Kalman developed the Kalman filter in 1960-1961 to estimate the state of a dynamic system from a series of incomplete and noisy measurements. The Kalman filter uses a recursive Bayesian approach to estimate the state of a system by minimizing the mean of the squared error. It provides an efficient computational means to estimate past, present, and even future states, and can do so even when the precise nature of the modeled system is unknown.
The document discusses Kalman filters, which are algorithms used to estimate the internal state of a system from a series of noisy measurements. Specifically:
- Kalman filters were developed in the 1960s to estimate the state of dynamic systems and filter out noise from sensor measurements in a reliable way.
- They work by using a system's predicted state and measurements from sensors to produce estimates of the true state that are better than either the predictions or measurements alone.
- The algorithm models the dynamic behavior of the system being estimated as well as the measurement noise characteristics, and uses these to produce an optimal estimate.
The document discusses Kalman filters, which are algorithms used to estimate the internal state of a system from a series of noisy measurements. Specifically:
- Kalman filters were developed in the 1960s to estimate the state of dynamic systems and filter out noise from sensor measurements in a reliable way.
- They work by using a system's predicted state and measurements from sensors to produce estimates of the true state that are better than either the predictions or measurements alone.
- The algorithm models the dynamic behavior of the system being estimated as well as the measurement noise characteristics, and uses these to produce an optimal estimate.
This document provides an overview of Kalman filter based GPS tracking. It discusses:
1) The basics of GPS including its satellite constellation and accuracy requirements.
2) The signal structure of GPS including the C/A and P codes transmitted on two frequencies.
3) How a conventional tracking loop works and its limitations in high dynamic situations.
4) How a Kalman filter can be incorporated into the tracking loop to optimally estimate GPS signals and overcome the limitations of conventional tracking loops. Simulation results show the Kalman filter based approach can track signals in high dynamics and during brief signal outages.
Radar 2009 a 16 parameter estimation and tracking part2Forward2025
This document summarizes a lecture on parameter estimation and tracking. It discusses tracking processes like track association, initiation, maintenance through prediction and updating, and termination. Filtering techniques like the Kalman filter are presented as ways to estimate target position and velocity while accounting for noise and maneuvers. Examples of civilian and military target maneuvers are provided to illustrate the challenges of tracking.
Seminar On Kalman Filter And Its ApplicationsBarnali Dey
The document discusses Kalman filters and their applications. It provides an overview of Kalman filters, explaining that they are used to estimate unknown system states from measurements that contain errors. It describes the basic algorithmic steps of Kalman filters, including prediction to project the state ahead and correction to incorporate new measurements. Finally, it gives examples of applications, such as for channel estimation in direct sequence spread spectrum communication systems.
The document discusses Rudolf Kalman and his development of the Kalman filter, a mathematical method widely used in fields such as electrical engineering, mechanical engineering, and communications. It provides background on Kalman, an overview of the Kalman filter and how it works, and examples of its applications, notably in enabling the Apollo 11 moon landing through its use in the spacecraft's guidance system. The Kalman filter provides an efficient recursive solution to problems of discrete-time data filtering and estimation in dynamic systems with random noise.
1) Probabilistic state estimation techniques like the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filters allow estimating the state of a system using probabilistic models and sensor measurements with uncertainty.
2) The Kalman filter provides a recursive solution to the linear filtering problem by estimating the current state as a weighted average of the prior state and new measurements, with the weights based on their uncertainties.
3) Extensions to the Kalman filter like the extended and unscented Kalman filters allow handling nonlinear systems by linearizing around the current estimate, while particle filters represent the state distribution with random samples and are more flexible for nonlinear and non-Gaussian problems like SLAM.
Kalman developed the Kalman filter in 1960-1961 to estimate the state of a dynamic system from a series of incomplete and noisy measurements. The Kalman filter uses a recursive Bayesian approach to estimate the state of a system by minimizing the mean of the squared error. It provides an efficient computational means to estimate past, present, and even future states, and can do so even when the precise nature of the modeled system is unknown.
