Measurement Update Research Articles

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

We have developed an adaptive sampling algorithm for an Explorer autonomous underwater vehicle (AUV) to conduct in-situ analysis of acoustic measurements to perform autonomous oil plume detection and tracking. The methodology of the tracking phase involves ongoing analysis of the detected plume, assessing target validity and proximity for AUV decision-making for plume mapping. We previously introduced the bumblebee flight path, a new biomimetic search pattern designed to maximize the spatial coverage in the oil plume detection phase. This paper focuses on a new tracking strategy as the key adaptive stage in our plume delineation. For initial development we used a 360-degree scanning sonar sensor model. Simulations were done with different plume models to assess the performance of the developed adaptive sampling algorithm. A convergence study demonstrated that the algorithm could successfully track the boundary of a non-regular shaped/patchy oil plume at up to a 0.05Hz sampling frequency. A sensitivity study identified the correlations between plume feature complexity and the anticipated range of acoustic measurement update delays. The decision-making architecture consists of three separate components which implements either proximity or boundary following control and contributes to the final decision on the next desired heading of the vehicle. A weight ratio, that determined the relative allocation of each component, was varied to study its impact on the tracking performance of the AUV. The novelty of our approach is in addressing the discontinuous and patchy nature of realistic oil plumes. Our sampling algorithm and its performance in simulations is a significant step beyond the practical limitations of existing gradient-following methods because it accounts for the oil patches and droplets which gradient-following algorithms do not.

Set-Membership Filtering for Nonlinear Dynamic Systems With Quadratic Inequality Constraints

The present study investigates the problem of set-membership filtering for nonlinear dynamic systems with general nonconvex inhomogeneous quadratic inequality constraints. The investigators propose an ellipsoidal state bounding estimation in the setting of unknown but bounded noise. In order to guarantee the on-line usage, the nonlinear function is linearized by Taylor expansion at each time step, where the bounding ellipsoid of the remainder is updated on-line based on the current state bounding ellipsoid. Furthermore, based on the remainder bounds and the constraints, both the state prediction and measurement update of the filtering can be transformed into a semidefinite programming problem that can be efficiently solved. In order to further reduce the computational complexity, a part-analytical formula of the shape matrix and the center of the bounding ellipsoid is derived using a decoupled technique, which is also helpful to clarify how these constraints affect the state estimation. Finally, typical numerical examples demonstrate the effectiveness of this filtering.

Auxiliary-Filter-Free Incompressible Particle Flow Filtering Using Direct Estimation of the Log-Density Gradient with Target Tracking Examples

This paper presents an incompressible particle flow filtering method that does not require an auxiliary filter by estimating log-density gradients directly from particles. Particle flow filter (PFF) is likely to avoid particle impoverishment and degeneracy problems that occur in particle filters because particles themselves move toward desired density to perform measurement updates. There are various implementation forms for PFF depending on the assumptions made about the flow. This paper deals with PFF using incompressible flow. Incompressible PFF requires the log-density gradient to calculate the flow. The well-known gradient estimation method for incompressible PFF is a finite difference method collaborating with k nearest neighbors(kNN) method. Since this method requires the prior knowledge about the prior density value in each particle, it is necessary to use an auxiliary filter or a density estimation technique. As a result, the performance of an auxiliary filter or a density estimation technique can directly affect the PFF performance, and the finite difference method is more likely to be inaccurate than directly estimating the log-density gradient. Therefore, this paper presents a PFF structure applying least-squares log-density gradient (LSLDG) method that estimates the log-density gradient directly from particles. In order to verify the performance of the presented structure, this paper performs both single and multiple target tracking simulations. Simulation results demonstrate that the presented structure has a relatively good estimation performance and works more robustly for various situations.

Bayesian Fill Volume Estimation Based on Point Level Sensor Signals

In dry bulk and fluid processing, the composites are usually stored in hoppers, tanks, or other containers. Due to the economic advantages, binary point level sensors, which detect fill level exceeding, are widely used for process monitoring and control. In this paper, we propose different filters for estimating the probability distribution of the fill volume based on a time-variant measurement distribution and a stochastic physical model with white process noise. A filter based on the model prediction with separated measurement update and two Bayesian particle filters are proposed and compared with a simulated ground truth. The performance measures are the root-mean-square error, the precision of the 95 % and 75 % credible intervals, and the average value of the estimated probability density function at the simulated fill volumes.

