Combination Of Kalman Filter Research Articles

Acceleration Feature Extraction of Human Body Based on Wearable Devices

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

Detection and Recognition of Moving Video Objects: Kalman Filtering with Deep Learning

Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers. The first layer, which is the input image, is of 100 * 100 size. The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling. The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms.

Multiple Hypothesis Detection and Tracking Using Deep Learning for Video Traffic Surveillance

Moroccan Intelligent Transport System is the first Moroccan system that uses the latest advances in computer vision, machine learning and deep learning techniques to manage Moroccan traffic and road violations. In this paper, we propose a fully automatic approach to Multiple Hypothesis Detection and Tracking (MHDT) for video traffic surveillance. The proposed framework combines Kalman filter and data association-based tracking methods using YOLO detection approach, to robustly track vehicles in complex traffic surveillance scenes. Experimental results demonstrate that the proposed approach is robust to detect and track the trajectory of the vehicles in different situations such as scale variation, stopped vehicles, rotation, varying illumination and occlusion. The proposed approach shows a competitive results (detection: 94.10% accuracy, tracking: 92.50% accuracy ) compared to the state-of-the-art approaches.

Real-Time Energy Management Strategy of Multi-Wheel Electric Drive Vehicles With Load Power Prediction Function

The lack of a load power prediction function in conventional multi-wheel electric drive vehicles leads to lags in control action. To address this issue, we developed a real-time energy management strategy with improved load power prediction accuracy. Based on an assessment of the overall vehicle structure, a mathematical model for each power source was established using theoretical analysis and data fitting. A method for the joint prediction of non-stationary load power combining Kalman filter and Markov chain forecasting methods was established, and a multi-objective optimization function was constructed under the nonlinear model predictive control framework. To enable real-time optimal control command, a sequential quadratic programming method in the finite time domain was applied. Finally, the multi-power source was optimized and coordinated. Multi-road driving experiments were carried out using a hardware-in-the-loop simulation platform. Comparisons of energy management control strategies with and without power prediction revealed that applying the former enhances the predictability of future load power, significantly optimizes the coordinated control of multiple power sources, improves vehicle fuel economy, and stabilizes bus voltage and battery state of charge. Moreover, it has specific reference significance in engineering application scenarios under conventional model predictive control.

RSS and Phase Kalman Filter Fusion for Improved Velocity Estimation in the Presence of Real-World Factors

Motion estimation of RFID tags has been shown to be possible using phase information, while position estimation is possible using received signal strength (RSS). This article combines phase and RSS information using a Kalman filter as sensor fusion technique. It compares the combined RSS and phase fusion Kalman filter technique for motion estimation with the phase only Kalman filter, exponential smoothing filter and raw phase motion estimation excluding a Kalman filter. The influence of frequency hopping broadcasting regulations on the performance of these techniques is investigated, an aspect that has not been properly addressed before. The primary factors found in real-world scenarios that have an effect on motion estimation techniques are identified as multipath reflections and the presence of multiple tags. The influence of such non-ideal environments are characterized and specific vulnerabilities of these techniques are investigated. Finally, the shortcoming of these techniques as well as advantages and possible environments where this technique can be successfully used are discussed.

Real-time thermal dynamic analysis of a house using RC models and joint state-parameter estimation

To enable optimal building energy management in response to the ever-changing building and boundary conditions, it is critical to have numerical models that can provide accurate online prediction based on economically measurable inputs and feedback. The present study explores the capabilities of using the unscented Kalman filter (UKF) in combination with resistance-capacitance (RC) models for online estimation of the thermal dynamics of single detached houses. A joint state-parameter UKF estimation approach is applied to estimate unknown state and model parameters by using fictitious process equations to augment the state vector to include model parameters. The performance of this approach is evaluated by comparing the estimated state values to the monitored data. In addition, the prediction capability of the updated model is also investigated. The estimation procedure, mathematical operations, and result analysis are presented in detail. The remarkable model performance achieved shows that the UKF can efficiently improve RC models’ predictability and enable timely online model updating and response prediction.

