Graph Optimization

Wireless Sensors Networks are widely used to monitor the areas. In many WSN applications it is important to achieve a good coverage of the observed area while the energetic efficiency and the lifetime of the network are high. The task of placing the sensor nodes while addressing these objectives is known as WSN layout problem. As the optimal sensors network layout can be reformulated as a multiobjective optimization problem, the paper presents a possible use of GHEA technique for solving the considered problem.

Abstract. The paper proposes a XOR-based network coded cooperation protocol for the uplink transmission of relay assisted cellular networks and an algorithm for selection and assignment of the relay nodes. The performances of the cooperation protocol are ...

Non-functional properties evaluation in Service Oriented Architecture (SOA) is still mostly an open challenge. Although this is a problem that has been already partially explored with some success, there is lack of consolidated results for more complex SOA applications based on services composition. This paper presents a contribution to performance evaluation of SOA-based applications integrated by BPEL. The evaluation technique is based on a performance-oriented reinterpretation of the BPEL specification as a performance modeling language within a multiformalism framework. The approach is based on automatic translation of PerfBPEL into Markov chains and it is implemented by means of SIMTHESys modeling and analysis framework to enable the interaction with other performance oriented formalisms.

Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera viewpoints , lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, datadriven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminativelytrained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.

System modeling is one of the important tasks to be solved during software development. As more complex software systems become as higher requirements are defined for demonstrative presentation of the system to be developed. To solve this task the main attention is devoted to the transparency of the model elements within the graphical presentation of the system. The paper defines the classification of different types of UML diagrams, which are created during development of the software system. This classification is based on the different combinations of nodes and arcs of the diagram graph. The UML class diagram is selected for deeper analysis to the elements' layout. Authors offer to use main principles of the genetic algorithm to automate the replacement of the diagram created in the manual way. Current results are quite theoretical yet and authors will continue the research based on the issues defined in this paper. Galapovs A. and Nikiforova O.. Several Issues on the Definition of Algorithm for the Layout of the UML Class Diagrams.

The extended Kalman filter (EKF) is a widely used algorithm for nonlinear estimation of Inertial Navigation Systems (INS) and the Global Position System (GPS) integration. However, EKF has several limitations, such as linearization dependency, and the model error statistics are assumed as a zero-mean Gaussian noise with known covariance. Consequently, if EKF is not tuned correctly, the INS error predictions can quickly diverge. To overcome the limitations of existing Kalman algorithms, this paper derives a real-time predictive approach. The proposed method increases the accuracy and the reliability requirements of loosely INS/GPS integration by estimating the unknown model errors of sensors without augmenting the state space. Also, considering the insufficiency of the researches on the integrated navigation in tangent (launch) frame, this research derives the navigation equations in tangent frame and its error model is analyzed. The estimation performance of the predictive approach is analyzed. The performance is verified using an experimental data acquired from a land-vehicle test. The results of predictive filter demonstrate superior performance to the traditional EKF. The test results of land-vehicle navigation validate the advantages of the presented method, which increases the position accuracy by an amount of 70% and it decreases the computational cost to 50% and improves the estimation performance for the integrated INS/GPS better than the traditional EKF. This test is a fundamental step to determine the capability of the filter for robotics and aerospace applications in future.

Multibody simulations are already used in many industries to speed up the development of new products. However, improvements in multibody formulations and the continuous increase of computational power open new fields of applications for the multibody simulations, such as using them as a basis for developing state observers. This work introduces novel state observers developed by combining a multibody model with some probabilistic estimators from the family of Kalman filters. Together with other multibody-based state observers already proposed in the literature, have been benchmarked by applying them to two mechanisms: a planar four-bar linkage, and a five-bar linkage. The accuracy of the estimations as well as the computational costs are examined under several scenarios: using encoders or gyroscopes as sensors, with different sampling rates for the sensors, and assuming different levels of modeling errors. The results obtained in this work can be employed to select a suitable state observer for a new application. All methods have been implemented as a reusable MATLAB toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab.

Novel Adaptive Unscented Kalman Filter (NAUKF) has been developed and applied to fuse the outputs of strap-down IMU, the measurements of GPS satellites (pseudo-range and Doppler), strap-down magnetometer and a barometric altimeter, using tight coupling architecture. The proposed filter NAUKF considers the residual unmodeled noises of process and measurement as non-zero mean Gaussian white noises, estimates and compensates for the mean (bias) and covariance of the noise online, based on the principles of adaptive filtering, even if they are time-varying quantities. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained and the NAUKF is obtained. The noise statistic estimators designed in the proposed algorithm are built on the basis of forgetting factors which are usually calculated empirically. In this research a new calculation concept based on "Genetic Algorithm" as an optimization tool is utilized to determine the optimal values of forgetting factors. The performance and convergence of noise statistic estimators used in NAUKF are checked by Monte-Carlo simulation. The utilization of NAUKF for Tightly-Coupled INS/GPS Integration (TCI) has shown a superiority against the UKF for TCI especially during the GPS outages. The comparison is done experimentally by means of real flight trip of an UAV.

