navigation system is given. The measures are confirmed to be less sensitive to the system model perturbation. It is also shown that the vertical component of the gyro bias can be considered unobservable with a tactical-grade inertial measurement unit for a horizontal constant-speed motion.

Time Synchronization Error and Calibration in Integrated GPS/INS Systems

Abstract: The necessity for the precise time synchronization of measurement data from multiple sensors is widely recognized in the field of global positioning system/inertial navigation system (GPS/INS) integration. Having precise time synchronization is critical for achieving high data fusion performance. The limitations and advantages of various time synchronization scenarios and existing solutions are investigated in this paper. A criterion for evaluating synchronization accuracy requirements is derived on the basis of a comparison of the Kalman filter innovation series and the platform dynamics. An innovative time synchronization solution using a counter and two latching registers is proposed. The proposed solution has been implemented with off-the-shelf components and tested. The resolution and accuracy analysis shows that the proposed solution can achieve a time synchronization accuracy of 0.1 ms if INS can provide a hard-wired timing signal. A synchronization accuracy of 2 ms was achieved when the test system was used to synchronize a low-grade micro-electromechanical inertial measurement unit (IMU), which has only an RS-232 data output interface.

Adaptive wavelet transform based on cross-validation method and its application to GPS multipath mitigation

Abstract: Global positioning system (GPS) multipath disturbance is a bottleneck problem that limits the accuracy of precise GPS positioning applications. A method based on the technique of cross-validation for automatically identifying wavelet signal layers is developed and used for separating noise from signals in data series, and applied to mitigate GPS multipath effects. Experiments with both simulated data series and real GPS observations show that the method is a powerful signal decomposer, which can successfully separate noise from signals as long as the noise level is lower than about half of the magnitude of the signals. A multipath correction model is derived based on the proposed method and the sidereal day-to-day repeating property of GPS multipath signals to remove multipath effects on GPS observations and to improve the quality of the GPS measurements.

A Nonlinear GPS/IMU based observer for rigid body attitude and position estimation

Abstract: This work proposes a position and attitude nonlinear observer based on inertial measurements and GPS pseudorange readings. The observation problem is formulated on SE(3), and the solution yields exponential convergence of the attitude and position estimates. The GPS pseudorange measurements and inertial sensor readings are exploited directly in the observer, and the integration of vector readings in the observer is discussed. The proposed observer dynamics compensate for the bias in the angular velocity sensor and the clock offset in GPS pseudorange measurements. The stability of the position and velocity estimates in the presence of bounded accelerometer noise is also analyzed. The properties of the GPS/IMU based observer are illustrated in simulation for a rigid body describing a challenging trajectory.

Vehicle localization in outdoor woodland environments with sensor fault detection

Abstract: This paper describes a 2D localization method for a differential drive mobile vehicle on real forested paths. The mobile vehicle is equipped with two rotary encoders, Crossbow's NAV420CA inertial measurement unit (IMU) and a NAVCOM SF-2050M GPS receiver (used in StarFire-DGPS dual mode). Loosely-coupled multisensor fusion and sensor fault detection issues are discussed as well. An extended Kalman filter (EKF) is used for sensor fusion estimation where a GPS noise pre-filter is used to avoid introducing biased GPS data (affected by multi-path). Normalized innovation squared (NIS) tests are performed when a GPS measurement is incorporated to reject GPS data outliers and keep the consistency of the filter. Finally, experimental results show the performance of the localization system compared to a previously measured ground truth.

Particle filtering for map-aided localization in sparse GPS environments

Abstract: This study presents the PosteriorPose algorithm, a Bayesian particle filtering approach for augmenting GPS and inertial navigation solutions with vision-based measurements of nearby lanes and stoplines referenced against a known map of environmental features. These relative measurements are shown to improve the quality of the navigation solution when GPS is available, and they are shown to keep the navigation solution converged in extended GPS blackouts. Measurements are incorporated with careful hypothesis testing and error modeling to account for non-Gaussian errors committed by vision-based detection algorithms. The PosteriorPose algorithm is implemented and validated in real-time on Cornell University's 2007 DARPA Urban Challenge entry; experimental data is presented showing the algorithm outperforming a tightly- coupled GPS/inertial navigation solution both in full GPS coverage and in an extended GPS blackout.