External Force Estimation in Linear Series Elastic Actuator Without Load-Side Encoder

This article presents a novel estimation method of the external force in linear series elastic actuators (SEAs). Conventionally, such force is estimated through the deflection of an elastic element, placed between motor and end-effector in SEAs (measured using two encoders). However, in some applications (e.g., wearable devices), it is difficult to mount a load-side encoder, due to cost or manufacturing issue. Moreover, methods based on encoders have limitations in terms of bandwidth, and, in turn, in estimating fast-varying impact forces. Additionally, SEAs may have a nonnegligible end-effector mass, therefore gravity (or accelerations of the actuator support) may add a contribution to the external force, not accounted by deflection-based estimation methods. To address these issues, we propose a method based on the Kalman filter in combination with the use of two MEMS accelerometers, mounted on the load-side and on the support of the SEA, for external force estimation and gravity (or support acceleration) compensation, respectively. We tested the proposed method even in a reduced version, with a single load-side accelerometer. Finally, it is shown that using the acceleration signal also improves the robustness against spring stiffness variations, compared to conventional methods. The experimental results show the effectiveness of the proposed methods.

A combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolation

The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF (Kalman Filter), ANN (Artificial Neural Network) and FL (Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT (Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively.This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing (1.7 s/sample) proves the suitability of this methodology for online diagnostics.

Motion Signal Processing for a Remote Gas Metal Arc Welding Application

This article covers the signal processing for a human–robot remote controlled welding application. For this purpose, a test and evaluation system is under development. It allows a skilled worker to weld in real time without being exposed to the associated physical stress and hazards. The torch movement of the welder in typical welding tasks is recorded by a stereoscopic sensor system. Due to a mismatch between the speed of the acquisition and the query rate for data by the robot control system, a prediction has to be developed. It should generate a suitable tool trajectory from the acquired data, which has to be a C 2 -continuous function. For this purpose, based on a frequency analysis, a Kalman-Filter in combination with a disturbance observer is applied. It reproduces the hand movement with sufficient accuracy and lag-free. The required algorithm is put under test on a real-time operating system based on Linux and Preempt_RT in connection to a KRC4 robot controller. By using this setup, the welding results in a plane are of good quality and the robot movement coincides with the manual movement sufficiently.

Совершенствование алгоритмов распознавания типов объектов в системах управления с применением нечеткой логики в условиях неопределенности

Introduction. The basis of modern information technology for complex automation systems in the face of uncertainty is the principle of situational management. Such technologies include the following: expert systems, neural network structures, fuzzy logic and associative memory. The development of intelligent technologies is associated with the combination of various methods of processing knowledge. This area provides increased performance, reduced knowledge. It is supposed that fuzzy logic and expert systems can be combined. Aim. Consider the task of improving recognition algorithms in control systems by combining optimal filtering methods and fuzzy logic at the stage of secondary processing of information about object parameters. Materials and methods. For preliminary processing, we will consider Kalman filters (FC), for the implementation of which in real time less computing resources are required in comparison with guaranteed estimation algorithms. In the proposed method, we apply FC Bank. We carry out mathematical modeling in the Mathcad 14. In the FuzzyTECH software environment, a fuzzy project “Recognizing the type of an object” is being developed. Development of a fuzzy model takes place in several stages. Firstly, the presentation of input variables in terms of linguistic variables. Secondly, relying on expert knowledge, we define terms for linguistic variables. Thirdly, the creation of a block of rules. Fourth, a study of the created fuzzy project. Results. The mathematical modeling of the FC in the devices for tracking the parameters of objects in the process of recognition in the Mathcad 14 software environment showed the possibility of using a filter bank in the considered devices. Algorithms for recognizing object types using fuzzy logic have been created and studied. Conclusion. The created control system algorithms, combining Kalman filtering and fuzzy logic, increase the efficiency of the recognition system.

Combined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States

AbstractThe scope of this study is to assess a combination of well-known techniques for bias reduction and spatial interpolation in an attempt to improve wind speed prediction for storms on a gridded domain. This is accomplished by implementing Kalman filter (KF) for bias reduction and universal kriging (UK) for spatial interpolation as postprocessing steps for the Weather Research and Forecasting (WRF) Model. It is shown that for surface wind speed, a linear KF is adequate for eliminating systematic model errors with the available storm history. KF-estimated wind speed biases at station locations are then interpolated across the model domain using UK. The combined KF–UK approach improves the wind speed forecast median bias by 55% and RMSE by 15% (bulk statistics), while benefits obtained at station-specific locations can reach maximum improvements of 72% for RMSE and 100% for bias. Contingency statistics that inform on model performance over four categories of wind speed magnitude reveal that calm/moderate winds are successfully corrected but strong/gale winds cannot be adequately corrected by the combination of KF and UK, which is a disadvantage for improving prediction of severe storm conditions.

Combined Kalman Filter and Multifeature Fusion Siamese Network for Real-Time Visual Tracking.