In this paper a brief review on the observability and estimability analysis of GPS/INS is presented. There have been various analysis results on the observability of INS errors. However different INS error dynamics models and reference frames of INS mechanization have been used in the observability analysis. Moreover, the analysis framework was not unique. In this paper, known observability analysis results are summarized first. Then relatively general analysis tools to handle system model perturbation on the observability and estimability is given.

In the last years some infrastructures and frameworks have been proposed to enable the compositional development of multiformalism models. This effort requires proper techniques and tools to guarantee generality and flexibility when combining multiple solvers and results obtained from the analysis of such models. The OsMoSys/DrawNET framework allows to develop and analyze complex performability models which are composed by several submodels expressed by means of different formal languages. In this paper we describe the approach to multisolution of multiformalism models in the OsMoSys/DrawNET framework and we introduce the mechanisms used to define the performance indices and generate the required results. A raid system is used to exemplify the solution process of a performability model from its creation to the analysis and the presentation of the results to the final user.

4D or dynamic imaging of the thorax has many potential applications [1, 2]. CT and MRI offer sufficient speed to acquire motion information via 4D imaging. However they have different constraints and requirements. For both modalities both prospective and retrospective respiratory gating and tracking techniques have been developed [3, 4]. For pediatric imaging, x-ray radiation becomes a primary concern and MRI remains as the de facto choice. The pediatric subjects we deal with often suffer from extreme malformations of their chest wall, diaphragm, and/or spine, as such patient cooperation needed by some of the gating and tracking techniques are difficult to realize without causing patient discomfort. Moreover, we are interested in the mechanical function of their thorax in its natural form in tidal breathing. Therefore free-breathing MRI acquisition is the ideal modality of imaging for these patients. In our set up, for each coronal (or sagittal) slice position, slice images are acquired at a rate of about 200-300 ms/slice over several natural breathing cycles. This produces typically several thousands of slices which contain both the anatomic and dynamic information. However, it is not trivial to form a consistent and well defined 4D volume from these data. In this paper, we present a novel graph-based combinatorial optimization solution for constructing the best possible 4D scene from such data entirely in the digital domain. Our proposed method is purely image-based and does not need breath holding or any external surrogates or instruments to record respiratory motion or tidal volume. Both adult and children patients' data are used to illustrate the performance of the proposed method. Experimental results show that the reconstructed 4D scenes are smooth and consistent spatially and temporally, agreeing with known shape and motion of the lungs.

An accurate orientation is crucial to a satisfactory position in pedestrian navigation. The orientation estimation, however, is greatly affected by errors like the biases of gyroscopes. In order to minimize the error in the orientation, the biases of gyroscopes must be estimated and subtracted. In the state of the art it has been proposed, but not proved, that the estimation of the biases can be accomplished using magnetic field measurements. The objective of this work is to evaluate the effectiveness of using magnetic field measurements to estimate the biases of medium-cost micro-electromechanical sensors (MEMS) gyroscopes. We carry out the evaluation with experiments that cover both, quasi-error-free turn rate and magnetic measurements and medium-cost MEMS turn rate and magnetic measurements. The impact of different homogeneous magnetic field distributions and magnetically perturbed environments is analyzed. Additionally, the effect of the successful biases subtraction on the orientation and the estimated trajectory is detailed. Our results show that the use of magnetic field measurements is beneficial to the correct biases estimation. Further, we show that different magnetic field distributions affect differently the biases estimation process. Moreover, the biases are likewise correctly estimated under perturbed magnetic fields. However, for indoor and urban scenarios the biases estimation process is very slow.

Cooperative approaches are becoming of great interest in automotive research. One of the most important ITS applications is cooperative localization. In this paper, the concept of a dynamic base station DGPS (DDGPS) and its application in the vehicular cooperative localization is introduced and discussed. The DDGPS is a decentralized cooperative method which aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated, based on the receiver's belief on its positioning and its uncertainty, and then broadcasted to other GPS receivers. A new method for fusing all the received corrections from different sources is proposed and the data dependency problem is also discussed.

