Combination Of Kalman Filter Research Articles

Data Analysis and Modeling of Chilled Water Loops in Air Conditioning Systems

Artificial neural network has been widely used in air conditioning systems as an effective method for predicting parameters, and the accuracy of ANN model relies on training data and network structure. In order to increase the quality of chilled water loops model, this paper develops an optimal data processing algorithm combining Kalman filtering with particle swarm optimization to compensate for uncertain factors and disturbances of collected data from the case building and establishes the nonlinear variation trend database. Based on Elman and BP neural networks, this paper proposes the improved network structures to avoid the local optimum predicted value of chilled water loops and increase data training speed. Simulation results show that this algorithm improves the data accuracy of current percentage (CP) of chillers and chilled water temperatures 12% and 9%. Compared with Elman and BP models, mean absolute errors of CP improved models are improved 24.1% and 10.3%, and mean squared errors of water temperature improved models are improved 5.2% and 4.8%. For the purpose of energy conservation control in air conditioning systems, this work has an application value and can be used for predicting other parameters of buildings.

Application of hybrid Kalman filter for improving water level forecast

Numerical modeling is one of the popular means to simulate and forecast the state of oceanographic systems. However, it still suffers from some limitations, e.g., parameter uncertainties, simplification of model assumptions, and absence of data for proper boundary and initial conditions. This paper proposes a hybrid data assimilation scheme, which combines the Kalman filter (KF) with a data-driven model (local linear model (LM)), to directly correct numerical model outputs at locations without measurements. Two different types of KF (unscented Kalman filter and two-sample Kalman filter) are tested and compared. A local LM is utilized to describe the evolution of the model state and then assimilated into the KF. This in turns simplifies the application of KF for highly complex nonlinear systems such as the dynamic motion of Singapore regional water. The proposed scheme is first examined using a simple hypothetical bay experiment followed by an operational model of the Singapore Regional Model (SRM), in which both are set up in the Delft3D modeling environment. This combination of KF and data-driven model provides insights into the influence of different error covariance estimations on the model updating accuracy. This research also provides guidance to offline utilization of KF in updating of numerical model output.

Video Surveillance for Vehicle Classification using Hybrid SVM and Enhanced Ensemble Classifier

Video surveillance has obtained significant growth in the application oriented research fields especially in monitoring and security systems. This paper focuses on utilizing video surveillance in roadside traffic areas for identifying the vehicles such as ambulances in order to provide better road safety especially during emergency situations. The analyses of these situations are done using the video cameras fixed at roadsides for the surveillance purposes with a distributed computing process. These video cameras provide larger video files which are to be processed using efficient machine learning approaches for enhanced vehicle tracking process. In this paper, a road traffic video surveillance system is proposed for automatically identifying the road accidents from the live video files. The proposed system utilizes Hadoop-Map Reduce framework for processing the video files distributive and also in parallel. The distributed video files are enhanced using Gaussian filtering. The background subtraction is carried out Markov Random field (MRF) with Bayesian estimation process along with shadow removal and mist removal for environmental disturbances. The scene classification is carried out using Linear Discriminant Analysis (LDA) while a Hybrid Support Vector Machine (HSVM) combining Kalman filter with the SVM is employed for tracking the vehicles. Thus the proposed method efficiently track and classify the foreground objects with use of object classification and detection in traffic scenes especially in identifying the ambulances and heavy vehicles. The results obtained from the experiments on the proposed research shows the efficiency of traffic monitoring using traffic scenes.

A Robust Home Alone Faint Detection Based on Wireless Sensor Networks

Target detection and tracking are one of the fundamental problems for wireless sensor networks and play an important role in the safety field. Faint detection is an important problem for the elderly people or patients or even pregnant women. It has wide application in current society. This paper proposed a method to collect information about the behavior and position of faint event in the sensing environment. This method detects and tracks faint person by combining Kalman filter and Camshift tracking algorithm. Experiments showed that the method yields good detection and tracking performance in complex environments.