The document discusses Kalman filters, which are algorithms used to estimate the internal state of a system from a series of noisy measurements. Specifically:
- Kalman filters were developed in the 1960s to estimate the state of dynamic systems and filter out noise from sensor measurements in a reliable way.
- They work by using a system's predicted state and measurements from sensors to produce estimates of the true state that are better than either the predictions or measurements alone.
- The algorithm models the dynamic behavior of the system being estimated as well as the measurement noise characteristics, and uses these to produce an optimal estimate.
The document discusses Kalman filters, which are algorithms used to estimate the internal state of a system from a series of noisy measurements. Specifically:
- Kalman filters were developed in the 1960s to estimate the state of dynamic systems and filter out noise from sensor measurements in a reliable way.
- They work by using a system's predicted state and measurements from sensors to produce estimates of the true state that are better than either the predictions or measurements alone.
- The algorithm models the dynamic behavior of the system being estimated as well as the measurement noise characteristics, and uses these to produce an optimal estimate.
This document provides an overview of Kalman filter based GPS tracking. It discusses:
1) The basics of GPS including its satellite constellation and accuracy requirements.
2) The signal structure of GPS including the C/A and P codes transmitted on two frequencies.
3) How a conventional tracking loop works and its limitations in high dynamic situations.
4) How a Kalman filter can be incorporated into the tracking loop to optimally estimate GPS signals and overcome the limitations of conventional tracking loops. Simulation results show the Kalman filter based approach can track signals in high dynamics and during brief signal outages.
Radar 2009 a 16 parameter estimation and tracking part2Forward2025
This document summarizes a lecture on parameter estimation and tracking. It discusses tracking processes like track association, initiation, maintenance through prediction and updating, and termination. Filtering techniques like the Kalman filter are presented as ways to estimate target position and velocity while accounting for noise and maneuvers. Examples of civilian and military target maneuvers are provided to illustrate the challenges of tracking.
Seminar On Kalman Filter And Its ApplicationsBarnali Dey
The document discusses Kalman filters and their applications. It provides an overview of Kalman filters, explaining that they are used to estimate unknown system states from measurements that contain errors. It describes the basic algorithmic steps of Kalman filters, including prediction to project the state ahead and correction to incorporate new measurements. Finally, it gives examples of applications, such as for channel estimation in direct sequence spread spectrum communication systems.
The document discusses Rudolf Kalman and his development of the Kalman filter, a mathematical method widely used in fields such as electrical engineering, mechanical engineering, and communications. It provides background on Kalman, an overview of the Kalman filter and how it works, and examples of its applications, notably in enabling the Apollo 11 moon landing through its use in the spacecraft's guidance system. The Kalman filter provides an efficient recursive solution to problems of discrete-time data filtering and estimation in dynamic systems with random noise.
1) Probabilistic state estimation techniques like the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filters allow estimating the state of a system using probabilistic models and sensor measurements with uncertainty.
2) The Kalman filter provides a recursive solution to the linear filtering problem by estimating the current state as a weighted average of the prior state and new measurements, with the weights based on their uncertainties.
3) Extensions to the Kalman filter like the extended and unscented Kalman filters allow handling nonlinear systems by linearizing around the current estimate, while particle filters represent the state distribution with random samples and are more flexible for nonlinear and non-Gaussian problems like SLAM.
This document describes an adaptive Kalman filter implementation for video denoising. It proposes processing video frames independently in the spatial domain and then applying an adaptive temporal Kalman filter to each pixel sequence to reduce complexity. An adaptive Kalman filter is used which can adjust its parameters based on noise statistics variations and detected motions between frames. The algorithm is tested through MATLAB simulation on sample video frames, showing it produces a denoised output with reduced noise while still responding to changes in pixel values over time. The design considerations for FPGA implementation focus on using fixed-point arithmetic and shift operations instead of division to optimize for the FPGA hardware.
