Kalman Filter

This text is a practical guide to building Kalman filters and shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB[registered], and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. Sometimes mistakes are introduced intentionally to the initial filter designs to show the reader what happens when the filter is not working properly. The text spends a great deal of time setting up a problem before the Kalman filter is actually formulated to give the reader an intuitive feel for the problem being addressed. Real problems are seldom presented in the form of differential equations and they usually do not have unique solutions. Therefore, the authors illustrate several different filtering approaches for tackling a problem. Readers will ...

MODELLING STUDY FOR CHARACTERIZING AND PREDICTING URBAN AIR POLLUTION Gregorio Andria, Giuseppe Cavone, Anna ML Lanzolla, Alessandro Rubino Department of Electrics and Electronics (DEE) – Politecnico di Bari Viale del Turismo 8, 74100 Taranto ...

a DIIAR c/o Polo of Como, Politecnico of Milan, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy ludovico.biagi@polimi.it b DIMeC, University of Modena and Reggio Emilia, via Vignolese 905/B, 41100 Modena, Italy (marco.dubbini, capra.alessandro, cristina.castagnetti)@unimore.it c Department of Mathematics, University of Modena and Reggio Emilia, Via Campi 213/B, 41100 Modena, Italy francesco.unguendoli@unimore.it

This paper presents a frame work for hardware acceleration for post video processing system implemented on FPGA. The deblocking filter algorithms ported on SOC having Altera NIOS-II soft core processor.SOC designed with the help of SOPC builder .Custom instructions are chosen by identifying the most frequently used tasks in the algorithm and the instruction set of NIOS-II processor has been extended. Deblocking filter new instruction added to the processor that are implemented in hardware and interfaced to the NIOS-II processor. New instruction added to the processor to boost the performance of the deblocking filter algorithm. Use of custom instructions the implemented tasks have been accelerated by 5.88%. The benefit of the speed is obtained at the cost of very small hardware resources.

This paper presents the design, sensing principles and in vitro evaluation of a novel instrumented sock intended for prediction and prevention of acute decompensated heart failure. The sock contains a drift-free ankle size sensor and a leg tissue elasticity sensor. Both sensors are inexpensive and developed using innovative new sensing ideas. Preliminary tests with the sensor prototypes show promising results: The ankle size sensor is capable of measuring 1 mm changes in ankle diameter and the tissue elasticity sensor can detect 0.15 MPa differences in elasticity. A low-profile instrumented sock prototype with these two sensors has been successfully fabricated and will be evaluated in the future in an IRB-approved human study.

This work deals with the state estimation and diagnosis of nonlinear systems with application to power systems. Dynamic modeling is performed using an index 1 property and decoupling techniques. New methods of state estimation, based on Extended Kalman Filter including a sliding window of measurements, are proposed to improve the robustness and accuracy. A new convergence study based on Lyapunov function and conditioning of the observability matrix is proposed to ensure the convergence of the observers. A combination of extended Kalman filter with moving horizon and the version with unknown input is considered to ensure the monitoring task. Performances of the proposed approaches were evaluated by numerical simulations of IEEE power system test.

— The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. The object detection and object classification are preceding steps for tracking an object in sequence of images. Mean shift algorithm is recently widely used in tracking clustering, etc. First phase of the system is to detect the moving objects in the video. Second phase of the system will track the detected object. In this paper, detection of the moving object has been done using simple background subtraction and tracking of single moving object has been done using modified mean shift method and Kalman filter. Further result of both algorithm is compared on basis on time and accuracy. Index Terms— object tracking; kalman filter; mean shift method..

The paper presents a novel control approach for the robot-assisted motion augmentation of disabled subjects during the standing-up manoeuvre. The main goal of the proposal is to integrate the voluntary activity of a person in the control scheme of the rehabilitation robot. The algorithm determines the supportive force to be tracked by a robot force controller. The basic idea behind

Robust calibration of option valuation models to quoted option prices is non-trivial but crucial for good performance. A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman) filters are needed to do inference since the models have latent variables (e.g. volatility). The statistical framework is made adaptive by introducing stochastic dynamics for the parameters. This allows the parameters to change over time, while treating the measurement noise in a statistically consistent way and using all data efficiently. The performance and computational efficiency of standard and iterated extended Kalman filters (EKF and IEKF) are investigated. These methods are compared to common calibration such as weighted least squares (WLS) and penalized weighted least squares (PWLS). A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic ...

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is
experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical
simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and
accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the
form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base
by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to
compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De
Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system
in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are
herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises
identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation
factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely
the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual
Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and
fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.