An Improved IMM Estimator Combined with Intelligent Input Estimation Technique for Tracking Maneuvering Target

The estimation performance of interactive multiple model (IMM) estimator for tracking a maneuvering target is influenced by the target motion models and application of filters. An improved IMM estimation algorithm combined with the intelligent input estimation technique is proposed in this study. The target motion models include the constant velocity (CV) model and the modified Singer acceleration model. The intelligent fuzzy weighted input estimation (IFWIE) is used to compute the acceleration input for the modified Singer acceleration model besides the application of standard Kalman filter (KF). The combination of KF and IFWIE can estimate the target motion state precisely and the proposed method is compared with the common IMM estimator. The simulation results prove the improved IMM estimator has superior estimation performance than the common IMM estimator, especially when the target changes the acceleration violently. The utilization of IFWIE for the improved IMM estimator can estimate the acceleration input effectively.

The Multi-target Localization Algorithm via Compressed Sensing

A multi-target localization algorithm based on compressed sensing was proposed in this paper. The issue of multi-target localization was transformed into compressed sensing. The algorithm greatly reduced the amount of wireless network’s communication data by transferring most of the computing work to the central server. This method made full uses of the priori information of the signal and the support set. It combined Kalman filter with Bayesian compressed sensing to improve the localization accuracy and noise immunity. Simulation results showed that the proposed method has good noise immunity, robustness and localization accuracy compared with traditional localization methods.

Target Tracking Based on Improved Camshift and Kalman Filter

A tracking algorithm based on improved Camshift and Kalman filter is proposed in this paper to deal with the problems in traditional Camshift algorithm, such as tracking failure under color interference or occlusion. Firstly, the proposed algorithm improves the single color target model and presents a novel target model, which fuses color and motion cues, to enhance the robustness and accuracy of target tracking. And in order to increase the tracking efficiency, the algorithm combines Kalman filter with the improved Camshift algorithm by using Kalman filter to predict the position of the tracking target under color noises and occlusion. The experiment results demonstrate that the proposed algorithm can track the target object accurately and has better robustness.

Robotic Fish’s Movement Based on Kalman Filter and PID Control

Environmental disturbances lead to the reaction lag and precision drop of robotic fish in Chinese under-water robotic fish game. To resolve the problem, this paper proposes a new method combined Kalman filter and PID control algorithm for controlling robotic fish. The proposed approach was applied into the synchronized swimming competition of robotic fish. Simulation on two dimensional underwater robot game simulation platform shows that the method enhances the accuracy of the movement, shortens the time to the target, and improves the anti-interference ability of robotic fish. It is effective and reliable.

Mars Science Laboratory Entry Atmospheric Data System Trajectory and Atmosphere Reconstruction

On 5 August 2012, the Mars Science Laboratory entry vehicle successfully entered Mars’s atmosphere and landed the Curiosity rover on its surface. The entry vehicle carried a system of heat shield instrumentation that measured the aerodynamic and aerothermal environment during entry. Included in these sensors were seven pressure transducers linked to ports across the entry vehicle forebody that recorded the local surface pressures during entry. These measured surface pressures were used to generate estimates of atmospheric quantities based on computationally modeled surface pressure distributions. Angle of attack, angle of sideslip, dynamic pressure, Mach number, atmospheric conditions, and vehicle aerodynamics were reconstructed from the measured pressures. Three data reduction algorithms using subsets of the available data were used to assess data quality and interpret the entry performance. A Kalman filter algorithm was used to combine all available data for a final, complete reconstruction. The reconstruction results indicate that the data quality was good, and aerodynamic performance was within the uncertainties of preflight models. The three independent reconstructions are in overall good agreement, with several small anomalies that were reconciled using reasonable corrections within known sources of error. The combined Kalman filter reconstruction identified winds that are reasonable compared with preflight atmospheric models.