Recursive Least Square: RLS Method-Based Time Series Data Prediction for Many Missing Data

Prediction methods for time series data with many missing data based on Recursive Least Square (RLS) method are proposed. There are two parameter tuning algorithms, time update and measurement update algorithms for parameter estimation of Kalman filter. Two learning methods for parameter estimation of Kalman filter are proposed based on RLS method. One is the method without measurement update algorithm (RLS-1). The other one is the method without both time and measurement update algorithms (RLS-2). The methods are applied to the time series data of Defense Meteorological Satellite Program (DMSP) / Special Sensor Microwave/Imager (SSM/I) data with a plenty of missing data. It is found that the proposed RLS-2 method shows smooth and fast convergence in learning process in comparison to the RLS-1.

측정 정보 갱신과 보완을 통한 V2X 네트워크의 측위 성능 향상

측정 정보 갱신과 보완을 통한 V2X 네트워크의 측위 성능 향상

Hyperbolic‐singular‐value‐decomposition‐based square‐root accurate continuous‐discrete extended‐unscented Kalman filters for estimating continuous‐time stochastic models with discrete measurements

SummaryThis paper presents novel square‐root accurate continuous‐discrete extended‐unscented Kalman filtering (ACD‐EUKF) algorithms for treating continuous‐time stochastic systems with discrete measurements. The time updates in such methods are fulfilled as those in the extended Kalman filter whereas their measurement updates are copied from the unscented Kalman filter. All this allows accurate predictions of the state mean and covariance to be combined with accurate measurement updates. The main weakness of this technique is the need for the Cholesky decomposition of predicted covariances derived in time‐update steps. Such a factorization is highly sensitive to numerical integration and round‐off errors committed, which may result in losing the covariance's positivity and, hence, failing the Cholesky decomposition. The latter problem is usually solved in the form of square‐root filtering implementations, which propagate not the covariance matrix but its square root instead. Here, we devise square‐root ACD‐EUKF methods grounded in the singular value decomposition (SVD). The SVD rooted in orthogonal transforms is applicable to any ACD‐EUKF with nonnegative weights, whereas the remaining ones, which can enjoy negative weights as well, are treated by means of the hyperbolic SVD based on J‐orthogonal transforms. The filters constructed are presented in a concise algorithmic form, which is convenient for practical use. Their two particular versions grounded in the classical and cubature unscented Kalman filtering parameterizations are examined in severe conditions of tackling a radar tracking problem, where an aircraft executes a coordinated turn. These are also compared to their non‐square‐root predecessor and other methods within the target tracking scenario with ill‐conditioned measurements.

Solar vector solving method based on photovoltaic array and its application in attitude estimation of navigation system

Solar-powered unmanned aerial vehicles usually fly at high altitudes, and they are mainly powered by the photocells covering the body of unmanned aerial vehicles. Considering that the solar vector cannot be affected by the disturbing magnetic field and harmful acceleration, a unit solar vector solving method based on photovoltaic array is proposed in this paper. The photocells with different installation angles are selected to form the photovoltaic array. The solar vector is solved by the least-squares method on the basis of normalization by using the output currents of the photovoltaic array. For eliminating the influence of faults and reflected light on the solving of the solar vector, an adaptive least-squares unit solar vector solving method is proposed. In addition, a solar vector measuring device is designed in order to verify the effectiveness of the proposed methods. By employing the structural advantages of the device, the current generated by the reflected light of the sky can be solved according to the currents generated by all photocells of this device. Thus, the solved current generated by the reflected light of the sky is more accurate. Moreover, strapdown inertial navigation system/solar vector/global positioning system integrated navigation Kalman filtering algorithm is proposed, in which the adaptive least-squares unit solar vector solving method is applied to the measurement update of the filter. The effectiveness of the methods proposed in this paper is illustrated by some numerical and physical simulations.