07 image filtering of colored noise based on kalman filterstudymate
This document summarizes a research paper on using a Kalman filter to improve the accuracy of vehicle tracking based on GPS data. It describes how the Kalman filter works as a linear recursive technique to estimate the true state of a dynamic system by reducing noise. The document outlines the mathematical model of the Kalman filter and how it is applied to predict and correct vehicle position over time. It also discusses tuning the Kalman filter parameters like process noise covariance Q and measurement noise covariance R. Evaluation of GPS data collected from vehicle tests shows the Kalman filter reduces errors in latitude, longitude and altitude compared to not using the filter.
This document summarizes two channel estimation methods for MIMO-OFDM systems: blind channel estimation and QRD-M/Kalman filter based detection. Blind channel estimation works by identifying the channel based on knowledge of the channel and data symbols using noise subspace approach and linear precoding. It has fast convergence, requires few OFDM symbols, and can be used with any number of transmit/receive antennas. QRD-M/Kalman filter based detection uses an adaptive complexity QRD-M algorithm and Kalman filters to track individual channels with lower complexity and good tracking ability. It decomposes the received signal into an upper triangular matrix and uses maximum likelihood detection on individual subcarriers. Both methods are analyzed and their advantages/dis
The document discusses built-in self-testing (BIST) for testing integrated circuits. BIST uses on-chip pattern generators and response compactors to test circuits without needing expensive external automatic test equipment. It reduces costs associated with test generation, storage, application and diagnosis. The document covers BIST architectures, linear feedback shift registers for pseudo-random pattern generation, response compaction, and fault coverage analysis of BIST.
This document summarizes a project presentation on using a second order extended Kalman filter (EKF) for state estimation of nonlinear dynamical systems. It describes how the first order EKF approximations do not always hold for highly nonlinear trajectories or high noise. The project tests a quadcopter model with nonlinear dynamics and high process and measurement noise using a second order EKF versus a first order EKF. The results show the second order EKF estimates remain closer to the true states. Using the second order EKF for state estimation in the control loop also results in trajectories closer to the optimal compared to using the first order EKF. Future work may consider state multiplicative noise and state-dependent process noise.
A combined approach for anomaly detection in production systems using ML tech...za_slide
1) The document proposes using machine learning techniques like OTALA and PCA to automatically build models of production systems and detect anomalies in real-time without needing expert knowledge of the system's components.
2) It tests this approach on a "Demonstrator" production system, using OTALA to model normal behavior as a state machine and PCA to reduce data dimensions. It then detects anomalies by comparing new data to these models.
3) Experiments show this approach can accurately detect various introduced anomalies, though it cannot diagnose their specific causes. The models also allow monitoring system behavior in real-time.
Introduction to adaptive filtering and its applications.pptdebeshidutta2
This document discusses linear filters and adaptive filters. It provides an overview of key concepts such as:
- Linear filters have outputs that are linear functions of their inputs, while adaptive filters can adjust their parameters over time based on the input signals.
- The Wiener filter and LMS algorithm are introduced as approaches for optimal and adaptive filter design, with the LMS algorithm minimizing the mean square error using gradient descent.
- Applications of adaptive filters include system identification, inverse modeling, prediction, and interference cancellation. An example of acoustic echo cancellation is described.
- The document outlines the LMS adaptive algorithm steps and discusses its stability and convergence properties. It also summarizes different equalization techniques for mitigating inter
The document discusses using Particle Image Velocimetry (PIV) to measure track deflection. PIV uses cameras and image analysis software to calculate horizontal and vertical displacement of a target attached to the rail from video images. The method was tested at three sites in South Africa. Tests showed PIV can reliably measure deflections with a standard deviation of 0.003-0.005mm. PIV provides a quick way to evaluate track condition and stiffness without complex instrumentation.