Multiple-Model Linear Kalman Filter Framework for Unpredictable Signals

This paper presents sensor fusion techniques for systems where the process model is a function of the human input and, therefore, unpredictable. The system consists of free and user-driven motion regimes. The free regime can be modeled as a damped sinusoidal waveform, while the driven regime and the transitions between regimes do not respect any sort of probability, pattern, or sequence. The quantity of interest is the deflection of a clamped beam, measured using three sensor technologies: 1) strain gages; 2) infrared; and 3) Hall effect sensors. Experiments using infrared-based motion capture as reference measuring system show that: 1) none of the sensors present optimal performance for both motion regimes and 2) measurement errors of each sensor differ significantly according to the motion regime. These findings suggest the use of sensor fusion techniques with low processing cost, compatible with real-time embedded applications. Our solution is based on a multiple-model linear Kalman filter in combination with motion segmentation. The motion segmentation discriminates gestures according to the knowledge of their process model. This allows a more predictive estimation during periods of free motion, while relying on a less predictive approach for unknown user-driven signals. In addition, we propose a framework on evaluation and selection of process models for unpredictable signals. The implementation was compared with single-sensor and single-model filter designs. Results based on human subject data reveal that the proposed method improves the error covariance of the estimate by a factor of 2.2 for driven motions and 12.7 for free motions in comparison with single-sensor filter design.

Why Risk-Based Multivariate Fault Detection and Diagnosis?

A novel risk-based fault detection method has been developed. The proposed method provides a dynamic process risk indication based on the probability of happening a fault and its consequence. In this method instead of generating an alarm based on residuals or signals an alarm is activated only when the calculated risk of operation exceeds the acceptable threshold. This is an important concept as it can funnel the attention and effort of operators to the faults which poses the most operational or safety risk. Application of this new risk-based approach provides early warning of the fault as well as the associated risk with the fault. Methodologies were developed to apply the concept with model based fault detection algorithm as well as multivariate history based fault detection techniques. In this paper we show the model based approach by combining Kalman filter with the risk based approach. The history-based approach was demonstrated using principal component analysis (PCA). This method has more power in discerning between operational changes and abnormal conditions which have potential to cause accidents.

An New Method for Multi-Object Tracking Using Energy Minimization-Based Data Association

multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.

Combining Kalman Filtering with ZigBee Protocol to Improve Localization in Wireless Sensor Network

We propose a low-cost and low-power-consumption localization scheme for ZigBee-based wireless sensor networks (WSNs). Our design is based on the link quality indicator (LQI)—a standard feature of the ZigBee protocol—for ranging and the ratiometric vector iteration (RVI)—a light-weight distributed algorithm—modified to work with LQI measurements. To improve performance and quality of this system, we propose three main ideas: a cooperative approach, a coefficient delta () to regulate the speed of convergence of the algorithm, and finally the filtering process with the extended Kalman filter. The results of experiment simulations show acceptable localization performance and illustrate the accuracy of this method.

A Positioning Scheme Combining Kalman Filtering with Vision Assisting for Wireless Sensor Networks

This paper presents the performance of an adaptive location estimator combining Kalman filtering (KF) scheme with vision-assisted scheme for wireless sensor networks. To improve the location accuracy, a KF tracking scheme is employed at a mobile terminal to track variations of the location estimate. In addition, with a vision-assisted calibration technique based on the normalized cross-correlation scheme, the proposed approach is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in measuring a dynamic environment. Therefore, using the vision-assisted approach to estimate the locations of the reference nodes as landmarks, a KF-based scheme with the landmark information can calibrate the location estimation and improve the corner effect. The experimental results demonstrate that more than 60 percent of the location estimates computed from the proposed approach have error distances less than 1.4 meters in a ZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, the proposed algorithm can achieve reasonably good performance.

Application of Kalman Filter in SOC Estimation of Power Lithium-Ion Battery

This paper analyses the application of Kalman Filter (KF) in Power Lithium-ion Battery SOC (State of Charge) estimation algorithm. After the analysis of two popular SOC estimate algorithm based on KF, an improved KF-SOC algorithm is proposed. The main advance of this improved algorithm is the introduction of parameter-rectification. The parameter-rectification which based on analysis of battery electrochemical principle and battery terminal voltage response curve is also achieved by KF. The main algorithm of improved KF-SOC is generated by the combination of KF and Ampere-hour integrated method. Later the simulations proved the new algorithm with high accuracy.