Distributed hybrid consensus–based square-root cubature quadrature information filter and its application to maneuvering target tracking

To handle nonlinear filtering problems with networked sensors in a distributed manner, a novel distributed hybrid consensus–based square-root cubature quadrature information filter is proposed. The proposed hybrid consensus–based square-root cubature quadrature information filter exploits fifth-order spherical simplex-radial quadrature rule to tackle system nonlinearities and incorporates a novel measurement update strategy into the hybrid consensus filtering framework, which takes the predicted measurement error into account and hence produces more accurate estimates. In addition, the proposed hybrid consensus–based square-root cubature quadrature information filter inherits the complementary positive features of both consensus on information and consensus on measurements methods and avoids sensitive matrix operations such as square-root decompositions and inversion of covariances, which is beneficial for numerical stability. Stability analysis with respect to consensus, convergence, and consistency for the proposed hybrid consensus–based square-root cubature quadrature information filter is also developed. The effectiveness of the proposed hybrid consensus–based square-root cubature quadrature information filter is validated through a maneuvering target tracking scenario. The simulation results show that the proposed hybrid consensus–based square-root cubature quadrature information filter outperforms the existing algorithms at the expense of a slight increase in computational cost.

Guaranteed methods based on constrained zonotopes for set-valued state estimation of nonlinear discrete-time systems

This paper presents new methods for set-valued state estimation of nonlinear discrete-time systems with unknown-but-bounded uncertainties. A single time step involves propagating an enclosure of the system states through the nonlinear dynamics (prediction), and then enclosing the intersection of this set with a bounded-error measurement (update). When these enclosures are represented by simple sets such as intervals, ellipsoids, parallelotopes, and zonotopes, certain set operations can be very conservative. Yet, using general convex polytopes is much more computationally demanding. To address this, we present in this paper two new methods, a mean value extension and a first-order Taylor extension, for efficiently propagating constrained zonotopes through nonlinear mappings. These extend existing methods for zonotopes in a consistent way. Examples show that these extensions yield tighter prediction enclosures than zonotopic estimation methods, while largely retaining the computational benefits of zonotopes. Moreover, they enable tighter update enclosures because constrained zonotopes can represent intersections much more accurately than zonotopes.

Prism Signal Processing of Coriolis meter data for gasoline fuel injection monitoring

Prism Signal Processing is a new recursive FIR technique offering rapid filter design and calculation. It has previously been applied to Coriolis mass flow metering to generate fast (48 kHz) flow measurement updates, facilitating for the first time the direct mass flow measurement of individual fuel pulses injected into a laboratory diesel fuel injection test bench. In this paper we describe an augmented sensor signal filtering scheme which enables rapid tracking of the desired mode of flow tube vibration while notching out undesired modes. The new scheme is applied to a gasoline injection test bench where the vibrational interference is greater than for the previously described diesel system due to increased hydraulic shock. The paper presents experimental findings which illustrate the further challenges to be overcome in order to achieve the goal of traceable direct mass flow measurement of individual fuel injection pulses. For example, when a fuel pulse is shorter than the resonant period of the flow tube, the observed phase difference appears to show dependence on the instantaneous phase of the flow tube vibration.

An Unconventional Multiple Low-Cost IMU and GPS-Integrated Kinematic Positioning and Navigation Method Based on Singer Model.

To release the strong dependence of the conventional inertial navigation mechanization on the a priori low-cost inertial measurement unit (IMU) error model, this research applies an unconventional multi-sensor integration strategy to integrate multiple low-cost IMUs and a global positioning system (GPS) for mass-market automotive applications. The unconventional integration strategy utilizes a basic three-dimensional (3D) kinematic trajectory model as the system model to directly estimate navigational parameters, and it allows the measurements from all of the sensors independently participating in measurement updates. However, the less complex kinematic model cannot realize smooth transitions between different motion statuses for the road vehicle with acceleration maneuvers. In this manuscript, we establish a more practical 3D kinematic trajectory model based on a “current” statistical Singer acceleration model to realize smooth transitions for the maneuvering vehicle. In addition, taking advantage of the unconventional strategy, we individually model the systematic errors of each IMU and the measurements of all sensors, in contrast to most existing approaches that adopt the common-mode errors for different sensors of the same design. A real dataset involving a GPS and multiple IMUs is processed to validate the success of the proposed algorithm model under the unconventional integration strategy.