This document discusses three methods for equalization in wideband TDMA systems: linear equalization, decision feedback equalization, and maximum likelihood sequence estimation using the Viterbi algorithm. Linear equalization methods like least mean square aim to minimize intersymbol interference but have limited performance. Decision feedback equalization has better performance than linear equalization by cancelling interference from previously decided symbols. Maximum likelihood sequence estimation using the Viterbi algorithm provides the best performance but highest complexity by estimating the most likely transmitted sequence. The document provides examples of equalizer structures and algorithms like LMS for adjusting filter coefficients to minimize intersymbol interference.
The document describes the principles of operation and first results of SMOS, a satellite mission to measure soil moisture and ocean salinity. It discusses the basic principles of synthetic aperture radiometry used by SMOS and describes the MIRAS instrument, including its array topology, receivers, digital correlator system, and calibration system. It also addresses instrument performance metrics like angular resolution and radiometric sensitivity. Lastly, it discusses image reconstruction algorithms and geolocalization of retrieval products.
@Powersupply(YeungnamUniv.) @NanheeKim @nh9k
질문이 있으면 언제든지 연락주세요!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
The document describes a seminar on Kalman filtering. It provides an outline of topics to be covered, including motivation, history, what a Kalman filter is, applications, advantages, how it works, criteria for estimators, the standard Kalman filter algorithm, an example of using it for linear systems, extending it to nonlinear systems using the extended Kalman filter algorithm, and another example applying it to a nonlinear system. It aims to introduce Kalman filtering, covering its development, methodology, uses, and implementation for both linear and nonlinear dynamic systems.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
Experimental Evaluation of a Novel Fast Beamsteering Algorithm for Link Re-Es...Avishek Patra
The millimeter-wave (mm-wave) bands are currently being explored for multi-Gbps wireless local area networks (WLANs). Directional antennas are required to overcome the high attenuation inherent at the mm-wave frequencies. However, directionality makes link maintenance and establishment tasks complex, especially under node mobility, as slight misalignment of antenna beams between nodes leads to link disruption. Consequently, low latency beamsteering algorithms are needed for fast link re-establishment to support seamless data provisioning. Solutions based on exhaustive sequential scanning induce high latency, thereby disrupting communication. On the other hand, existing low latency proposals typically consider only static links, depend on additional hardware, or require a priori information about the network environment. In this paper, we propose a generic, fast mm-wave beamsteering algorithm that utilizes the previous valid link information to initiate the feasible antenna sector pair search and adaptively increases the sector search space around it to re-establish a link. Additionally, we experimentally evaluate the performance of our algorithm through measurements conducted in a real indoor environment using 60 GHz packet radio transceivers. The results show that, compared to exhaustive sequential scanning, our algorithm reduces the required sector search space, and thereby the link re-establishment latency, by 89% on average compared to exhaustive sequential scanning.
The sampling theorem can be explained as follows:
1. According to the sampling theorem, a continuous-time signal x(t) that has no frequency components higher than B Hz can be perfectly reconstructed from its samples if it is sampled at a frequency fs that is greater than 2B samples/second. This minimum sampling frequency fs is called the Nyquist rate.
2. The sampling theorem states that for a bandlimited signal with maximum frequency B Hz, the signal must be sampled at a frequency fs that is greater than 2B samples/second in order to avoid aliasing and allow perfect reconstruction of the original continuous-time signal from the samples.
3. Aliasing occurs when the signal is sampled at a rate lower than
Presentation made by Prof. Adriano Camps (Universitat Politècnica de Catalunya) at ICMARS 2010 (India, 16-December-2010) on the MIRAS instrument aboard ESA's SMOS mission.
Dynamic Music Emotion Recognition Using State-Space Modelsmultimediaeval
This document describes research on dynamic music emotion recognition using state-space models. It focuses on two subtasks: feature development for static affect prediction and new modeling approaches for dynamic affect prediction. The researchers extract audio features from music clips and use Kalman filters and Gaussian process state-space models to model the affect trajectories over time. They train and evaluate the models on development and test datasets, finding that clustering the training data improves results and that Kalman filters and Gaussian process models perform similarly, with Gaussian processes slightly better for valence estimation. Baseline audio features also perform relatively well compared to other features.