This work illustrates an educational project flow of an electronic system. This system is developed to support applications in which there are the need to measure motion parameters and transmit them to a remote unit for real-time teleprocessing. In order to be useful in many operative contexts, the system is flexible, compact, and lightweight. It integrates a tri-axial inertial sensors, a GPS module, a wireless transceiver and can drive a pocket camera. Data acquisition and packetization are handled in order to increase data throughput on radio bridge and to minimize power consumption. A trajectory reconstruction algorithm, implementing the Kalman-filter technique, allows to obtain real-time body tracking using only inertial sensors. Thanks to a graphical user interface it is possible to remotely control the system operations and to display the motion data. Following this detailed design procedure it is possible to reproduce this platform easily adapting it to your own aim.

In this paper we present an analysis of the positioning uncertainty increase rate for a group of mobile robots. The simplified version for a group of N robots moving along one dimension is considered. The one dimension restriction permits us to extract an exact expression for the accumulation of positioning uncertainty in a group of robots equipped with proprioceptive (odometric in this case) and exteroceptive (relative distance between robots) sensors. The solution obtained provides insight in the structure of the multirobot localization problem. In addition, it serves both as an approximation and a starting point for examining the more realistic case ofN robots moving on a plane. Our derivation is based on a Kalman filter estimator that combines all measurements from all robots in the group. Furthermore, we analyze the effect of initial uncertainty, number of robots (N ) and sensor noise on the rate of positioning uncertainty increase. The analytical results derived in this paper ...

The six degrees-of-freedom Stewart platform, or hexapod, is in widespread use in the flight simulation industry for the generation of motion cues that are representative of those experienced in actual flight. For closed-loop control of such motion platforms, but also to be able to objectively assess the quality of the generated simulator motion, accurate measurement of the kinematic state of the motion platform is required. In current practice, the inference of such knowledge relies mainly on the isolated use of actuator length measurements and on, in certain cases, on-platform inertial sensors. The purpose of the current work is to extend a previously proposed and conceptually superior method, based on a tightly-coupled fusion of measurements provided by these sensors using the Iterated Extended Kalman Filter (IEKF). Results from computer simulations indicate that the IEKF has difficulty in converging to the true system state of a six degrees-of-freedom Stewart platform. This is be...

In this paper, new attitude determination algorithms using pseudolite signal phase measurements are developed and presented with realistic simulations. Pseudolite signals are used to replace GPS signals which are often not available due to blocking by nearby large structures, for example a crew return vehicle under the International Space Station. A new observation model needs to be applied because spherical wavefronts are present for pseudolite signals, which are much closer to the antennas than GPS signals. Both static and filtering methods are developed. An iterative algorithm using the Levenberg-Marquardt method and a nonlinear Predictive filter provide a deterministic solution; however, they are not suitable for moving cases. Instead, the nonlinear Predictive filter, the extended Kalman filter and the Unscented filter using angular velocity measurements as well as pseudolite signal phase measurements are developed. In the convergence comparison, the Unscented filter and the Predictive filter show more robust behavior than that of the extended Kalman filter.

Measurement and estimation of motion variables is an important feature for autonomous navigation. Since the not-so-distant past, global positioning systems (GPS) and GPS-aided inertial navigation systems (INS) have been the main measurement tools for civil and military unmanned systems. A widely employed technique has been the extended Kalman filter (EKF) for the integration and filtering of GPS and INS data, which serves to remove sensor noises and biases, as well as estimating motion states between GPS updates. While many navigation systems depend almost entirely on the integrity and reliability of GPS data, persistent challenges in this technology are non-linearities and the need to maintain estimation accuracy in the absence of GPS, in order to ensure safe operation of unmanned platforms. Recent development in unmanned systems has also presented new challenges for measurement and estimation. One of these is that new designs tend toward the use of position-and attitude-only sensors such as gyro-less measurement units, magnetometers for geo-relative attitude measurement, vision-based systems for relative navigation, etc. The underlying reasons may be to overcome reliability problems with gyroscopes, cost reduction, or both. For the purpose of navigation and control, such measurement systems require online estimation of state variables that are unavailable or too costly to measure. For example, estimation of unmeasured velocities and angular rates presents a particularly challenging task, because higher sampling rates are desired but may lead to amplification of measurement noise. Different types of such sensors may also be used together, which calls for efficient and accurate sensor fusion techniques. In this special issue, three papers are devoted to the problem of filtering for low-cost measurement systems. First, Hong et al. present a simple filtering method to suppress short-term broadband noise in low-cost MEMS gyroscopes in mobile robots. At issue is the broadband gyro noise that compromises accuracy in yaw angle determination. Instead of Kalman filtering, Hong proposes a simple two-phase filtering method that consists of a threshold filter and moving-average filter,

This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications. A known mathematical calibration model has been adopted. The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude, heading, and tilt angles estimation that are of interest to geomatics applications. The evaluation method consists of two stages. First, the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field. Second, the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model. A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.

