Real-Time Optimal State Estimation Scheme With Delayed and Periodic Measurements

The performance of a Kalman filter that estimates core power in a pressurized water reactor was evaluated using data from the Loss of Fluid Test (LOFT) Reactor and linear error analysis techniques. The Kalman filter provides a more accurate core power estimate in real time than does the conventional method of power estimation. Accuracies of better than 0.75% of rated power are obtained with the Kalman filter. If this accuracy improvement can be translated into higher levels of power operation, then more revenue can be generated by a commercial power plant. Scaling the LOFT 150-MW(thermal) l accuracy improvement up to a 1000-MW(electric) commercial power plant size, assuming that average power production is increased by the amount of the accuracy improvement, results in more than $800 000 of extra revenue per year ($0.06/kW·h).

We provide algorithms for inferring GPS (Global Positioning System) location and for quantifying the uncertainty of this estimate in real time. The algorithms are tested on GPS data from locations in the Southern Hemisphere at four significantly different latitudes. In order to rank the algorithms, we use the so-called log-score rule. The best algorithm uses an Ornstein–Uhlenbeck (OU) noise model and is built on an enhanced Kalman Filter (KF). The noise model is capable of capturing the observed autocorrelated process noise in the altitude, latitude and longitude recordings. This model outperforms a KF that assumes a Gaussian noise model, which under-reports the position uncertainties. We also found that the dilution-of-precision parameters, automatically reported by the GPS receiver at no additional cost, do not help significantly in the uncertainty quantification of the GPS positioning. A non-learning method using the actual position measurements and employing a constant uncertainty does not even converge to the correct position. Inference with the enhanced noise model is suitable for embedded computing and capable of achieving real-time position inference, can quantify uncertainty and be extended to incorporate complementary sensor recordings, e.g., from an accelerometer or from a magnetometer, in order to improve accuracy. The algorithm corresponding to the augmented-state unscented KF method suggests a computational cost of , where is the dimension of the augmented state-vector and is an adjustable, design-dependent parameter corresponding to the length of “past values” one wishes to keep for re-evaluation of the model from time to time. The provided algorithm assumes . Hence, the algorithm is likely to be suitable for sensor fusion applications.

This letter is concerned with the problem of optimal linear exponential quadratic state estimation for discrete-time Gauss–Markov systems with intermittent observations. The optimal estimator and optimal cost are derived via an information state approach. The necessary and sufficient condition for the existence of the estimator is provided. For a special case when a scalar parameter in the exponential cost goes to zero, the derived estimator reduces to the corresponding Kalman filter. It is also interesting to note that the resulting estimator has the same form as that obtained from the $H_{\infty }$ setting.

High dynamic aircraft is a very attractive new generation vehicles, in which provides near space aviation with large flight envelope both speed and altitude, for example the hypersonic vehicles. The complex flight environments for high dynamic vehicles require high accuracy and stability navigation scheme. Since the conventional Strapdown Inertial Navigation System (SINS) and Global Position System (GPS) federal integrated scheme based on EKF (Extended Kalman Filter) is invalidation in GPS single blackout situation because of high speed flight, a new high precision and stability integrated navigation approach is presented in this paper, in which the SINS, GPS and Celestial Navigation System (CNS) is combined as a federal information fusion configuration based on nonlinear Unscented Kalman Filter (UKF) algorithm. Firstly, the new integrated system state error is modeled. According to this error model, the SINS system is used as the navigation solution mathematic platform. The SINS combine with GPS constitute one error estimation filter subsystem based on UKF to obtain local optimal estimation, and the SINS combine with CNS constitute another error estimation subsystem. A non-reset federated configuration filter based on partial information is proposed to fuse two local optimal estimations to get global optimal error estimation, and the global optimal estimation is used to correct the SINS navigation solution. The χ 2 fault detection method is used to detect the subsystem fault, and the fault subsystem is isolation through fault interval to protect system away from the divergence. The integrated system takes advantages of SINS, GPS and CNS to an immense improvement for high accuracy and reliably high dynamic navigation application. Simulation result shows that federated fusion of using GPS and CNS to revise SINS solution is reasonable and availably with good estimation performance, which are satisfied with the demands of high dynamic flight navigation. The UKF is superior than EKF based integrated scheme, in which has smaller estimation error and quickly convergence rate.

Pose estimation in real time is a challenging research area with numerous applications. Obtaining detected key points in each pose presents us with useful applications, one such application being in the augmented reality domain. This paper presents an approach to 3D model overlay on an image with pose key point information. The model used in this work is based on the paper titled Multi-Person Pose Estimation. An FCN is employed for extracting key point information from the data. The method used in this work involves intersecting the image plane and the desired 3D model in real time and orienting the model correctly in accordance with the key point information. The performance will greatly depend on the GPU specifications.KeywordsAugmented realityPose estimation3D modelReal-time applications

Many technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.

