Crane Guidance Gesture Tracking and Recognition With Nonlinear Estimation and Fuzzy Logic

2018 ◽  
Author(s):  
Xin Wang ◽  
Chris Gordon ◽  
Edwin E. Yaz
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Visual Sensor ◽  
Inference System ◽  
Time Dynamic ◽  
Gesture Tracking ◽  
Nonlinear Kalman Filtering ◽  
Tracking And Recognition

This paper presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. Kinect visual sensor and MYO armband sensor are jointly utilized to perform data fusion in providing more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real-time. Dynamic equations for arm gesture movement are formulated with Newton-Euler equations based on Denavit-Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gestures recognition. Hardware experiments have shown the efficacy of proposed method for crane guidance applications.

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

A new metaheuristic unscented Kalman filter for state vector estimation of the induction motor based on Ant Lion optimizer

2018 ◽  
Vol 37 (3) ◽  
pp. 1054-1068 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi ◽  
Youssef Barradi
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Content Type ◽  
Ant Lion Optimization ◽  
Ant Lion

PurposeTo improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.Design/methodology/approachThe main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.FindingsSynthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.Originality/valueUsing the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

Nonlinear Kalman Filtering With Expensive Forward Models Via Measure Change

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Brian J. Burrows ◽  
Douglas Allaire
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Test Problems ◽  
Model Approximation ◽  
Forward Models ◽  
Estimation Scheme ◽  
Increase In Accuracy

Abstract Filtering is a subset of a more general probabilistic estimation scheme for estimating the unobserved parameters from the observed measurements. For nonlinear, high speed applications, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are common estimators; however, expensive and strongly nonlinear forward models remain a challenge. In this paper, a novel Kalman filtering algorithm for nonlinear systems is developed, where the numerical approximation is achieved via a change of measure. The accuracy is identical in the linear case and superior in two nonlinear test problems: a challenging 1D benchmarking problem and a 4D structural health monitoring problem. This increase in accuracy is achieved without the need for tuning parameters, rather relying on a more complete approximation of the underlying distributions than the Unscented Transform. In addition, when expensive forward models are used, we achieve a significant reduction in computational cost without resorting to model approximation.

scholarly journals Fuzzy Logic-based Adaptive Extended Kalman Filter Algorithm for GNSS Receivers

2018 ◽  
Vol 68 (6) ◽  
pp. 560
Author(s):  
Pasumarthi Babu Sreeharsha ◽  
Venkata Ratnam Devanaboyina
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite Systems ◽  
Scintillation Index ◽  
Ionospheric Scintillation ◽  
Inference System ◽  
Gnss Receivers ◽  
Ionospheric Scintillations ◽  
Adaptive Extended Kalman Filter

<p class="p1">Designing robust carrier tracking algorithms that are operable in strident environmental conditions for global navigation satellite systems (GNSS) receivers is the discern task. Major contribution in weakening the GNSS signals are ionospheric scintillations. The effect of scintillation can be known by amplitude scintillation index <em>S</em>4 and phase scintillation index sf parameters. The proposed fuzzy logic based adaptive extended Kalman filter (AEKF) method helps in modelling the signal amplitude and phase dynamically by Auto-Regressive Exogenous (ARX) analysis using Sugeno fuzzy logic inference system. The algorithm gave good performance evaluation for synthetic Cornell scintillation monitor (CSM) data and real-time strong scintillated PRN 12 L1 C/A data on October 24<span class="s1"><sup>th</sup></span>, 2012 around 21:30 h, Brazil local time collected by GNSS software navigation receiver (GSNR’x). Fuzzy logic algorithm is implemented for selecting the ARX orders based on estimated amplitude and phase ionospheric scintillation observations. Fuzzy based AEKF algorithm has the capability to mitigate ionospheric scintillations under both geomagnetic quiet and disturbed conditions.</p>

A NOVEL ADAPTIVE NEURO-FUZZY UNSCENTED KALMAN FILTER FOR SLAM

2011 ◽  
Vol 08 (01) ◽  
pp. 223-243 ◽  
Author(s):  
RAMAZAN HAVANGI ◽  
MOHAMMAD TESHNEHLAB ◽  
MOHAMMAD ALI NEKOUI
Keyword(s):  
Kalman Filter ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
A Priori ◽  
Inference System ◽  
Localization And Mapping ◽  
Neuro Fuzzy ◽  
Measurement Noise Covariance ◽  
Slam Algorithm ◽  
Sampling Instant

Extended Kalman filter (EKF) has been used as a popular choice to solve simultaneous localization and mapping (SLAM) problem. However, SLAM algorithm based on EKF-SLAM has two serious drawbacks, namely the linear approximation of nonlinear functions and the calculation of Jacobin matrices. For solving these problems, SLAM algorithm based on unscented Kalman filter (UKF-SLAM) has been recently proposed. However, the performance of the UKF-SLAM and thus the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices respectively denoted by Qk and Rk. Imprecise knowledge of these statistics can cause significant degradation in performance. This article proposes the development of an adaptive neuro-fuzzy UKF (ANFUKF) for SLAM. The Adaptive neuro-fuzzy attempts to estimate the elements of Rk matrix in the UKF-SLAM algorithm at each sampling instant when measurement updating step is carried out. The adaptive neuro-fuzzy inference system (ANFIS) supervises the performance of the UKF-SLAM with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (GD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much as possible. The simulation results show the effectiveness of the proposed algorithm.