Methods and Apparatuses For GPS Coordinates Extrapolation When GPS Signals Are Not Available

Abstract: Systems and methods for extrapolating GPS coordinates beyond line of sight are disclosed. A coordinate extrapolation system (CES) can include a memory, a processor, and a GPS receiver. The CES can receive GPS signals and determine GPS coordinates corresponding to a location. If GPS signals are unavailable, the CES models the surface of the earth and extrapolates the GPS coordinates corresponding to the location at which GPS signals are unavailable. Methods for extrapolating the GPS coordinates and calibrating the CES are also disclosed.

On the capability of high sensitivity GPS for precise indoor positioning

Abstract: High-sensitivity GPS receivers allow to track very weak GPS signals (-180 dBW or even below) that are due to strong signal attenuation by e.g. foliage or constructive materials. Consequently, they outperform classical geodetic receivers of more than 30 dB and enable the signal tracking and positioning even indoors. This increase of GPS availability opens up new applications such as seamless outdoor-indoor positioning and navigation. However the positioning accuracy is often poor. In this paper, we use an exemplary data set from our GPS indoor test network to analyse the capability of high sensitivity GPS receivers for precise indoor positioning. A general study of the performance parameters (e.g. DOP factors, C/No-values) gives a first impression of the improved availability. The detailed analysis of the observed-computed values of the pseudo-ranges quantify the indoor delay which can reach up to 100 m. Considering the positioning solution, it is shown that a C/N0- based signal weighting can improve the accuracy of about 70 % for the mean value over 40 min observation time and up to 40% for the scatter of the coordinate time series.

Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation

Abstract: This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with nonlinear dynamic process modeling for Global positioning system (GPS) navigation processing. Many estimation problems, including the GPS navigation, are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model, however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKF is a nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state distribution through the nonlinear dynamics of system. The UKF exhibits superior performance when compared with EKF since the series approximations in the EKF algorithm can lead to poor representations of the nonlinear functions and probability distributions of interest. GPS navigation processing using the proposed approach will be conducted to validate the effectiveness of the proposed strategy. The performance of the UKF with nonlinear dynamic process model will be assessed and compared to those of conventional EKF.

Integration of INS and Un-Differenced GPS Measurements for Precise Position and Attitude Determination

Abstract: The integration of GPS and INS observations has been extensively investigated in recent years. Current systems are commonly based on the integration of INS data and the double differenced GPS measurements from two GPS receivers in which one is used as a reference receiver set up at a precisely surveyed control point and another is as the rover receiver whose position is to be determined. The requirement of a base receiver is to eliminate the significant GPS measurement errors related to GPS satellites, signal transmission and GPS receivers by double differencing measurements from the two receivers. With the advent of precise satellite orbit and clock products, the un-differenced GPS measurements from a single GPS receiver can be applied to output accurate position solutions at centimetre level using a positioning technology known as precise point positioning (PPP). This then opens an opportunity for the integration of un-differenced GPS measurements with INS for precise position and attitude determination. In this paper, a tightly coupled un-differenced GPS/INS system will be developed and described. The mathematical models for both INS and un-differenced GPS measurements will be introduced. The methods for mitigating GPS measurement errors will also be presented. A field test has been conducted and the results indicate that the integration of un-differenced GPS and INS observations can provide position and velocity solutions comparable with current double difference GPS/INS integration systems.

