Updating inertial navigation systems with VOR/DME information

Diver and submersible navigation using acoustic techniques in and around complex underwater structures such as drilling and production platforms is complicated by the effects of multipath acoustic propagation. The research described in this paper is concerned with the investigation of techniques to overcome such effects, thereby leading to navigation systems which yield accurate locations of divers and submersibles operating within the core of the underwater structure. Several strategies are being studied including fixed transponder systems placed at prescribed locations in the structure and systems with tracking pencil-beams. The paper describes a computer simulation of a representative underwater structure which was developed for the study of the interaction between structural members and propagating acoustic beams generated by transponders mounted on and around the structure. It evaluates the detection probability of a diver or submersible operating within the structure as a function of acoustic source coordinates and yields information on optimal placement of acoustic sources and gives additional insight into the nature of the multipath transmission effect. INTRODUCTION A requirement exists for systems which can facilitate accurate navigation and location of divers or RCVs within the vicinity of offshore structures to allow routine inspection and maintenance. 1,2 Currently available short base-line or long base-line transponder oriented navigational systems 3 cannot be directly employed as they rely entirely on line of sight communication Transponder navigation systems compute locations on the basis of time-elapse measurements which are only valid along straight line paths. Within an off-shore structure, with its multiple structural members, signals to and from omni-directional transponders will be heavily corrupted by multipath transmission effects due to multiple reflections from various members, as well as by additive acoustic noise interference which may be both random and impulsive in nature, and will lead to ambiguous computations of distance based purely on time elapse measurements. A system which can be considered as more reliable in the underwater environment of the offshore structure than an omni-directional transponder system is an Inertial Navigation System. However two factors militate against such a system being employed for diver/RCV support. The first is cost, the second is gyroscopic drift which requires frequent compensation, and is particularly critical in the Azimuth gyroscope. This paper presents details of research being conducted at Strathclyde University towards the development of an accurate and reliable acoustic navigation system which employs some novel techniques of analysis, signal processing, and simulation to overcome the disadvantages outlined above. Several different strategies for system configuration and diver location are being currently pursued. After a discussion of some of these strategies, which are based on the location of sensors, results on the optimisation of these locations for representative offshore structures will be covered, and an ensuing measure of performance probability of detection for varying numbers of sensors-will be developed. The work on optimisation of sensor location and the attendant detection probabilities is extremely important as it leads to a reliable and robust system configuration for any specified offshore structure.

Suppression of the G-sensitive drift of laser gyro in dual-axis rotational inertial navigation system

Gyro drift is an important technical index that affects the position precision of vehicle Fiber-Optic Gyroscope inertial navigation system. According to the characteristics of the vehicle strapdown inertial navigation system(SINS) and full observability of the North gyro drift, a two position gyro drift online calibration method is proposed under the approximate horizontal condition. The actual value of gyro drift is calculated by estimating the equivalent North gyro drift of two different positions. This method does not need to dismantle complex SINS equipment, and the system accuracy can meet the requirements of vehicle navigation.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

Precise navigation is crucial for the long range unmanned aerial vehicles (UAVs). But today’s UAVs still rely on global navigation satellite system (GNSS) for accurate navigation; this brings in the safety issues as the weak satellite signals can be interfered. To achieve accurate autonomous navigation, we propose to utilize publicly available remote sensing maps to compensate the drifts from the inertial navigation systems. To deal with severe visual variations between the camera images and the map image, we propose a hierarchical outlier filtering strategy to filter out the feature correspondence with a voting algorithm at the first stage, filter out the wrong matched position with the predictions from the inertial navigation system at the second stage. Then an Extended Kalman Filter (EKF) is constructed to combine the matched position with the Inertial Navigation System (INS) to achieve accurate and drift free localization performance. To test the proposed algorithm, we have conducted long range real flight experiments with a CH-4 UAV and the overall localization error is less than 10 m.KeywordsImage matching locationIntegrated navigationLong range UAV

In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In...

A method is presented to synchronize and aid the absolute navigation solutions of the members of a swarm by means of relative inter-swarm measurements. Moreover, the operational benefits thereof are discussed. Collaborative operation of vehicles, especially of unmanned aerial vehicles in close formation, requires accurate relative and absolute navigation. A novel method for collaborative multi-sensors fusion of relative and absolute navigation information is presented designed to cope with GNSS-denied environment. Relative inter-swarm measurements and absolute navigation means cannot be fused straight away as they are defined in fundamental different frames. Moreover, the sensor fusion has to fulfill two objectives – firstly, restoring the relative geometry of the swarm, and secondly, determining the absolute positions of each vehicle in an optimal way for the overall swarm. Key for collaborative navigation is to establish a common knowledge within the swarm which is achieved by exchanging the absolute state estimations, the state uncertainties and the relative measurements. Based on the common knowledge the “Consistent INS Drift Algorithm” computes for each vehicle a new position which is statistically optimal for the overall swarm. Simultaneously, these new positions represent the actual geometry of the swarm by essentially resampling the relative measurements. By fusing these new positions the navigation filters are synchronized and the relative measurements are implicitly used to aid the absolute navigation solutions. This reveals several operational benefits: the absolute navigation solutions are consistent – the whole swarm drifts consistently as only the Inertial Navigation Systems (INS) may remain as absolute navigation means, and hence, the naming “Consistent INS Drift Algorithm”. Moreover, the drift is statistically slower than the best individual INS. Having at least one vehicle equipped with a drift-free navigation mean such as Terrain Referenced Navigation stops the swarm to drift. Therefore, heterogeneous navigation systems are natively supported enabling true collaboration.

High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme.

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

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Generally, an autonomous under vehicle (AUV) is equipped with some navigation devices to know its own position and motion. One of them is an inertial navigation system (INS). The INS calculates its own absolute position and attitude using just internal sensors in real time. And the position accuracy depends strongly on precision of inertial sensors. In particular, the precision of optical gyroscopes strongly influences the position and attitude accuracy. And moreover, the precision depends on the size of them. In fact, it is necessary that the size of INS becomes large in order to improve the position accuracy. Based on that, Japan Agency for Marine-Earth Science and Technology (JAMSTEC) developed a compact and high-performance INS which can be installed easily into a small AUV and output accurate position and attitude. It was developed with homegrown technology which is caused by JAMSTEC's knowledge and the technical capabilities of a domestic manufactures. In this paper, its schema, specifications and functions are described.

Accurate and reliable navigation systems will become increasingly important in future aircraft applications, in particular within unmanned aerial vehicle systems. This paper describes a particle filter approach of integrating an Inertial navigation system (INS) with a terrain-aided positioning system (TAP) to achieve such a system. The integrated system is realized applying a marginalized particle filter (MPF) where the highly nonlinear TAP is designed tightly with the INS using one and the same filter. In order to better estimate the multi-modal errors in the altitude measurements, a first order Generalized Pseudo-Bayesian (GPB1) filter is used for this purpose. This will also reduce the number of particles in the MPF and therefore also reduce the computational workload. The performance of the algorithm has been evaluated using recorded flight data from the Saab Gripen fighter aircraft. Compared to an existing INS/TAP system based on a suboptimal integration of a point mass filter representing TAP and a single extended Kalman filter estimating the INS errors, the MPF approach is similar in performance but shows better results on convergence times when recovering after loss of data.

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

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