SiamFC has a simple network structure and can be pretrained offline on a large data set, so it has attracted the attention of many researchers. It has no online learning process at all. Hence, there are no good solutions for some complex tracking scenarios such as occlusion and large target deformation. For this problem, we propose a method using the Kalman filter method and fusion multiresolution features and get multiple response scores. The Kalman filter acquires the target’s trajectory information, which is used to process complex tracking scenes and to change the selection method of the search area. This also enables our tracker to stably track fast moving targets.The introduction of the Kalman filter compensates for the shortcomings that SiamFC can only track offline, and the tracking network has an online learning process. The fusion of multiresolution features to obtain multiple response scores map helps the tracker to obtain robust features that can be adapted to a variety of tracking targets. Our proposed method has reached the state-of-the-art in testing on five data sets and can be run in real time (40 fps), including OTB2013, OTB2015, OTB50, VOT2015 and VOT 2016.

Novel Data Assimilation Algorithm for Nearshore Bathymetry

AbstractIt can be expensive and difficult to collect direct bathymetry data for nearshore regions, especially in high-energy locations where there are temporally and spatially varying bathymetric features like sandbars. As a result, there has been increasing interest in remote assessment techniques for estimating bathymetry. Recent efforts have combined Kalman filter–based techniques with indirect video-based observations for bathymetry inversion. Here, we estimate nearshore bathymetry by utilizing observed wave celerity and wave height, which are related to bathymetry through phase-averaged wave dynamics. We present a modified compressed-state Kalman filter (CSKF) method, a fast and scalable Kalman filter method for linear and nonlinear problems with large numbers of unknowns and measurements, and apply it to two nearshore bathymetry estimation problems. To illustrate the robustness and accuracy of our method, we compare its performance with that of two ensemble-based approaches on twin bathymetry estimation problems with profiles based on surveys taken by the U.S. Army Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We first consider an estimation problem for a temporally constant bathymetry profile. Then we estimate bathymetry as it evolves in time. Our results indicate that the CSKF method is more accurate and robust than the ensemble-based methods with the same computational cost. The superior performance is due to the optimal low-rank representation of the covariance matrices.

Rapid SINS Two-Position Ground Alignment Scheme Based on Piecewise Combined Kalman Filter and Azimuth Constraint Information.

The accuracy and rate of convergence are two important performance factors for initial ground alignment of a strapdown inertial navigation system (SINS). For navigation-grade SINS, gyro biases and accelerometer offsets can be modeled as constant values during the alignment period, and they can be calibrated through two-position ground alignment schemes. In many situations for SINS ground alignment, the azimuth of the vehicle remains nearly constant. This quasi-stationary alignment information can be used as an augmented measurement. In this paper, a piecewise combined Kalman filter utilizing relative azimuth constraint (RATP) is proposed to improve the alignment precision and to reduce the time consumption for error convergence. It is presented that a piecewise time-invariant linear system can be combined into a whole extended time-invariant linear system so that a piecewise combined Kalman filter can be designed for state estimation. A two-position ground alignment algorithm for SINS is designed based on the proposed piecewise combined Kalman filter. Numerical simulations and experimental results show its superiority to the conventional algorithms in terms of accuracy and the rate of convergence.

Sea Ice Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and Interscan Banding

Effective methods of sea ice classification are crucial to regional ice type mapping using spaceborne synthetic aperture radar (SAR), especially in ScanSAR mode. However, in TerraSAR-X ScanSAR images of ocean scenes, the scalloping and interscan banding (ISB) artifacts are usually visible. Though these two artifacts could be reduced to a certain degree where residual artifacts do not impede the visual interpretation of SAR images, this is not the case when it comes to sea ice classification. The difficulty mainly comes from the involvement of texture features in sea ice classification. Texture features, especially gray level co-occurrence matrix (GLCM) are useful bases for sea ice classification using SAR data in single or dual polarization. When GLCM is applied to TerraSAR-X ScanSAR data, however, scalloping and ISB artifacts are found to be intensified, further affecting the classification result. How to largely eliminate the effects of scalloping and ISB on sea ice classification has not been studied thoroughly yet. In this paper, an approach combining Kalman filter, GLCM, and support vector machine is proposed. An independent testing shows that this approach is effective at the removal of scalloping and ISB's effects on sea ice classification using TerraSAR-X ScanSAR data, with the overall accuracy of 88.26%. Besides, convolutional neural network is implemented to compare with the proposed approach.