Magnetometers are sensors that can sense the earth magnetic field from which heading can be determined. Gyroscopes can provide the angular rate from which the heading can be calculated, they can be available in low-cost and light weight. Nevertheless, the problem of using gyroscopes as a sole source of heading is that gyroscopes readings are drifting with time in addition to the accumulated errors due to mathematical integration operation. Magnetometer is available in low-cost, it does not suffer from mathematical integration errors, and it can provide an absolute heading from magnetic north. Magnetometer readings are usually affected by magnetic fields, other than the earth's field, and by other error sources. Therefore magnetometer calibration is required to use magnetometer as a reliable source of heading. In this paper, two techniques are proposed for fast automatic 3D-space magnetometer calibration requiring small space coverage. There is no user involvement during calibration and there are no required specific movements. The proposed techniques perform 3D-space magnetometer calibration by calibrating the three magnetometer readings in the device frame which makes the magnetometer useful for determining heading in untethered devices, especially in pedestrian navigation. In which, portable navigation devices (such as smartphones) are always moving freely between the user's hands, belt, pocket, or placed on the ear during phone calls. The two proposed magnetometer calibration techniques are also capable of calibrating magnetometers in tethered devices, given that the platform to which the device is tethered is capable of performing 3D motion.

Using absolute navigation systems (such as global navigation satellite systems) has proved to be insufficient for indoor navigation or when navigating in urban canyons due to multipath and obstruction. This opened the gate widely for sensor-based navigation systems to be used, particularly after the development of low-cost microelectromechanical system sensors. Heading determination is one of the most important aspects for navigation solutions. Magnetometer is a low-cost sensor that can provide an absolute heading from magnetic north by sensing the Earth's magnetic field. Magnetometer readings are usually affected by magnetic fields, other than the Earth's magnetic field, and by other error sources; therefore, magnetometer calibration is required. In this paper, a technique is proposed for fast and automatic magnetometer calibration that requires small space coverage. There is no user involvement in the calibration process, and there are no required specific movements. The proposed technique performs 3-D-space magnetometer calibration using 2-D calibration equations with pitch and roll sectors. The 3-D-space is divided into a group of pitch and roll sectors. Inside each sector, 2-D calibration can be performed for the leveled magnetometer readings, which make the calibration process faster and requiring less data. This technique makes the magnetometer useful for determining heading in pseudo-tethered devices, particularly when used while driving. Pseudo-tethered navigation devices are tethered at normal operation, but they can change their orientation according to user needs such as portable vehicle navigation devices, which can be placed on the dashboard of a vehicle or attached to the wind shield.

This work illustrates an educational project flow of an electronic system. This system is developed to support applications in which there are the need to measure motion parameters and transmit them to a remote unit for real-time teleprocessing. In order to be useful in many operative contexts, the system is flexible, compact, and lightweight. It integrates a tri-axial inertial sensors, a GPS module, a wireless transceiver and can drive a pocket camera. Data acquisition and packetization are handled in order to increase data throughput on radio bridge and to minimize power consumption. A trajectory reconstruction algorithm, implementing the Kalman-filter technique, allows to obtain real-time body tracking using only inertial sensors. Thanks to a graphical user interface it is possible to remotely control the system operations and to display the motion data. Following this detailed design procedure it is possible to reproduce this platform easily adapting it to your own aim.

In INS/GPS integration system, Euler angle and quaternion are often used to represent attitude. In a tightly-coupled system, both the Euler angle-based and quaternion-based approaches have nonlinear system propagation and observation models. In this paper, the unscented Kalman filter is employed as the nonlinear filtering method in the Euler anglebased and quaternion-based approaches. Field experiments are conducted based on train rides. One trajectory involves frequent short GPS outage periods, and the other contains two long GPS outage periods. Numerical results show that both approaches can present robust and accurate estimation results. Nevertheless, the quaternion-based approach does not exhibit singularity problem, and it is more suitable to be used in the INS/GPS system with very low-cost inertial sensors.