Intelligent FastSLAM: An Intelligent Factorized Solution to Simultaneous Localization and Mapping

2017 ◽  
Vol 14 (01) ◽  
pp. 1650026 ◽  
Author(s):  
Ramazan Havangi
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Inference System ◽  
Localization And Mapping ◽  
Position And Orientation ◽  
Parametric Filter ◽  
Over Time

FastSLAM is a well-known solution to the simultaneous localization and mapping (SLAM) problem. In FastSLAM, a nonparametric filter is used for the mobile robot pose (position and orientation) estimation, and a parametric filter is used for the feature location's estimation. The performance of the conventional FastSLAM degrades over time due to the particle depletion and unknown statistic noises. In this paper, intelligent FastSLAM (IFastSLAM) is proposed. In this approach, an evolutionary filter (EF) searches stochastically along with the state space for the best robot's pose estimation and an adaptive fuzzy unscented Kalman filter (AFUKF) is used for the feature location's estimation. In AFUKF, a fuzzy inference system (FIS) supervises the performance of the unscented Kalman filter with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences in order to get better consistency. We demonstrate the proposed algorithm with simulations and real-world experiments. The results show that the proposed method is effective, and its performance outperforms conventional FastSLAM.

scholarly journals Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

2019 ◽  
Vol 94 ◽  
pp. 02004
Author(s):  
Dah-Jing Jwo ◽  
Shu-Ming Chang ◽  
Jen-Hsien Lai
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Statistical Characteristics ◽  
Signal Quality ◽  
Time Varying ◽  
Multiple Model ◽  
Gps Navigation ◽  
Satellite Signal

A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

scholarly journals Nonlinear Filtering of Oscillatory Measurements in Cardiovascular Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-18
Author(s):  
Ranjan Vepa
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Nonlinear Filtering ◽  
Process Model ◽  
Neuron Model ◽  
Unscented Kalman Filter ◽  
Low Noise ◽  
Model Based ◽  
Biological Neuron ◽  
Influence Of Noise

An array of nonidentical and locally connected chaotic biological neurons is modelled by a single representative chaotic neuron model based on an extension of the Hindmarsh-Rose neuron. This model is then employed in conjunction with the unscented Kalman filter to study the associated state estimation problem. The archetypal system, which was deliberately chosen to be chaotic, was corrupted with noise. The influence of noise seemed to annihilate the chaotic behaviour. Consequently it was observed that the filter performs quite well in reconstructing the states of the system although the introduction of relatively low noise had a profound effect on the system. Neither the noise-corrupted process model nor the filter gave any indications of chaos. We believe that this behaviour can be generalised and expect that unscented Kalman filtering of the states of a biological neuron is completely feasible even when the uncorrupted process model exhibits chaos. Finally the methodology of the unscented Kalman filter is applied to filter a typical simulated ECG signal using a synthetic model-based approach.

A mutated FastSLAM using soft computing

2017 ◽  
Vol 44 (4) ◽  
pp. 416-427 ◽  
Author(s):  
Ramazan Havangi
Keyword(s):  
Kalman Filter ◽  
Soft Computing ◽  
Pose Estimation ◽  
Design Methodology ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
Inference System ◽  
Content Type ◽  
Localization And Mapping ◽  
Neuro Fuzzy

Purpose Simultaneous localization and mapping (SLAM) is the problem of determining the pose (position and orientation) of an autonomous robot moving through an unknown environment. The classical FastSLAM is a well-known solution to SLAM. In FastSLAM, a particle filter is used for the robot pose estimation, and the Kalman filter (KF) is used for the feature location’s estimation. However, the performance of the conventional FastSLAM is inconsistent. To tackle this problem, this study aims to propose a mutated FastSLAM (MFastSLAM) using soft computing. Design/methodology/approach The proposed method uses soft computing. In this approach, particle swarm optimization (PSO) estimator is used for the robot’s pose estimation and an adaptive neuro-fuzzy unscented Kalman filter (ANFUKF) is used for the feature location’s estimation. In ANFUKF, a neuro-fuzzy inference system (ANFIS) supervises the performance of the unscented Kalman filter (UKF) with the aim of reducing the mismatch between the theoretical and actual covariance of the residual sequences to get better consistency. Findings The simulation and experimental results indicate that the consistency and estimated accuracy of the proposed algorithm are superior FastSLAM. Originality/value The main contribution of this paper is the introduction of MFastSLAM to solve the problems of FastSLAM.

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