A Novel Architecture for Ultra-Tight HSGPS-INS Integration

Abstract: Global Positioning System (GPS) currently fulfills the positioning requirements of many applications under Line-Of-Sight (LOS) environments. However, many Location-Based Services (LBS) and navigation applications such as vehicular navigation and personal location require positioning capabilities in environments where LOS is not readily available, e.g., urban areas, indoors and dense forests. Such environments either block the signals completely or attenuate them to a power level that is 10-30 dB lower than the nominal signal power. This renders it impractical for a standard GPS receiver to acquire and maintain signal tracking, which causes discontinuous positioning in such environments. In order to address the issue of GPS tracking and positioning in degraded signal environments, a novel architecture for ultra-tight integration of a High Sensitivity GPS (HSGPS) receiver with an inertial navigation system (INS) is proposed herein. By enhancing receiver signal tracking loops through the use of optimal estimators and with external aiding, the capabilities of the receiver can be substantially improved. The proposed approach is distinct from the commonly used ultra-tightly coupled GPS/INS approaches and makes use of different tracking enhancement technologies used in typical HSGPS receivers, multichannel cooperated receivers and the current ultra-tightly coupled GPS/INS methods. Furthermore, the effects of inertial measurement unit (IMU) quality, receiver oscillator noise and coherent integration time on weak signal tracking are also analyzed. Simulated test results in both static and dynamic testes show that, the designed INS-aided GPS receiver can track the incoming weak GPS signals down to 15 dB-Hz without carrier phase locked, or 25 dB-Hz with carrier phase locked. When there are multiple strong GPS signals in view, the other weak signals can be tracked down to 15 dB-Hz with carrier phase locked.

Design of a Single Frequency GPS Software Receiver

Abstract: The GPS software recevier is more flexible than GPS hardware receiver.It upgrades the system perform- ance and has unexampled advantage becase it is prone to realize more complex arithmetic under special background. The paper places emphasis on a single frequency GPS software recevier based on PC.The GPS signal gets down- coverted to an intermediate frequency by the RF front-end.Then the IF is sampled and transmitted to the PC by the data collection beard.And all the data processing is done in PC,including signal acquisition,signal tracking and posi- tion calculations.The hardware that collects data and the algorithm of data processing are discussed in the paper.The system has been tested and the result indicates that the system works well and the precision is satisfactory.

Tightly-coupled GPS / INS system design for autonomous urban navigation

Abstract: This paper analyzes the design decisions made in building the tightly-coupled position, velocity, and attitude estimator used as a position feedback signal for autonomous navigation in Cornell University's 2007 DARPA urban challenge robot, 'Skynet.' A statistical sensitivity analysis is conducted on Skynet's estimator by examining the changes in its output as critical design decisions are reversed. The effects of five design decisions are considered: map aiding via computer vision algorithms, inclusion of differential corrections, filter integrity monitoring, WAAS augmentation, and inclusion of carrier phases. The effects of extensive signal blackouts are also considered. All estimator variants are scrutinized both in a statistical sense and in a practical sense, by comparing each variant's performance on logged data recorded at the 2007 DARPA urban challenge.

Improved Vehicle Navigation Using Aiding with Tightly Coupled Integration

Abstract: Vehicle navigation poses difficulties as it requires the uninterrupted availability of accurate positioning information, even in circumstances without an ideal condition. Global Positioning System (GPS) provides consistently accurate positioning solutions if four or more GPS satellites can be observed. Unfortunately, this condition is usually not satisfied if a vehicle is going through urban canyon, tunnel or forest canopy. Even with High Sensitivity and Assisted GPS receivers, reliable positioning using GPS alone in difficult urban situations is still a challenge. Inertial Navigation System (INS), consists of self- contained sensors that can continuously provide accurate short term positioning solutions. The integration of GPS and INS can overcome the GPS drawback and provide continuous navigation solutions even during GPS signal outages. Though newly developed MEMS-based INS sensors have relatively low accuracy, they are compact and inexpensive, which is very suitable for vehicle navigation. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. This paper presents the integration of GPS with MEMS-based INS in a tightly coupled scheme. Tightly coupled integration can make use of GPS signals even if less than four GPS satellites are observed. Thus it offers better integration options. To further improve the GPS/INS integration results, non- holonomic constraints and heading observations were used in this study to improve the online positioning accuracies. The results showed the drift errors could be significantly reduced when non- holonomic constraints and/or heading information were used, during periods with GPS signal outage. In addition, a backward smoother called Rauch-Tung-Striebel (RTS) was also implemented for offline processing needs purpose. The integration results showed that the RTS smoother can significantly reduce the drift errors even if neither non-holonomic constraints nor heading information were used.