This document summarizes a senior design project to create a sign language teaching glove. The goal was to develop a portable device that can accurately detect gestures and provide feedback to users learning sign language. The system included sensors to detect gestures, software to analyze the data using Kalman filtering and pattern recognition, and LEDs for feedback. While some components like the MPU-6050 motion sensors worked well, other parts like the flex sensors and Bluetooth module failed. The project achieved 75% accuracy in recognizing gestures but aims to improve performance by addressing current limitations.
Flight Dynamics Software Presentation Part I Version 5Antonios Arkas
This document describes an orbit determination simulator and its key features:
- It uses a weighted least-squares estimator to process range and angular tracking measurements from multiple Earth stations to determine orbital state. It can estimate parameters like reflectivity coefficient, ballistic coefficient, and antenna biases.
- It provides outputs like the determined orbit, validity metrics, covariance analysis, residuals graphs, and confidence ellipsoids. It can also propagate determined state covariance over time.
- The simulator was validated against another flight dynamics software by comparing results from processing real tracking data. Determined states and other parameters showed close agreement.
- Consider covariance analysis is performed to assess impact of neglected parameters like antenna biases. This is done through formal
Feedback systems are used to stabilize amplifiers and control systems. Negative feedback reduces the gain of an amplifier, making it less sensitive to variations and more stable. While feedback stabilizes systems, it can also cause oscillations if the loop gain exceeds 1. Researchers like Bode and Nyquist analyzed stability and developed criteria to determine when feedback amplifiers will become unstable. Oscillators are designed to produce stable oscillations by ensuring the feedback is positive. Frequency domain analysis using Fourier techniques can be used to analyze linear and time-invariant systems. Design specifications for control systems include parameters for time response, steady state error, and gain/phase margins.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Mais conteúdo relacionado
Semelhante a kalman filter illustrated with 2D example
This document describes an adaptive Kalman filter implementation for video denoising. It proposes processing video frames independently in the spatial domain and then applying an adaptive temporal Kalman filter to each pixel sequence to reduce complexity. An adaptive Kalman filter is used which can adjust its parameters based on noise statistics variations and detected motions between frames. The algorithm is tested through MATLAB simulation on sample video frames, showing it produces a denoised output with reduced noise while still responding to changes in pixel values over time. The design considerations for FPGA implementation focus on using fixed-point arithmetic and shift operations instead of division to optimize for the FPGA hardware.
07 image filtering of colored noise based on kalman filterstudymate
This document summarizes a research paper on using a Kalman filter to improve the accuracy of vehicle tracking based on GPS data. It describes how the Kalman filter works as a linear recursive technique to estimate the true state of a dynamic system by reducing noise. The document outlines the mathematical model of the Kalman filter and how it is applied to predict and correct vehicle position over time. It also discusses tuning the Kalman filter parameters like process noise covariance Q and measurement noise covariance R. Evaluation of GPS data collected from vehicle tests shows the Kalman filter reduces errors in latitude, longitude and altitude compared to not using the filter.
This document summarizes two channel estimation methods for MIMO-OFDM systems: blind channel estimation and QRD-M/Kalman filter based detection. Blind channel estimation works by identifying the channel based on knowledge of the channel and data symbols using noise subspace approach and linear precoding. It has fast convergence, requires few OFDM symbols, and can be used with any number of transmit/receive antennas. QRD-M/Kalman filter based detection uses an adaptive complexity QRD-M algorithm and Kalman filters to track individual channels with lower complexity and good tracking ability. It decomposes the received signal into an upper triangular matrix and uses maximum likelihood detection on individual subcarriers. Both methods are analyzed and their advantages/dis
The document discusses built-in self-testing (BIST) for testing integrated circuits. BIST uses on-chip pattern generators and response compactors to test circuits without needing expensive external automatic test equipment. It reduces costs associated with test generation, storage, application and diagnosis. The document covers BIST architectures, linear feedback shift registers for pseudo-random pattern generation, response compaction, and fault coverage analysis of BIST.
