A Prototype Personal Navigation System

Personal navigation system(PNS) is mainly used for tracking and locating personnel walking real-time location which can be realized as underground exploration or indoor positioning when the GPS signal loses. MEMS inertial sensors are small size, low cost, low power consuming, mass-production capability and silicon-based sensors. Strap-down inertial navigation system can achieve its autonomous navigation capabilities by fixing the sensor on the user's shoes. Zero velocity updating (ZUPT) can correct the tracking of navigation algorithm. This paper proposes an adaptive algorithm of threshold value in ZUPT of PNS. The test results show that the adaptive algorithm can effectively correct the error of tracking. The tracking of navigation algorithm with zero velocity updating is very close to the real track of walker.

Due to the progress of portable computing devices such as PDAs, cellular phones and small sized PCs, many personal navigation systems have been developed which navigate their users to display routes to given destinations. Those navigation systems mainly focus on the guidance for personal use. In this paper, we have developed a group navigation system, which provides facilities for (1) personally customizable route navigation to a given destination, (2) management of group movement and (3) rehearsal usage when we make the personally customized route navigation. In our system, using wireless ad-hoc communication a few leaders of a group can collect and distribute the information about its members' current positions and give each member a suitable suggestion when the user is losing his/her way. The personalized route navigation scenario (program) running on portable devices can be obtained automatically only by clicking intersections sequentially on a given map and giving pictures and comments. A rehearsal mode is also prepared when we make the personalized route navigation.

Rapid development of IT area leads to increasing use of the personal digital assistant (PDA). In these days, many PDA products equipped with GPS receiver for PNS (personal navigation system) are on the market. Furthermore many robots would be equipped GPS receiver to navigate by itself in recent future. A common feature of PNS and robot navigation is slow moving, which often results in incorrect navigation. Also, there is much probability of blocking and reflecting by surrounding environment, which may also result in incorrect navigation. Therefore it is needed to pre-process the signals to adapt it to various applications. In this paper, an algorithm called GPS Buffering Algorithm (GBA) to pre-process the signals is proposed. For this, GPS data obtaining from uNAV GPS receiver are analyzed and classified based on dynamic characteristic. Then, the proposed algorithm is applied to the data and some test results are shown to verify the usefulness of the algorithm

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.

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.

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.

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

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.

Doctoral student experience and outcomes largely depend on the quality of supervision. Research on doctoral supervision usually focuses on the attributes, styles, and roles of a supervisor during doctoral training, while the process and effects of initiating supervisor-supervisee relationships remain less explored. This paper aims to examine how students' strategies for supervisor selection are related to subsequent difficulties in interaction with their supervisors and their confidence about their future dissertation defense. Based on a nationwide survey of doctoral students in Russia (N = 1796) and regression analysis, we demonstrate that academic criteria such as a supervisor's subject-matter expertise and successful supervision history are the best factors for students to consider to reduce the probability of future difficulties with the supervisor and to avoid experiencing a lack of confidence. Using an existing positive personal relationship with a supervisor as a criterion reduces the chances of interaction difficulties, but does not affect a student's confidence in future academic success. At the same time, the "only option" scenario (i.e., a situation when students have no alternatives to choose from) is associated with higher chances of facing difficulties during the doctoral journey and a lower level of student confidence. The study highlights the critical role of agency in supervisor selection, showing that structured support for informed decision-making - whether through institutional matchmaking systems or student mentoring programs - can mitigate risks of poor supervision outcomes. These findings call for policy interventions to promote transparency in supervisor selection processes, especially in contexts where students have limited choices. The results of the research can be used by prospective doctoral students and doctoral program administrators to create a personal or institutional navigation system for choosing a supervisor.

Due to melting ice at the North Pole, navigation at high latitudes is increasing regularly. Therefore, navigation and survey at these high latitudes becomes more and more a key issue, especially for underwater operations where GNSS signals are not available. Navigation at high latitudes is a challenge for inertial navigation systems. Alignment of these systems with respect to North becomes more difficult when closer to the pole because the physical effect that is used becomes smaller and smaller when going to high latitudes. In order to evaluate heading accuracy and navigation performance of its inertial systems under these extreme conditions, iXBlue had the opportunity to test several of its navigation systems on a vessel up to 81 degrees of latitude during summer 2014. Thanks to the collaboration of the Norwegian Polar Institute, iXBlue has tested QUADRANS gyrocompass and PHINS navigation systems on the research Vessel Lance. The paper emphasizes on the potential difficulties encountered by inertial navigation systems at high latitudes. In a second part the paper presents and analyzes performance of all navigation systems and gyrocompasses under test in terms of heading accuracy and long term stability and navigation accuracy without GNSS aiding. The third part explains why we obtained extremely good results during this test campaign.

Nowadays, unmanned aerial vehicles (UAVs) play a vital role in many applications and have been successfully employed in a wide variety of applications such as road surveying, precision agriculture, landslide monitoring, cultural heritage mapping, and pipeline monitoring. All of these applications require accurate and stable navigation system. Currently, most of the commercially available UAVs rely on the integration of the Global Satellite Navigation Systems (GNSS) and Inertial Navigation Systems (INS) to estimate the position, velocity, and attitude. The small form factor of modern UAVs allowed them to operate in more challenging environments such as urban and natural canyons. In these environments, the GNSS availability cannot be guaranteed, and hence the navigation solution will deteriorate dramatically during these GNSS signal outages due to the drift exhibited by the inertial navigation solution. Using other aiding sensors is crucial to limit the accumulated errors associated with INS measurements. Onboard cameras can offer a useful clue to support the navigation solution during the GNSS signal outage periods. Varieties of monocular visual odometry based on photogrammetric and Structure from Motion (SfM) approaches have been proposed to assist the navigation estimation process. The main problem of using the monocular VO technique is the loss of scale if neither external measurement nor a priori knowledge about the surrounding environment are available. Moreover, the estimated camera pose from VO is prone to drift with time. This paper introduces a novel approach for estimating the navigation states of an Unmanned Aerial Vehicle (UAV) by integrating the visual information that obtained from a monocular camera with the Inertial Measurement Unit (IMU) observations via an Extended Kalman Filter (EKF). Most of the current monocular VO algorithms rely on a calibrated camera model and apply the conventional photogrammetric and SfM approaches. While these approaches can help towards estimating the relative rotation and translation by tracking image features and applying geometrical constraints, they cannot estimate the motion scale using only the image visual features.

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.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.