Utilizing multipath reflections in deeply integrated GPS/INS architecture for navigation in urban environments

Abstract: This paper investigates the constructive use of multipath reflections of Global Positioning System (GPS) signals for navigation in urban environments. Urban navigation applications are generally characterized by a significant presence of multipath signals. In order to maintain reliable and accurate navigation capabilities, it is critical to distinguish between direct signal and multipath. At the same time, multipath reflections can be exploited as additional measurements for those cases where the number of direct path satellites is insufficient to compute the navigation solution. The paper develops a method for the identification of multipath reflections in received satellite signals: i.e. multipath is separated from direct signal and a line-of-site between the GPS receiver and a multipath reflecting object is determined. Once multipath reflections are identified, they can be used constructively for navigation. The method presented in the paper exploits an open loop batch-processing GPS receiver, laser scanner and inertial navigation system (INS) to identify multipath reflections in received satellite signals. Experimental GPS, inertial and laser scanner data collected in real urban environments are applied to demonstrate identification of multipath reflections.

Autonomous orbit propagation system and method

Abstract: A method of predicting a location of a satellite is provided wherein the GPS device, based on previously received information about the position of a satellite, such as an ephemeris, generates a correction acceleration of the satellite that can be used to predict the position of the satellite outside of the time frame in which the previously received information was valid. The calculations can be performed entirely on the GPS device, and do not require assistance from a server. However, if assistance from a server is available to the GPS device, the assistance information can be used to increase the accuracy of the predicted position.

An accurate localization for mobile robot using extended Kalman filter and sensor fusion

Abstract: This paper presents an accurate localization scheme for mobile robots based on the fusion of an ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve an accuracy of less than 100 mm. The INS consists of a yaw gyro and two wheel-encoders, and the U-SAT consists of four transmitters and a receiver. Besides the proposed localization method, we will fuse these in an extended Kalman filter. The performance of the localization was verified by simulation and two actual data sets (straight and curve) gathered from about 0.5 m/s of actual driving data. The localization methods used were general sensor fusion and sensor fusion through a Kalman filter using data from the INS. Through simulation and actual data analysis, the experiment shows the effectiveness of the proposed method for autonomous mobile robots.

Comparison of low-cost GPS/INS sensors for Autonomous Vehicle applications

Abstract: Autonomous Vehicle applications (Unmanned Ground Vehicles, Micro-Air Vehicles, UAVpsilas, and Marine Surface Vehicles) all require accurate position and attitude to be effective. Commercial units range in both cost and accuracy, as well as power, size, and weight. With the advent of low-cost blended GPS/INS solutions, several new options are available to accomplish the positioning task. In this work, we experimentally compare three commercially available, off-the-shelf units insitu, in terms of both position, and attitude. The compared units are a Microbotics MIDG-II, a Tokimec VSAS-2GM, along with a KVH Fiber Optic Gyro. The position truth measure is from a Trimble Ag122 DGPS receiver, and the attitude truth is from the KVH in yaw. Care is taken to make sure that all measurements are taken simultaneously, and that the sensors are all mounted rigidly to the vehicle chassis. A series of measurement trials are performed, including light driving on coastal roads and highway speeds, static bench testing, and flight data taken in a light aircraft both flying up the coast as well as aggressively maneuvering. Allan Variance analysis performed on all of the sensors, and their noise characteristics are compared directly. A table is included with the final consistent models for these sensors, and a methodology for creating such models for any additional sensors as they are made available. The Microbotics MIDG-II demonstrates performance that is superior to the Tokimec VSAS-2GM, both in terms of raw positioning data, as well as attitude data. While both perform quite well during flight, the MIDG is much better during driving tests. This is due to the MIDG internal tightly-coupled architecture, which is able to better fuse the GPS information with the noisy inertial sensor measurements.