This document summarizes a project presentation on using a second order extended Kalman filter (EKF) for state estimation of nonlinear dynamical systems. It describes how the first order EKF approximations do not always hold for highly nonlinear trajectories or high noise. The project tests a quadcopter model with nonlinear dynamics and high process and measurement noise using a second order EKF versus a first order EKF. The results show the second order EKF estimates remain closer to the true states. Using the second order EKF for state estimation in the control loop also results in trajectories closer to the optimal compared to using the first order EKF. Future work may consider state multiplicative noise and state-dependent process noise.
A combined approach for anomaly detection in production systems using ML tech...za_slide
1) The document proposes using machine learning techniques like OTALA and PCA to automatically build models of production systems and detect anomalies in real-time without needing expert knowledge of the system's components.
2) It tests this approach on a "Demonstrator" production system, using OTALA to model normal behavior as a state machine and PCA to reduce data dimensions. It then detects anomalies by comparing new data to these models.
3) Experiments show this approach can accurately detect various introduced anomalies, though it cannot diagnose their specific causes. The models also allow monitoring system behavior in real-time.
Introduction to adaptive filtering and its applications.pptdebeshidutta2
This document discusses linear filters and adaptive filters. It provides an overview of key concepts such as:
- Linear filters have outputs that are linear functions of their inputs, while adaptive filters can adjust their parameters over time based on the input signals.
- The Wiener filter and LMS algorithm are introduced as approaches for optimal and adaptive filter design, with the LMS algorithm minimizing the mean square error using gradient descent.
- Applications of adaptive filters include system identification, inverse modeling, prediction, and interference cancellation. An example of acoustic echo cancellation is described.
- The document outlines the LMS adaptive algorithm steps and discusses its stability and convergence properties. It also summarizes different equalization techniques for mitigating inter
The document discusses using Particle Image Velocimetry (PIV) to measure track deflection. PIV uses cameras and image analysis software to calculate horizontal and vertical displacement of a target attached to the rail from video images. The method was tested at three sites in South Africa. Tests showed PIV can reliably measure deflections with a standard deviation of 0.003-0.005mm. PIV provides a quick way to evaluate track condition and stiffness without complex instrumentation.
This document discusses three methods for equalization in wideband TDMA systems: linear equalization, decision feedback equalization, and maximum likelihood sequence estimation using the Viterbi algorithm. Linear equalization methods like least mean square aim to minimize intersymbol interference but have limited performance. Decision feedback equalization has better performance than linear equalization by cancelling interference from previously decided symbols. Maximum likelihood sequence estimation using the Viterbi algorithm provides the best performance but highest complexity by estimating the most likely transmitted sequence. The document provides examples of equalizer structures and algorithms like LMS for adjusting filter coefficients to minimize intersymbol interference.
The document describes the principles of operation and first results of SMOS, a satellite mission to measure soil moisture and ocean salinity. It discusses the basic principles of synthetic aperture radiometry used by SMOS and describes the MIRAS instrument, including its array topology, receivers, digital correlator system, and calibration system. It also addresses instrument performance metrics like angular resolution and radiometric sensitivity. Lastly, it discusses image reconstruction algorithms and geolocalization of retrieval products.