Heading Drift Reduction for Foot-Mounted Inertial Navigation System via Multi-Sensor Fusion and Dual-Gait Analysis

Foot-mounted inertial navigation is an important issue in areas such as pedestrian localization, gait analysis, and sport training. However, low-cost inertial sensors suffer from several errors that make the navigation results less convincing. In this paper, a multi-sensor approach with one sensor on each foot is presented to reduce the system heading drift. Through dual-gait analysis, gait parameters between two feet are employed to make the non-collocated and uncoupled subsystems be related to each other. A step length estimator based on an inverted pendulum model is developed to derive a relative position vector between the two foot-mounted sensors rather than a distance scalar. A Kalman-type filter with one time update and two measurement updates is developed to fuse the velocity and position observations at foot and person levels, respectively. Experiments were conducted by four healthy subjects, and experimental results show that the proposed sensor fusion method can effectively reduce the heading drift of inertial navigation and make the captured dual-foot motion closer to its actual process.

Upper Atmosphere Radiance Data Assimilation: A Feasibility Study for GOLD Far Ultraviolet Observations

AbstractFar ultraviolet observations of Earth's dayglow from the National Aeronautics and Space Administration (NASA) Global‐scale Observations of the Limb and Disk (GOLD) mission presents an unparalleled opportunity for upper atmosphere radiance data assimilation. Assimilation of the Lyman‐Birge‐Hopfield (LBH) band emissions can be formulated in a similar fashion to lower atmosphere radiance data assimilation approaches. To provide a proof‐of‐concept for such an approach, this paper presents assimilation experiments of simulated LBH emission data using an ensemble filter measurement update step implemented with National Oceanic and Atmospheric Administration (NOAA)'s Whole Atmosphere Model (WAM) and National Center for Atmospheric Research (NCAR)'s Global Airglow (GLOW) model. Primary findings from observing system simulation experiments (OSSEs), wherein “truth” atmospheric conditions simulated by NCAR's Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM) are used to generate synthetic GOLD data, are as follows: (1) Assimilation of GOLD LBH disk emission data can reduce the bias in model temperature specification (ensemble mean) by 60% under both geomagnetically quiet conditions and disturbed conditions. (2) The reduction in model uncertainty (ensemble spread) as a result of assimilation is about 20% in the lower thermosphere and 30% in the upper thermosphere for both conditions. These OSSEs demonstrate the potential for far ultraviolet radiance data assimilation to dramatically reduce the model biases in thermospheric temperature specification and to extend the utility of GOLD observations by helping to resolve the altitude‐dependent global‐scale response of the thermosphere to geomagnetic storms.

A new measurement for yaw estimation of land vehicles using MARG sensors

PurposeThe magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances.Design/methodology/approachIn this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally.FindingsThe experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm.Originality/valueThe paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields.

Progressive Gaussian approximation filter with adaptive measurement update

This paper presents a progressive Gaussian approximation filter with adaptive measurement update for nonlinear systems. First, a progressive Gaussian filtering (PGF) framework with variable step-size is presented by using the Bayesian rule, and second, a progressive Gaussian approximation filter is designed under the framework of the variable step-size PGF, where the means and covariances of corresponding Gaussian distributions are calculated by using the unscented transform. Moreover, termination conditions for stopping the progressive measurement update are given, which are not only good for the computational efficiency, but also improve the adaptive ability against measurement uncertainties. Finally, by simulations of a target tracking example, it demonstrates that the proposed method has higher computational efficiency and robustness than the existing PGAF.

An Improved Hatch Filter Algorithm towards Sub-Meter Positioning Using only Android Raw GNSS Measurements without External Augmentation Corrections