In this paper, a new algorithm is developed for attitude estimation using Global Positioning System (GPS) signals. The new algorithm is based on a predictive filtering scheme designed for spacecraft without rate measuring devices. The major advantage of this new algorithm over traditional Kalman filter approaches is that the model error is not assumed to represented by an unbiased Gaussian noise process with known covariance, but instead is determined during the estimation process. This is achieved by simultaneously solving system optimality conditions and an output error constraint. This approach is well suited for GPS attitude estimation since some error sources that contribute to attitude inaccuracy, such as signal multipath, are known to be non-Gaussian processes. Also, the predictive filter scheme can use either GPS signals or vector observations or a combination of both for attitude estimation, so that performance characteristics can be maintained during periods of GPS attitude sensor outage. The performance of the new algorithm is tested using flight data from the REX-II spacecraft. Results are shown using the predictive filter to estimate the attitude from both GPS signals and magnetometer measurements, and comparing that solution to a magnetometer-only based solution. Results using the new estimation algorithm indicate that GPS-based solutions are verified to within 2 degrees using the magnetometer crosscheck for the REX-II spacecraft. GPS attitude accuracy of better than 1 degree is expected per axis, but cannot be reliably proven due to inaccuracies in the magnetic field model.

In this paper, new attitude determination algorithms using pseudolite signal phase measurements are developed and presented with realistic simulations. Pseudolite signals are used to replace GPS signals which are often not available due to blocking by nearby large structures, for example a crew return vehicle under the International Space Station. A new observation model needs to be applied because spherical wavefronts are present for pseudolite signals, which are much closer to the antennas than GPS signals. Both static and filtering methods are developed. An iterative algorithm using the Levenberg-Marquardt method and a nonlinear Predictive filter provide a deterministic solution; however, they are not suitable for moving cases. Instead, the nonlinear Predictive filter, the extended Kalman filter and the Unscented filter using angular velocity measurements as well as pseudolite signal phase measurements are developed. In the convergence comparison, the Unscented filter and the Predictive filter show more robust behavior than that of the extended Kalman filter.

In this paper, new attitude determination algorithms using pseudolite signal phase measurements are developed and presented with realistic simulations. Pseudolite signals are used to replace GPS signals which are often not available due to blocking by nearby large structures, for example a crew return vehicle under the International Space Station. A new observation model needs to be applied because spherical wavefronts are present for pseudolite signals, which are much closer to the antennas than GPS signals. Both static and filtering methods are developed. An iterative algorithm using the Levenberg-Marquardt method and a nonlinear Predictive filter provide a deterministic solution; however, they are not suitable for moving cases. Instead, the nonlinear Predictive filter, the extended Kalman filter and the Unscented filter using angular velocity measurements as well as pseudolite signal phase measurements are developed. In the convergence comparison, the Unscented filter and the Predictive filter show more robust behavior than that of the extended Kalman filter.

This article considers the application of Divided Difference Filter (DDF) to the orientation estimation, based on a quaternion-error continuous-discrete time model. DDF is a nonlinear estimator that in contrast to Taylor's expansion of extended Kalman Filter (EKF), exploit the polynomial approximations as a multivariable extension of Stirling's interpolation formula and require no derivatives. The DDF can be based on 1st and 2nd order Stirling's interpolation, which is named the divided difference filter-1st order (DDF1) and the divided difference filter-2nd order (DDF2). The orientation kinematics is defined in a quaternion vector space that unlike the Euler angle representation does not have any singularity problem. The presented nonlinear orientation model is an exact error model and is independent of the rigid body dynamics. The nonlinear process model includes six error-states in which only non-scalar elements of quaternion error vector are included in the error-state equations. The fourth element of quaternion error vector, which obeys unit norm constraint, is removed from system states to alleviate the estimated error covariance matrix divergence. The measurement system is a MARG sensor, which consists of a tri-axial rate gyro, a tri-axial accelerometer and a tri-axial magnetometer. The nonlinear measurement model is obtained based on the principals of magnetometer and accelerometer and the properties of the quaternion vector space. For the presented nonlinear orientation model, the performance of three filters namely DDF, EKF and Unscented Kalman Filter (UKF) is compared for different sampling frequencies in terms of the rms error, the captured area under the error norm curve, the estimated state variance and the computational cost. It is shown that under the same initial angle-error conditions, DDFs and UKF are more robust than EKF. The DDFs perform better than unscented Kalman filter (UKF) although the computational load for UKF is less. Among DDF1 and DDF2, DDF2's performance is slightly better but with more computation load. In the case of no initial angle-error conditions, the performance of the four filters is the same especially when the low noise level condition is considered.