@Powersupply(YeungnamUniv.) @NanheeKim @nh9k
질문이 있으면 언제든지 연락주세요!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
The document describes a seminar on Kalman filtering. It provides an outline of topics to be covered, including motivation, history, what a Kalman filter is, applications, advantages, how it works, criteria for estimators, the standard Kalman filter algorithm, an example of using it for linear systems, extending it to nonlinear systems using the extended Kalman filter algorithm, and another example applying it to a nonlinear system. It aims to introduce Kalman filtering, covering its development, methodology, uses, and implementation for both linear and nonlinear dynamic systems.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
Experimental Evaluation of a Novel Fast Beamsteering Algorithm for Link Re-Es...Avishek Patra
The millimeter-wave (mm-wave) bands are currently being explored for multi-Gbps wireless local area networks (WLANs). Directional antennas are required to overcome the high attenuation inherent at the mm-wave frequencies. However, directionality makes link maintenance and establishment tasks complex, especially under node mobility, as slight misalignment of antenna beams between nodes leads to link disruption. Consequently, low latency beamsteering algorithms are needed for fast link re-establishment to support seamless data provisioning. Solutions based on exhaustive sequential scanning induce high latency, thereby disrupting communication. On the other hand, existing low latency proposals typically consider only static links, depend on additional hardware, or require a priori information about the network environment. In this paper, we propose a generic, fast mm-wave beamsteering algorithm that utilizes the previous valid link information to initiate the feasible antenna sector pair search and adaptively increases the sector search space around it to re-establish a link. Additionally, we experimentally evaluate the performance of our algorithm through measurements conducted in a real indoor environment using 60 GHz packet radio transceivers. The results show that, compared to exhaustive sequential scanning, our algorithm reduces the required sector search space, and thereby the link re-establishment latency, by 89% on average compared to exhaustive sequential scanning.
The sampling theorem can be explained as follows:
1. According to the sampling theorem, a continuous-time signal x(t) that has no frequency components higher than B Hz can be perfectly reconstructed from its samples if it is sampled at a frequency fs that is greater than 2B samples/second. This minimum sampling frequency fs is called the Nyquist rate.
2. The sampling theorem states that for a bandlimited signal with maximum frequency B Hz, the signal must be sampled at a frequency fs that is greater than 2B samples/second in order to avoid aliasing and allow perfect reconstruction of the original continuous-time signal from the samples.
3. Aliasing occurs when the signal is sampled at a rate lower than
Presentation made by Prof. Adriano Camps (Universitat Politècnica de Catalunya) at ICMARS 2010 (India, 16-December-2010) on the MIRAS instrument aboard ESA's SMOS mission.
Dynamic Music Emotion Recognition Using State-Space Modelsmultimediaeval
This document describes research on dynamic music emotion recognition using state-space models. It focuses on two subtasks: feature development for static affect prediction and new modeling approaches for dynamic affect prediction. The researchers extract audio features from music clips and use Kalman filters and Gaussian process state-space models to model the affect trajectories over time. They train and evaluate the models on development and test datasets, finding that clustering the training data improves results and that Kalman filters and Gaussian process models perform similarly, with Gaussian processes slightly better for valence estimation. Baseline audio features also perform relatively well compared to other features.
This document summarizes a senior design project to create a sign language teaching glove. The goal was to develop a portable device that can accurately detect gestures and provide feedback to users learning sign language. The system included sensors to detect gestures, software to analyze the data using Kalman filtering and pattern recognition, and LEDs for feedback. While some components like the MPU-6050 motion sensors worked well, other parts like the flex sensors and Bluetooth module failed. The project achieved 75% accuracy in recognizing gestures but aims to improve performance by addressing current limitations.
Flight Dynamics Software Presentation Part I Version 5Antonios Arkas
This document describes an orbit determination simulator and its key features:
- It uses a weighted least-squares estimator to process range and angular tracking measurements from multiple Earth stations to determine orbital state. It can estimate parameters like reflectivity coefficient, ballistic coefficient, and antenna biases.
- It provides outputs like the determined orbit, validity metrics, covariance analysis, residuals graphs, and confidence ellipsoids. It can also propagate determined state covariance over time.
- The simulator was validated against another flight dynamics software by comparing results from processing real tracking data. Determined states and other parameters showed close agreement.