Performance of a deeply coupled commercial grade GPS/INS system from KVH and NovAtel Inc.

Abstract: NovAtel Inc. and KVH Industries have jointly developed a commercial grade, single enclosure GPS/INS system. The integrated KVH CG-5100 IMU features fiber-optic gyros and MEMs accelerometers, and provides inertial data at 100 Hz. NovAtelpsilas OEMV3 receiver is the GPS engine. Weighing 5.2 lbs, the combined system will feature the tightly coupled architecture that is a key characteristic of NovAtelpsilas SPAN (Synchronized Position Attitude Navigation) technology. The GPS receiver provides aiding information for the INS, and is reciprocally aided by feedback from the INS to improve signal tracking. The feedback from the INS to the GPS engine is the deeply coupled aspect of the system. It is also tightly coupled. GPS measurements are used to update the INS filter, providing high quality aiding information whenever there are least two satellites available. The combined system has an optional wheel sensor, which is used to further aid the INS during times of reduced GPS availability in land vehicle applications.

An intelligent and autonomous MEMS IMU/GPS integration scheme for low cost land navigation applications

Abstract: An intelligent scheme to integrate inertial navigation system/global positioning system (GPS) is proposed using a constructive neural network (CNN) to overcome the limitations of current schemes, namely Kalman filtering (KF). The proposed CNN technique does not require prior knowledge or empirical trials to implement the proposed architecture since it is able to construct its architecture “on the fly,” based on the complexity of the vehicle dynamic variations. The proposed scheme is implemented and tested using Micro-electro-mechanical systems inertial measurement unit data collected in a land-vehicle environment. The performance of the proposed scheme is then compared with the multi-layer feed-forward neural networks (MFNN) and KF- based schemes in terms of positioning accuracy during GPS signal outages. The results are then analyzed and discussed in terms of positioning accuracy and learning time. The preliminary results presented in this article indicate that the positioning accuracy were improved by more than 55% when the MFNN and CNN-based schemes were implemented. In addition, the proposed CNN was able to construct the topology by itself autonomously on the fly and achieve similar prediction performance with less hidden neurons compared to MFNN-based schemes.

Tightly-Coupled INS, GPS, and Imaging Sensors for Precision Geolocation

Abstract: : Recent technological advances have significantly improved the capabilities of micro-air vehicles (MAV). This is evidenced by their expanding use by government, police, and military forces. A MAV can provide a real-time surveillance capability to even the smallest units, which provides commanders with a significant advantage. This capability is a result of the availability of miniaturized autopilot systems which typically combine inertial, pitot-static, and GPS sensors into a feedback flight-control system. While these autopilots can provide an autonomous flight capability, they have some limitations which impact their operational effectiveness. One of the primary issues is poor image geolocation performance, which limits the use of these systems for direct measurements of target locations. This poor geolocation performance is primarily a consequence of the relatively large attitude errors characteristic of low-performance inertial sensors. In previous efforts, we have developed a tightly-coupled image-aided inertial navigation system to operate in areas not serviced by GPS. This system extracts navigation information by automatically detecting and tracking stationary optical features of opportunity in the environment. One characteristic of this system is vastly reduced attitude errors, even with consumer-grade inertial sensors. In this paper, the benefits of incorporating image-based navigation techniques with inertial and GPS measurements is explored. After properly integrating GPS with the image-aided inertial architecture, the system is tested using a combination of Monte-Carlo simulation and flight test data. The flight test data was flown over Edwards AFB using representative hardware. The experimental results are compared with validated truth data.