An adaptive approach is presented for optimal estimation of a sampled stochastic process with finite-state unknown parameters. It is shown that for processes with an implicit generalized Markov property that the optimal (conditional mean) state estimates can be formed from (i) a set of optimal estimates based on known parameters, and (ii) a set of "learning" statistics which are recursively updated. The formulation thus provides a separation technique which simplifies the optimal solution of this class of nonlinear estimation problems. Examples of the separation technique are given for prediction of a non-Gaussian Markov process with unknown parameters and for filtering the state of a Gauss-Markov process with unknown parameters. General results are given on the convergence of optimal estimation systems operating in the presence of unknown parameters. Conditions are given under which a Bayes optimal (conditional mean) adaptive estimation system will converge in performance to an optimal system which is "told" the value of unknown parameters.

The work is devoted to the development of an algorithm for the optimal Radio Signal Time Delay Estimation Performance in passive location systems of stationary targets based on the TDOA method in two-dimensional space. A realistic model of the radio signal at the input of sensors (base station receivers) is considered, considering the random power value as a function of the distance to the source. The optimal estimate is based on the strategy of maximum posterior probability density. The calculation of the statistical characteristics of the obtained estimate of the radio signal delay time is carried out. The Bayesian Cramér - Rao lower bound (BCRLB) of the latency estimate is calculated. It is shown that the use of a priori statistical information on the path loss of a radio signal model can improve the accuracy of estimating the propagation delay time of a radio signal in TDOA/SSR-Based Source Localization Systems.

The article proposes an integrated approach to modeling the initial stage of tracking air targets in radar systems based on a combination of a probabilistic-temporal model of the GERT type and a recursive state estimation procedure using the Kalman filter. At the macro level, the process of interaction "radar-tracked object" is described by an exponential GERT network using moment generating functions, which allows analytically forming an equivalent W-function, determining the distribution and probability density functions of the interaction time, as well as taking into account multivariate transition trajectories, returns to previous states, and detection confirmation delays. It is shown that the GERT model effectively reproduces global temporal-probabilistic regularities of the process, but does not provide recursive correction of coordinates and velocities estimates in real time and minimization of the mean square error. To overcome these limitations, it is proposed to integrate the Kalman filter as a micro-level tool for trajectory estimation. A discrete linear stochastic model in the state space is formulated, a two-step recursion "prediction-correction", principles of selection of motion models (CV, CA, CT), structures of noise covariance matrices and tuning procedures based on RMSE and NEES criteria are presented. It is shown that the time characteristics obtained from GERT can be used to initialize the initial state and covariances, as well as for adaptive tuning of the sampling step and process noise parameters. The modeling results demonstrate the complementarity of the approaches: GERT forms a structural-probabilistic framework of the tracking process, while the Kalman filter provides smoothing of measurements, reduction of the spread of estimates and a significant reduction of RMSE compared to the macro-level estimate. The proposed integration framework "GERT → Kalman" creates a methodological basis for building a comprehensive model of goal tracking in a cluttered environment, combining the analysis of the temporal-probabilistic structure of the process with highly accurate recursive real-time state estimation.

BART is a state-of-the-art active end-to-end bandwidth measurement method that estimates not only the available bandwidth but also the link capacity of the bottleneck link. It uses a Kalman filter to give estimates in real time during a measurement session. In this paper, we have studied the impact of 802.11 networks on the bandwidth estimates produced by BART. The Kalman filter used by BART is tunable, and one of the contributions of this paper is to show how the Kalman filter should be adjusted to improve real-time tracking and estimation accuracy when the bottleneck is an 802.11 link. Further, the paper contributes by discussing how to interpret the estimates produced by BART and similar bandwidth estimation tools relying on self-induced congestion when used in wireless scenarios. An analysis show that the BART estimates produced are correct - but corresponds to a fair share of the wireless link rather than to the unused capacity. However, the estimates do indicate how much bandwidth an application or device in the wireless network can expect when sending and/or receiving network traffic.

A Comparison of Multiple Imputation and Optimal Estimation for Missing and Uncertain Urban Air Toxics Data

Correcting of real-time radar rainfall bias using a Kalman filtering approach

Real-time inverse estimation of multi-directional random waves from vessel-motion sensors using Kalman filter

This paper considers the problem of parameter estimation from noisy measurements of discrete-time chaotic systems. The guaranteed approach assumes that the uncertainty is represented by intervals of possible values of the unknown variables (state, model parameter and measurement errors). The developed algorithm is based on interval analysis and can be used in the forward and backward time direction. The result of the guaranteed estimation is interval estimates that contain the true values of the unknown variables. The proposed algorithm can be usefully associated with common estimation methods developed in the field of optimization approach and estimation in real time. If the estimation problem is solved by the least squares method or its modifications, the guaranteed algorithm can be used to specify the set of possible values of the unknown variables. It decreases the number of local minima of the cost function. Computed interval estimates may also be used to verify the results obtained using the modifications of the Kalman filter for nonlinear systems. In the practical section, the dependence of the results on the number of available measurements and noise level is examined.

: The study is directed toward the analysis and implementation of an optimal estimator (Kalman filter) and an optimal regulator to provide active control of the inertial instrument test platform. The design specifications are to maintain angular position within + or - .001 arcseconds and angular rate with + or - .00001667 arcseconds/second. A forced separation concept is utilized to allow the independent evaluation of the Kalman filter and the optimal regulator. Optimal and suboptimal Kalman filter models are developed and evaluated at physically realizable sampling rates. A general optimal estimation and control algorithm is developed and a proposed sequence of algorithm computations is presented. (Author)