Development of micro-electro mechanical system (MEMS) and micro-electro optical mechanical system (MEOMS) inertial sensors has been driven by the need for inexpensive sensing solutions in military and commercial applications. In addition to traditional attitude estimation and automobile applications, the reduced cost of MEMS/MEOMS inertial sensors has spurred new applications in personal navigation, pose estimation, audio visualization, cueing, etc. Electromechanical inertial sensors are also increasingly used in low cost attitude heading reference systems (AHRS) and backup attitude indicators, when the required accuracies are not too stringent. With the current performance levels of MEMS sensors, AHRS systems using electromechanical sensors rely on some form of external aiding to generate a better attitude solution. External aiding could come from an air data computer, global positioning system (GPS), etc., but the aiding comes at an increased cost. Aiding sources have their own set of errors and may not be available at all times. For example, air data sources suffer from problems such as icing, blocked pressure ports etc., and GPS integrity can be compromised due to interference. This dissertation addresses the problem of low cost attitude estimation using a triad of MEMS gyros and accelerometers for fixed wing and rotary wing aircrafts, under conditions when external aiding is unavailable or not useful. Using angular rate, acceleration and magnetic measures, a unit quaternion algorithm is formulated by combining a non linear attitude estimator with fuzzy logic concepts. Static and dynamic conditions of the aircraft are exploited to adaptively alter gains in correction loops used to correct input rate measures. Standard tests are simulated to assess the performance of the formulated algorithm. Real world flight data is used to compare the results of the proposed algorithm with an extended Kalman filter, and the error analysis is presented. It is shown that the fuzzy non linear estimator algorithm can be used to compute a reasonably accurate ii attitude solution using inexpensive MEMS sensors, even when external sources of aiding are unavailable.

Contemporary surveying in measurement-whiledrilling (MWD) processes incorporates measurements from three-axes accelerometers and magnetometers. Unfortunately, magnetometer-related problems limit the navigation performance of this technique. The introduction of fiber-optic-gyroscope (FOG)-based inertial navigation system (INS) in MWD aims at overcoming these limitations. However, drifts in the measurements provided by the INS might be prohibitive for the long-term utilization of this modern navigation-method downhole. One of the main obstacles precluding the elimination of these measurement drifts is the limited observability of the azimuth angle state provided by the INS. This paper explores the feasibility of utilizing a FOG-based tactical-grade inertial measurement unit (IMU) as a complete surveying sensor for a MWD processes downhole by implementing an innovative in-drilling alignment (IDA) procedure. During IDA, the IMU is exposed to controlled dynamics that excites azimuth-related states. This allows better and faster alignment that can reduce long-term navigation drifts, thus improving the overall accuracy in INS-based MWD processes. It is suggested that one take advantage of the longitudinal space available in the drilling-pipe system and impose controlled motion on the IMU to excite its states and increase its observability. Theoretical simulations and analytical approximations exploring the IDA idea have shown reduction in the steady-state azimuth-error variance and in the time required to achieve convergence with the increase of the acceleration-controlled motion. Several practical aspects of implementing this approach are evaluated and compared.

This paper presents a new method to determine the mass of an inactive space object from the fusion of photometric and astrometric data. Typically, the effect of solar radiation pressure is used to determine area-to-mass ratio for space objects from angles observations. The area-to-mass ratio of a space object can greatly affect its orbital dynamics. As a consequence, angles data are sensitive to this quantity. On the other hand, photometric data is not sensitive to mass but is a strong function of the albedo-area and the rotational dynamics of the space object. The albedo-area can be used to determine the amount of energy reflected from solar radiation. Since these two data types are sensitive to albedo-area and area-to-mass, then through fusion of photometric data with angles data it is possible to determine the area and mass of a space object. This work employs an unscented Kalman filter to estimate rotational and translational states, area and mass of an inactive space object. Mass is not observerable with only angles data or only photometric data alone, but it is shown in this work that with the two combined data types mass can be recovered. Recovery of space object characteristics and attitude and orbit trajectories with sufficient accuracy is demonstrated in this paper via simulation.

This work illustrates an educational project flow of an electronic system. This system is developed to support applications in which there are the need to measure motion parameters and transmit them to a remote unit for real-time teleprocessing. In order to be useful in many operative contexts, the system is flexible, compact, and lightweight. It integrates a tri-axial inertial sensors, a GPS module, a wireless transceiver and can drive a pocket camera. Data acquisition and packetization are handled in order to increase data throughput on radio bridge and to minimize power consumption. A trajectory reconstruction algorithm, implementing the Kalman-filter technique, allows to obtain real-time body tracking using only inertial sensors. Thanks to a graphical user interface it is possible to remotely control the system operations and to display the motion data. Following this detailed design procedure it is possible to reproduce this platform easily adapting it to your own aim.