Many advertisers adopt the integrated marketing communications perspective that emphasizes the importance of synergy in planning multimedia activities. However, the role of synergy in multimedia communications is not well understood. Thus, the authors investigate the theoretical and empirical effects of synergy by extending a commonly used dynamic advertising model to multimedia environments. They illustrate how advertisers can estimate and infer the effectiveness of and synergy among multimedia communications by applying Kalman filtering methodology. Using market data on Dockers brand advertising, the authors first calibrate the extended model to establish the presence of synergy between television and print advertisements in consumer markets. Second, they derive theoretical propositions to understand the impact of synergy on media budget, media mix, and advertising carryover. One of the propositions reveals that as synergy increases, advertisers should not only increase the media ...

Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically , the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.

A high order signal model is proposed in which the states are Kronecker tensor products of probability distributions. This model enables an optimal linear filter to be specified. A minimum residual error variance criterion may be used to select the number of discretizations and Kronecker products. The filtering of LIDAR data from a coal shiploader environment is investigated. It is demonstrated that the proposed method can outperform conventional Kalman and hidden Markov model filters.

An autonomous ground vehicle has various devices from where it collects data and performs an action. State estimation with noise present in it is one of the key requirements for many real-time problems and engineering. State estimation is an essential requirement for many real-life systems from local to multi-resource information integration. The Kalman filter and its variability have been used successfully in solving state equity problems. The Kalman filter can be used to predict the next set of actions our car will take based on the information received. In this paper we have implemented kalman filter for state estimation and results are obtained. I. INTRODUCTION An autonomous ground vehicle is a portable robot that combines multiple sensor navigation with positioning, intelligent decision making and control technology. The aim of the study was to identify autonomous driving in a car instead of human drivers and to improve vehicle safety and transport efficiency. The main purpose of self-driving cars is to transport people from one place to another without the help of a driver. The self-driving system should control many parameters, including speed, acceleration, orientation, and maneuvering, so that the car can be driven without human help. All of these control parameters are controlled by a decision-making module, which handles all visual data from the vehicle and sensors. The visual module determines the relationship between the ego car and the surrounding environment. Vehicle stability control is mainly based on various parameters (e.g. lateral acceleration, yaw rate) of movement to determine the appropriate control strategy and achieve the safety of vehicles moving in effective control. Often, the condition of a car can be measured by the variety of sensors in the car. Restricted by the current level of technology, some important variables require the use of more expensive measuring devices (such as speed, yaw scale), or were unable to take precise measurements (such as slip angle), parameter estimation is the best solution to meet the requirements of a robust vehicle control system. State estimation having noise in the system is one of the key requirements for many real-time and engineering problems. Accuracy is the primary constraint in applications such as target tracking, automotive land vehicle and flight control systems, non-linear process control and optimization, real time surveillance and life safety applications. The classical Kalman filtering technique (KF) which is a state estimation technique which can solve this problem and is widely used in many fields. The main objective of KF is tracking the dynamic states in presence of incomplete and noisy measurements. The KF dynamics results from cycles of filtering and prediction. The Gaussian probability density functions framework is used to derive and interpreters the cycle dynamics. On the other hand, mostly the problems in real are non-linear in nature, and the kalman filter performance degrades when there is a violation of assumptions of the system Gaussian distribution and system linearity To improve performance and overcome KF limitations compared to system incomprehensibility, the Extension Kalman Filter (EKF) was introduced. EKF manages system irregularities by converting non linear system to linear by inserting limitations of the first Taylor series around the current error and covariance error.It uses the output of the component to represent the rate of change of non-linear functions, which aims to maintain Gaussian sound. If the state is a vector, then the partial derivative parameters can be grouped into new matrix, called the Jacobian matrix. 1) Prediction: In this step the Kalman filter predicts new values from initial values and predicts the uncertainty / error / variation of our prediction according to the various processes present in the system. Our model will assume that the AGV will move at a constant speed due to zero acceleration but will actually have a dynamic speed i.e. the speed will change from time to time. This change in speed of this car is uncertain / error / variance and we bring it into our system by processing noise.

—Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method for estimation of a speech signal from its noisy version. It utilizes the prior probability distributions of the signal and noise processes, which are assumed to be zero-mean Gaussian processes. The function of Kalman filter is assessed for different types of noise such as babble, crowd, large city, and highway. The noise cancellation is implemented for each of aforementioned noises which their powers vary in a range of values. This method of noise reduction yields better speech perceived quality and efficient results compared to Wien-er filter.