Impact of Current Disturbances on AUV Docking: Model-Based Motion Prediction and Countering Approaches

Underwater docking enables autonomous underwater vehicles (AUVs) to operate independently of a surface vessel for extended periods. To perform the docking mission, special attention has to be paid to the navigation, guidance, and control issues of the vehicle, especially under current disturbances. Based on a full dynamic model of an AUV in currents, this paper studies the influences of current disturbances on AUV docking motion. A comprehensive docking scheme is introduced, which combines Kalman filter type navigation, guidance with current compensation, and proportional-integral-derivative (PID) controllers for both cross-track and heading control, to ensure successful docking operations. To counter the current effects, the proposed guidance algorithm applies current estimation and attitude compensation for motion correction online; in addition, an upstream control strategy in the case of strong current is also discussed. The proposed algorithms are initially validated through model-based numerical simulations, which provide effective guidance for the succeeding docking experiments conducted in a current generating pool. The feasibility and effectiveness of the countering approaches for AUV docking under current disturbances are demonstrated during the pool trials.

Vehicle braking force distribution with electronic pneumatic braking and hierarchical structure for commercial vehicle

Brake by wire that allows a number of braking functions exists on commercial vehicles under the name electronic pneumatic braking system, which can improve braking comfort and safety of commercial vehicle. The design of braking force distribution control method has drawn much more attention. Considering structure characteristics of electronic pneumatic braking system, a control method with a three-layer hierarchical structure is proposed. The first layer presents an estimation method towards vehicle mass and centre of gravity combining Kalman filter with recursive least square. The second layer is developed by designing a fixed relationship between the brake pedal displacement and vehicle braking deceleration. A deceleration control method that only uses brake pedal displacement as input is applied to make the vehicle follow the desired deceleration. The third layer includes a braking force distribution control method between the front and rear axles. The vehicle deceleration can be achieved with a smooth axle load transfer process based on linear matrix inequality. In addition, the controllers of wheel slip rate and valve hysteresis compensation are designed to improve control accuracy and efficiency. Utilizing a hardware-in-the-loop simulation test rig of electronic pneumatic braking system, the experiment results show that the control method can improve the braking performance of vehicle.

Robust model predictive control for path-following of underactuated surface vessels with roll constraints

Abrupt maneuvering or strong disturbances from sea waves probably lead to large roll motion of underactuated surface vessels, which severely affects stability of surface vessels. A novel path-following control for vessels with roll constraints is proposed by combining Kalman filter, disturbance observer and robust constrained model predictive control method. Firstly, a switched linear uncertain model with coupled surge, sway, roll and yaw is constructed for underactuated surface vessels. Then, an improved Sage-Husa Adaptive Kalman Filter is proposed to estimate vessels' full states by partial states information and eliminate the effects of measurement noises. Next, the disturbance observer, which is used to estimate low-frequency disturbances from wind, waves and oceans, is combined with constrained model predictive control to improve the robustness of the closed-loop control system. In this paper, the effectiveness of the proposed algorithm is verified by theoretical analysis, simulations and experiments.

Utilizing the Ensemble Kalman Filter and Ensemble Kalman Smoother for Combined State and Parameter Estimation of a Three-Dimensional Towed Underwater Cable Model

Abstract A finite difference method (FDM) solving the coupled partial differential equations governing three-dimensional (3D) motions of a towed underwater cable has been implemented in a combined ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), as a new approach to combined state and parameter estimation for towed underwater cables. A simulation study of the method applied to a seismic streamer has been performed. Cable state variables as well as model parameters are estimated. Parameters estimated are crossline ocean current varying with time as well as cable tangential drag coefficient. The presented results indicate that the method is able to estimate state as well as parameters for seismic streamers.

Method to Reduce Imaging Errors in Dynamic Target Observation of the Geostationary Interferometric Microwave Sounder

The Geostationary Interferometric Microwave Sounder (GIMS) is a synthetic aperture microwave sounder working in time-sharing sampling mode with a rotating circular antenna array, thus having a relatively long imaging period. Since target motion in the imaging period will pose a problem to imaging effect, observing dynamic target such as fast changing weather of tropical cyclones will introduce imaging error to the retrieved image of GIMS. A method that combines Kalman filter and interpolation to reduce imaging error are presented, which utilizes a time sequence of visibilities collected by time-sharing sampling method. Simulations of GIMS's observation process in viewing a series of near real case brightness temperature maps modeled by the National Center for Environmental Prediction Final Operational Global Analysis data/Weather Research and Forecasting Model/Radiative Transfer for Television Infrared Observation Satellite Operational Vertical Sounder method, together with postprocessing of dynamic observation error-correction processing and image retrieval are conducted for the method. Results show that the method can reduce imaging error even if there are random noises in visibilities.