Combining Kalman Filter with Mixture Color Model Tracking of Pallet Image

In this paper, we propose combining the mixture color model with Kalman filter method. The purpose is to enable forklifts to search for pallets, but it is able to meet fully automated system with real-time. We focused on pallets for image feature and tracking. First, we manually segmented 30 pallet images and statistics of the best color threshold, this method must find the threshold of different color space and mixture of two important color spaces containing HSV and YCbCr, we extracted the H and the Cb composition mixtures to find the best color threshold, and using a combination of Kalman filter(KF) and the color model method to track pallet images, we then used the logic function to keep our information after obtaining the color image segmentation, the noise of the image must be removed, this algorithm can be used on video sequences efficiently. Finally, experimental results show that the method has effective tracking pallet images in the video sequences.

Assimilation of geodetic dynamic ocean topography using ensemble based Kalman filter

Estimation of ocean circulation is investigated via assimilation of satellite measurements of the dynamic ocean topography (DOT) into the global finite-element ocean model (FEOM). The DOT was obtained by means of a geodetic approach from carefully cross-calibrated multi-mission altimeter data and GRACE gravity fields. The spectral consistency was achieved by consistently filtering both, the sea surface and the geoid. The filter length is determined by the spatial resolution of the gravity field and corresponds to approximately 241km half width for the GRACE-based gravity field model ITG-Grace03s.The assimilation of the geodetic DOT was performed by employing a local singular evolutive interpolated Kalman (SEIK) filter in combination with the method of weighting of observations. It is shown that this approach leads to a successful assimilation technique that reduced the RMS difference between the model and the data from 16cm to 5cm during one year of assimilation. The ocean model returns an optimized mean dynamic ocean topography. The effects of assimilation on transport estimates across several hydrographic World Ocean Circulation Experiment (WOCE) sections show improvements compared to the FEOM run without data assimilation. As a result of the assimilation, DOT estimates are available in the polar or coastal regions where the geodetic estimates from satellite data alone are not adequate. Furthermore, more realistic features of the ocean can be seen in these areas compared to those obtained using the filtered data fields.

Combination of Kalman Filter and Least-Error Square Techniques in Power System

An algorithm based on the concept of combining Kalman filter and least-error square (LES) techniques is proposed in this paper. The algorithm is intended to estimate signal attributes like amplitude, frequency and phase angle in the online mode. This technique can be used in protection relays, digital AVRs, DGs, DSTATCOMs, FACTS, and other power electronics applications. The Kalman filter is modified to operate on a fictitious input signal and provides precise estimation results insensitive to noise and other disturbances. At the same time, the LES system has been arranged to operate in critical transient cases to compensate the delay and inaccuracy identified because of the response of the standard Kalman filter. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations and a laboratory test are presented to highlight the usefulness of the proposed method. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of nonlinear loads and noise.

Audio–visual speaker identification using dynamic facial movements and utterance phonetic content

Robust multimodal identification systems based on audio–visual information has not been thoroughly investigated yet. The aim of this work is to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently imitation of valid speakers could be reduced to a large extent. These parameters are applied to a Hidden Markov Model (HMM) audio–visual identification system. In this work a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. The proposed model is compared to other feature extraction methods including Kalman filtering, neural networks, adaptive network fuzzy inference system (ANFIS) and auto recursive moving average. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. The combined Kalman filtering and proposed model led to the best performance. The phonetic content of pronounced sentences is also evaluated to achieve the optimized phonetic combinations which lead to the best identification rate.

Model-based estimation of the torque characteristics of an automated dual-clutch transmission in wet technology

In many modern automotive driveline concepts the power transfer between the drive input and output is conducted by a friction clutch. The drivability of a vehicle and the exploitation of the engine power depend primarily on the interaction between the available torque and the torque delivered through the clutch. The clutch characteristic is therefore important in this context. A clutch characteristic defines the relationship between the actuating variable of the contact force and the torque delivered through the clutch. In this paper two different model approaches are discussed for estimating of the clutch characteristics using an adaptive, extended Kalman filter in combination with the recursive prediction error method. The two approaches differ in the level of abstraction and itemization used in the prediction models. The verification studies indicate that both modelling approaches allow an unbiased estimation of the clutch characteristic. However, model-dependent differences arise in transient behaviour (such as convergence time) as well as in the implementation and computational costs.