Multiple Kalman filters model with shaping filter GPS real-time deformation analysis

In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.

Mitigation of Transients in Capacitor Coupled Substations Using Traditional RLC Filter Techniques

This article presents an extensive examination and modeling of Capacitor Coupled Substations (CCS), noting some of their inherent constraints. The underlying implementation of a CCS is to supply electricity directly from high-voltage (HV) transmission lines to low-voltage (LV) consumers through coupling capacitors and is said to be cost-effective as compared to conventional distribution networks. However, the functionality of such substations is susceptible to various transient phenomena, including ferroresonance and overvoltage occurrences. To address these challenges, the study uses simulations to evaluate the effectiveness of conventional resistor-inductor-capacitor (RLC) filter in mitigating hazardous overvoltage resulting from transients. The proposed methodology entails using standard RLC filter to suppress transients and its associated overvoltage risks. Through a series of MATLAB/Simulink simulations, the research emphasizes the practical effectiveness of this technique. The study examines the impact of transients under varied operational scenarios, including no-load switching conditions, temporary short-circuits, and load on/off events. The primary aim of the article is to assess the viability of using an established technology to manage system instabilities upon the energization of a CCS under no-load circumstances or in case of a short-circuit fault occurring on the primary side of the CCS distribution transformer. The findings underscore the effectiveness of conventional RLC filters in suppressing transients induced by the CCS no-load switching.

Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.

Multiple Model Filtering in the Presence of Gaussian Mixture Measurement Noises

A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance as the interacting multiple model filter at the price ofless computational cost. Numerically robust implementation of the filter is presented to meetpractical applications. An example on bearings-only guidance demonstrates the effect of the proposedalgorithm.

Investigation of the different weight models in Kalman filter:A case study of GNSS monitoring results

During geodetic monitoring with GNSS technology one of important steps is the correct processing and analysis of the measured displacements. We used the processing method of Kalman filter smoothing algorithm, which allows to evaluate not only displacements, but also the speed, acceleration, and other characteristics of the deformation model. One of the important issues is the calculation of the obser- vations weight matrix in the Kalman filter. Recurrence algorithm of Kalman filtering can calculate and specify the weights during processing. However, the weights obtained in such way do not always exactly correspond to the actual observation accuracy. We established the observations weights based on the accuracy of baseline measurements. In the presented study, we offered and investigated different models of establishing the accuracy of the baselines. The offered models and the processing of the measured displacements were tested on an experimentally geodetic GNSS network. The research results show that despite of different weight models, changing weights up to 2 times do not change Kalman filtering ac- curacy extremely. The significant improvements for Kalman filtering accuracy for baselines shorter than 10 km were not got. Therefore, for typical GNSS monitoring networks with baseline range 10-15 km, we recommend to use any kind of models. The compulsory condition for getting correct and reliable results is checking results on blunders. For baselines, which are longer than 15 km we propose to use weight model which include baseline standard deviation from network adjustment and corrections for baseline length and its accuracy.

Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model

The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.

A novel maneuvering multi-target tracking algorithm based on multiple model particle filter in clutters

To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method.

Particle-filter-based walking prediction model for occlusion situations

In the field of mobile robotics,human tracking has emerged as an important objective for facilitating human-robot interaction.In this paper,we propose a particle-filter-based walking prediction model that will address an occlusion situation.Since the target being tracked is a human leg,a motion model for a leg is required.The validity of the proposed model is verified experimentally.

A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern

Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.

PARTICLE FILTERING BASED AUTOREGRESSIVE CHANNEL PREDICTION MODEL

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.

Kalman Filter for Generalized 2-D Roesser Models

The design problem of the state filter for the generalized stochastic 2-D Roesser models, which appears when both the state and measurement are simultaneously subjected to the interference from white noise, is discussed. The well-known Kalman filter design is extended to the generalized 2-D Roesser models. Based on the method of “scanning line by line”,the filtering problem of generalized 2-D Roesser models with mode-energy reconstruction is solved. The formula of the optimal filtering, which minimizes the variance of the estimation error of the state vectors, is derived. The validity of the designed filter is verified by the calculation steps and the examples are introduced.

Algebraic Attack on Filter-Combiner Model Keystream Generators

Algebraic attack was applied to attack Filter-Combintr model keystreamgenerators. We proposed the technique of function composition to improve the model, and the improvedmodel can resist the algebraic attack. A new criterion for designing Filter-Combiner model was alsoproposed: the total length I. of Linear Finite State Machines used in the model should be largeenough and the degree d of Filter-Combiner function should be approximate [L/2].

Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter

Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.

A “Dressed” Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation （3DVAR）. Ensemble optimal interpolation （EnOI）, a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ＂dressing＂ a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ＂dressing＂ means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF （DrEnKF for short） scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model （HYCOM） over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors （RMSE） at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.

A Framework of Finite-model Kalman Filter with Case Study： MVDP-FMKF Algorithm

然而，过滤技术的 Kalman 广泛地在许多应用被使用了为线性 Gaussian 系统的标准 Kalman 过滤器不能通常工作很好或甚至面对大模型无常分叉。在实际应用程序，有高费用的实验的大数字是昂贵的或对甚至不可能获得一个准确系统模型。在有限模型的适应控制上由我们的以前的开创的工作激发了，过滤的有限模型的 Kalman 的一个框架在这份报纸被介绍。这个框架想那大模型无常可以被能与对方很不同的已知的模型的一个有限集合限制。而且，在集合的已知的模型的数字能灵活地被选择以便换句话说，不明确的模型可以被已知的模型之一总是接近大模型无常被已知的模型的凸的壳盖住。在介绍框架以内根据经由最小化的向量距离原则的适应切换的想法，一个简单有限模型的 Kalman 过滤器， MVDP-FMKF，被广泛的模拟算术地提出并且说明。MEMS 回转仪飘移的一个实验验证了建议算法的有效性，显示有限模型的 Kalman 过滤器的机制在 Kalman 过滤器的实际应用有用、有效，特别在惯性的航行系统。

A numerical storm surge forecast model with Kalman filter

Kalman filter data assimilation technique is incorporated into a standard two-dimensional linear storm surge model. Imperfect model equation and imperfect meteorological forcimg are accounted for by adding noise terms to the momentum equations. The deterministic model output is corrected by using the available tidal gauge station data. The stationary Kalman filter algorithm for the model domain is calculated by an iterative procedure using specified information on the inaccuracies in the momentum e- quations and specified error information for the observations. An application to a real storm surge that occurred in the summer of 1956 in the East China Sea is performed by means of this data assimilation technique. The result shows that Kalman filter is useful for storm surge forecast and hindcast.

A NEW MODELING AND FILTERING APPROACH FOR TRACKING MANEUVERING TARGETS

A new modeling and filtering approach for tracking maneuvering targets is presented in thispaper.The approach,which makes optimal estimate for the model With the random variable possible,depends on random step modeling of target maneuvers.In the new model,the unknown targetacceleration is treated as a random variable and then estimated directly.A detector is designed tofind out the target maneuvers and the estimation algorithm will be restarted when the maneuvers oc-cur.Combination of three-dimention Kalman filter with a detector forms a tracker for maneuveringtargets.The new tracking scheme is easy to implement and its capability is illustrated in two trackingexamples in which the new approach is compared with Mooses’on the performance.

Monotonic Digit Filter for Limited-Area Model

Numerical diffusion or filter are used in most numerical models in order to eliminate small-scale (near two-grid intervals in wavelength) waves, However, conventional diffusion or filter schemes introduce the noise, and indeed few people realized, by filters themselves. For instance, most filters are troubled when they are put to use on meteorological fields with sharp gradient or with steep slope and consequently, the recurrence of undesirable numerical high-frequent oscillations (overshooting and undershooting) seems to be inevitable, Particularly when diffusion or filter is implemented in limited-area models, serious side effects on the limited-area boundaries often contaminate the modeling results. The merits and demerits are surveyed for commonly used diffusion or filter operations. A new type of monotonic digit filter is suggested to prevent overshooting and undershooting (due to the computational shock and Gibbs oscillation) nearby the discontinuous or nearly discontinuous locations when the filtering process was carried out, meanwhile the high selective property of damping is retained. Moreover, the new filter is designed on the implicit framework so that it can easily handle the problem of boundary diminishing in limited-area modeling.

Updating Geologic Models using Ensemble Kalman Filter for Water Coning Control

This study investigated the feasibility of updating prior uncertain geologic models using Ensemble Kalman filter for controlling water coning problems in horizontal wells. Current downhole data acquisition technol-ogy allows continuous updating of the reservoir models and real-time control of well operations. Ensemble Kalman Filter is a model updating algorithm that permits rapid assimilation of production response for res-ervoir model updating and uncertainty assessment. The effect of the type and amount of production data on the updated geologic models was investigated first through a synthetic reservoir model, and then imple-mented on a laboratory experiment that simulated the production of a horizontal well affected by water con-ing. The worth of periodic model updating for optimized production and oil recovery is demonstrated.