In May 2016, the availability of GNSS raw measurements on smart devices was announced by Google with the release of Android 7. It means that developers can access carrier-phase and pseudorange measurements and decode navigation messages for the first time from mass-market Android-devices. In this paper, an improved Hatch filter algorithm, i.e., Three-Thresholds and Single-Difference Hatch filter (TT-SD Hatch filter), is proposed for sub-meter single point positioning with raw GNSS measurements on Android devices without any augmentation correction input, where the carrier-phase smoothed pseudorange window width adaptively varies according to the three-threshold detection for ionospheric cumulative errors, cycle slips and outliers. In the mean time, it can also eliminate the inconsistency of receiver clock bias between pseudorange and carrier-phase by inter-satellite difference. To eliminate the effects of frequent smoothing window resets, we combine TT-SD Hatch filter and Kalman filter for both time update and measurement update. The feasibility of the improved TT-SD Hatch filter method is then verified using static and kinematic experiments with a Nexus 9 Android tablet. The result of the static experiment demonstrates that the position RMS of TT-SD Hatch filter is about 0.6 and 0.8 m in the horizontal and vertical components, respectively. It is about 2 and 1.6 m less than the GNSS chipset solutions, and about 10 and 10 m less than the classical Hatch filter solution, respectively. Moreover, the TT-SD Hatch filter can accurately detect the cycle slips and outliers, and reset the smoothed window in time. It thus avoids the smoothing failure of Hatch filter when a large cycle-slip or an outlier occurs in the observations. Meanwhile, with the aid of the Kalman filter, TT-SD Hatch filter can keep continuously positioning at the sub-meter level. The result of the kinematic experiment demonstrates that the TT-SD Hatch filter solution can converge after a few minutes, and the 2D error is about 0.9 m, which is about 64%, 89%, and 92% smaller than that of the chipset solution, the traditional Hatch filter solution and standard single point solution, respectively. Finally, the TT-SD Hatch filter solution can recover a continuous driving track in this kinematic test.

Multisensor Navigation Systems Multicore Architecture

Multisensor navigation systems with an inertial navigation system (INS), employ GPS and other aiding sensors to correct INS errors and improve navigation accuracy. Kalman filter (KF) is used to fuse the aiding sensor measurements and estimate errors. Both the time update, in which the state vector and covariance matrix are propagated, and the measurement update, in which all the aiding sensor measurements are fused with the INS-derived variables, have high computational costs. Two forms of the KF, the original covariance KF and the newer information KF, are examined. The most efficient parts of each are combined into a hybrid covariance/information filter. The state and measurement spaces of the hybrid filter are partitioned by sensors leading to blocked matrix formulas for both time update and measurement update. The blocked equations lead directly to threaded architecture implementations. The threaded architecture permits either serial operation with a single processor or parallel operation with multiple processors. Formulas are derived and evaluated for the floating-point operation counts for a general unblocked-serial hybrid filter and its blocked-serial and blocked-parallel implementations for an INS aided by GPS and other sensors. The blocked-parallel hybrid filter shows a significant improvement compared to the blocked-serial and unblocked-serial implementations.

Prediction and measurement update of fungal toxin geospatial uncertainty using a Stacked Gaussian process

The paper develops a stacked Gaussian process using both field and wet-lab measurements to predict fungal toxin (aflatoxin) concentrations in corn in South Carolina. While most of the aflatoxin contamination issues associated with the post-harvest period in the U.S. can be controlled with expensive testing, a systematic and economical approach is lacking to determine how the pre-harvest aflatoxin risk adversely affects crop producers as aflatoxin is virtually unobservable on a geographical and temporal scale. This information gap carries significant cost burdens for grain producers and it is filled by the proposed stacked Gaussian process. The novelty of the paper is twofold. First, the aflatoxin probabilistic maps are obtained using an analytical scheme to propagate the uncertainty through the stacked Gaussian process. The model predictions are validated both at the Gaussian process component level and at the system level for the entire stacked Gaussian process using historical field data. Second, a novel derivation is introduced to calculate the analytical covariance of aflatoxin production at two geographical locations. This is used to predict aflatoxin at unobserved locations using measurements at nearby locations but with the prior mean and covariance provided by the stacked Gaussian process. As field measurements arrive, this measurement update scheme may be used in targeted field inspections to warn farmers of emerging aflatoxin contaminations.

자기교란에 의한 방위각 추정 오차 최소화를 위해 기구학적 구속조건을 투영한 칼만 필터

With regard to 3D orientation estimation based on IMMU (Inertial Magnetic Measurement Unit) signals, the yaw estimation accuracy may be significantly degraded as a result of magnetic distortions. Consequently, several yaw estimation Kalman filters (KFs) possessing distortion compensation mechanisms have been proposed. However, majority of the conventional methods fail to effectively curb inaccuracies due to distortion when magnetic fields are extremely distorted. In this paper, we propose a new KF projecting a kinematic constraint to minimize yaw estimation errors induced by magnetic distortions. After the measurement update using magnetometer signals, the proposed method additionally corrects the yaw estimation through projection of a kinematic constraint on a conventional unconstrained KF. Experimental results show that the proposed KF outperformed the conventional KF by approximately 52-67%.