Nonlinear system state estimation is a very common task. For non-linear system doesn't exist an optimal Bayesan estimator and so is necessary to use approximate solutions. The most common and used estimator in this case is the Extended Kalman Filter which approximates the non-linear system by a first order Taylor series. A more recent non linear filter is the Unscented Kalman Filter. In this article the two filters are compared by being applied to the estimation of the state of MHELI, a VTOL UAV developed by the Unmanned Technologies Research Institute S.p.A. equipped with an Inertial Measurement Unit and a GPS. Key words:

Many modern fusion architectures are designed to process and fuse data in networked systems. Alongside the advantages, such as scalability and robustness, distributed fusion techniques particularly have to tackle the problem of dependencies between locally processed data. In linear estimation problems, uncertain quantities with unknown cross-correlations can be fused by means of the covariance intersection algorithm, which avoids overconfident fusion results. However, for nonlinear system dynamics and sensor models perturbed by arbitrary noise, it is not only a problem to characterize and parameterize dependencies between estimates, but also to find a proper notion of consistency. This paper addresses these issues by transforming the state estimates to a different state space, where the corresponding densities are Gaussian and only linear dependencies between estimates, i.e., correlations, can arise. These pseudo Gaussian densities then allow the notion of covariance consistency to be used in distributed nonlinear state estimation.

Many modern fusion architectures are designed to process and fuse data in networked systems. Alongside the advantages, such as scalability and robustness, distributed fusion techniques particularly have to tackle the problem of dependencies between locally processed data. In linear estimation problems, uncertain quantities with unknown cross-correlations can be fused by means of the covariance intersection algorithm, which avoids overconfident fusion results. However, for nonlinear system dynamics and sensor models perturbed by arbitrary noise, it is not only a problem to characterize and parameterize dependencies between estimates, but also to find a proper notion of consistency. This paper addresses these issues by transforming the state estimates to a different state space, where the corresponding densities are Gaussian and only linear dependencies between estimates, i.e., correlations, can arise. These pseudo Gaussian densities then allow the notion of covariance consistency to be used in distributed nonlinear state estimation.

A novel concept for determining the full state of a projectile near the muzzle of the gun has been investigated. The methodology relies on the gun system inducing a known spatially varying magnetic field. Using readings from a cluster of magnetometers embedded within the projectile, the 12-DOF is determined by solving a nonlinear set of equations using Maple simulation programs. The estimated results of the model have a good agreement compared with the actual calculated data obtained from the magnetic field components of the sensor. The results of estimation are highly dependent upon the data burst period and the number of sensors. It was found that when the number of sensors increases, the final error decreases but errors due to nonlinearities start to set in if the data burst period is increased beyond a certain length. Finally, the model methodology works extremely well and the magnetometer sensor performance has been enhanced.

When multiple correlated data channels are simultaneously processed, multichannel-autoregressive (M-AR) processes may be combined with optimal filtering such as Kalman or H ∞ for prediction or estimation from noisy observations. However, the estimation of the M-AR parameters from noisy observations is a key issue to be addressed. Off-line or iterative approaches have been proposed recently, but their computational costs are high or some of them may diverge. Using on-line approaches such as EKF and SPKF is of interest, but the size of the state vector to be estimated is quite high. To reduce this size and the resulting computational costs, we suggest using dual optimal filters. In this paper, we study the relevance of cross-coupled Kalman filters and cross-coupled H ∞ filters. The comparative simulation study we carry out shows that our approach corresponds to a compromise between computational cost and performances in terms of pole estimation accuracy.

This work illustrates an educational project flow of an electronic system. This system is developed to support applications in which there are the need to measure motion parameters and transmit them to a remote unit for real-time teleprocessing. In order to be useful in many operative contexts, the system is flexible, compact, and lightweight. It integrates a tri-axial inertial sensors, a GPS module, a wireless transceiver and can drive a pocket camera. Data acquisition and packetization are handled in order to increase data throughput on radio bridge and to minimize power consumption. A trajectory reconstruction algorithm, implementing the Kalman-filter technique, allows to obtain real-time body tracking using only inertial sensors. Thanks to a graphical user interface it is possible to remotely control the system operations and to display the motion data. Following this detailed design procedure it is possible to reproduce this platform easily adapting it to your own aim.