- Consider covariance analysis is performed to assess impact of neglected parameters like antenna biases. This is done through formal
Feedback systems are used to stabilize amplifiers and control systems. Negative feedback reduces the gain of an amplifier, making it less sensitive to variations and more stable. While feedback stabilizes systems, it can also cause oscillations if the loop gain exceeds 1. Researchers like Bode and Nyquist analyzed stability and developed criteria to determine when feedback amplifiers will become unstable. Oscillators are designed to produce stable oscillations by ensuring the feedback is positive. Frequency domain analysis using Fourier techniques can be used to analyze linear and time-invariant systems. Design specifications for control systems include parameters for time response, steady state error, and gain/phase margins.
Semelhante a kalman filter illustrated with 2D example (20)
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
6. When and where?
Tracking and navigation
– Tracking missiles, aircrafts and spacecrafts
– GPS technology
– Visual reality
Tracking in
HEP experiments
7. KF assumptions
Linear system
– System parameters are linear
function of parameters at
some previous time
– Measurements are linear
function of parameters
White Gaussian noise
– White: uncorrelated in time
– Gaussian: noise amplitude
KF is the
optimal filter
9. KF description: example
System parameters: v
System model:
linear motion y = k x
vi = A vi - 1
Measurement model:
m i = H vi
1
i
k
y
1
0
Δx
1
i
k
y
i
k
y
0
1
i
m
i
k
y
i
v
10. Noise
Noise: e
Noise covariance matrix
System noise: vi = A vi – 1+ qi Q = E(qqT)
Measurement noise: m i = H vi + ri
R = E(rrT)
)
e
E(e
)
e
E(e
)
e
E(e
)
e
E(e
)
E(ee
V 2
2
1
2
2
1
1
1
T
Gaussian E(e2) = σ2
11. KF algorithm
Prediction: vi
- = A vi – 1
Correction: vi = vi
- + K (mi – H vi
-)
vi = A vi – 1 + q
m i = H vi + r
v
^
v KF
Kalman gain matrix
minimize the difference v - v
^
^
^ ^ ^
^
12. Kalman gain matrix
It is easy to show
K = V-HT (H V-HT +R)-1,
where Vi
- = AVi-1AT + Q
Minimize the expected error
Limits:
– system noise << measurement noise vi = vi
–
– system noise >> measurement noise vi = H–1 mi
0
K
V
)
e
E(e
)
e
E(e
)
e
E(e
)
e
E(e
)
E(ee
V
;
v
v
e
ab
ij
2
2
1
2
2
1
1
1
T
^ ^
^
13. Error on parameters
Predictor: Vi
- = AVi-1AT + Q
– Q: system noise
Corrector: Vi = (I - KH) Vi
-
– error reduced
16. Matrix description of system state, model
and measurement
Progressive method
Proper dealing with noise
KF overview
prediction
correction
17. Application to particle tracking
Detector:
– Silicon vertex detector
– Central drift
chamber
Description
of track:
5 parameters
18. Advantages of using KF
in particle tracking
Progressive method
– No large matrices has to be inverted
Proper dealing with system noise
Track finding and track fitting
Detection of outliers
Merging track from different segments
19. Modifications of KF
(!) Non - linear
system extended
Kalman filter
Full precision only
after the last step
– Prediction
– Correction
– Smoothing
Kalman filter
Least squares
x
y
20. Conclusion
We have demonstrated the principles
predictor – corrector method
combining model and measurement
Very useful in tracking
For given assumptions, KF is the optimal filter
Extensions for non-linear systems
Extensive application
To sum up…
21. Tracking in BELLE detector
Track finding
Track fitting
Track managing
22. Notation overview
v: vector of parameters
– v: our estimation
– v-: predicted value
m: vector of measurements
A: matrix describing linear system vi = A vi – 1
H: matrix describing measurements m i = H vi
V: error (on parameter) covariance matrix
Q: system noise covariance matrix
R: measurement noise covariance matrix
K: Kalman gain matrix
^