ABSTRACT Starting with the birth of Web 2.0, the quantity of data managed by large-scale web services has grown exponentially, posing new challenges and infrastructure requirements. This has led to new programming paradigms and architectural choices, such as map-reduce and NoSQL databases, which constitute two of the main peculiarities of the specialized massively distributed systems referred to as Big Data architectures. The underlying computer infrastructures usually face complexity requirements, resulting from the need for efficiency and speed in computing over huge evolving data sets. This is achieved by taking advantage from the features of new technologies, such as the automatic scaling and replica provisioning of Cloud environments. Although performances are a key issue for the considered applications, few performance evaluation results are currently available in this field. In this work we focus on investigating how a Big Data application designer can evaluate the performances of applications exploiting the Apache Hive query language for NoSQL databases, built over a Apache Hadoop map-reduce infrastructure. This paper presents a dedicated modeling language and an application, showing first how it is possible to ease the modeling process and second how the semantic gap between modeling logic and the domain can be reduced, by means of vertical multiformalism modeling.

This work illustrates an educational project flow of an electronic system. This system is developed to support applications in which there are the need to measure motion parameters and transmit them to a remote unit for real-time teleprocessing. In order to be useful in many operative contexts, the system is flexible, compact, and lightweight. It integrates a tri-axial inertial sensors, a GPS module, a wireless transceiver and can drive a pocket camera. Data acquisition and packetization are handled in order to increase data throughput on radio bridge and to minimize power consumption. A trajectory reconstruction algorithm, implementing the Kalman-filter technique, allows to obtain real-time body tracking using only inertial sensors. Thanks to a graphical user interface it is possible to remotely control the system operations and to display the motion data. Following this detailed design procedure it is possible to reproduce this platform easily adapting it to your own aim.

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 for the airframe acceleration, that occurs primarily as 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.

Big Data applications represent an emerging field, which have proved to be crucial in business intelligence and in massive data management. Big Data promises to be the next big thing in the development of strategical computer applications, even if it requires considerable investment and an accurate resource planning, as the architectures needed to perform at the requisite speed need to scale easily on to a large number of computing nodes. Appropriate management of such architectures benefits from the availability of performance models, to allow developers and administrators to take informed decisions, saving time and experimental work. This paper presents a dedicated modeling language showing firstly how it is possible to ease the modeling process and secondly how the semantic gap between modeling logic and the domain can be reduced.

Progress of ICT is shifting the paradigm of systems organization towards a distributed approach, in which physical deployment of components influences the evaluation of systems properties. This contribution can be considered as a problem of graph layout optimization, well-known in literature where several approaches have been exploited in different application fields with different solving techniques. Then again, complex systems can be only studied by means of different formalisms which codification is the aim of language engineering. Telemaco is a tool that supports a novel approach for the application of graph layout optimizations to heterogeneous models, based on the OsMoSys framework and on the language engineering principles. It can cope with different graph-based formalisms by exploiting either their core graph nature or their different specialized features by means of language hierarchies. In this paper Telemaco is introduced together with its foundations and an example of application to Wireless Sensor Networks (WSN) deployment.

This paper deals with the problem of cooperative tracking using large groups of sensor nodes. A Kalman filter-like estimator is implemented and tested for this purpose. The focus of this paper is to examine the effect of the sensor density in the monitored area on the accuracy of the tracking results. The work is mainly motivated by the fact that the target position information issued by a sensor node necessarily passes across other sensors before reaching the final destination. The uncertainty in the relative localization process (between sensor nodes) therefore plays an important role in the accuracy of the final target position estimation. To address this issue, we investigate the improvement in tracking efficiency per additional sensor node as the coverage of the monitored region increases. Moreover, we give the expression of the upper bound on the target positioning uncertainty increase rate as a function of the sensor density, the intrinsic localization uncertainty for each sensor, and the accuracy of the relative sensor localization system. Thorough simulations have been conducted in order to validate the analytical results using concrete test cases.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5345383

A typical Inertial Navigation System (INS) fuses acceleration and angular rate readings with aiding measurements obtained by GPS and a compass. Here we present a robust state estimation framework based on the Extended Kalman Filter (EKF) applied to low-cost electronics typically installed on-board small unmanned airplanes. It uses airspeed measurements as a backup operation mode replacing GPS updates when temporarily unavailable. We demonstrate the applicability of the proposed approach to real-world scenarios using a challenging dataset recorded on-board a manned glider including long-term circling. A comparison between the normal operation mode and the backup solution reveals minimal difference between the respective orientation estimates, a position error growth sub-linear with time during GPS outage and a seamless transition back to GPS-based operation.

The solving of Vehicle Routing Problem (VRP) is a difficult combinatorial optimisation task. The interactive programming environment Venera and programming language Mars, oriented to ease the build up of constructive heuristic algorithms for VRP, have been developed. The structure and functionality of programming environment and programming language are described.