Introduction and Implementations of the Kalman Filter最新文献

Novel Direct and Accurate Identification of Kalman Filter for General Systems Described by a Box-Jenkins Model 基于Box-Jenkins模型的通用系统卡尔曼滤波直接准确辨识方法

Introduction and Implementations of the Kalman Filter Pub Date : 2019-04-30 DOI: 10.5772/INTECHOPEN.81793

R. Doraiswami, L. Cheded

{"title":"Novel Direct and Accurate Identification of Kalman Filter for General Systems Described by a Box-Jenkins Model","authors":"R. Doraiswami, L. Cheded","doi":"10.5772/INTECHOPEN.81793","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81793","url":null,"abstract":"A novel robust Kalman filter (KF)-based controller is proposed for a multivari-able system to accurately track a specified trajectory under unknown stochastic disturbance and measurement noise. The output is a sum of uncorrelated signal, disturbance and measurement noise. The system model is observable but not controllable while the signal one is controllable and observable. An emulator-based two-stage identification is employed to obtain a robust model needed to design the robust controller. The system and KF are identified and the signal and output error estimated. From the identified models, minimal realizations of the signal and KF, the disturbance model and whitening filter are obtained using balanced model reduction techniques. It is shown that the signal model is a transfer matrix relating the system output and the KF residual, and the residual is the whitened output error. The disturbance model is identified by inverse filtering. A feedback-feedforward controller is designed and implemented using an internal model of the reference driven by the error between the reference and the signal estimate, the feedforward of reference and output error. The successful evaluation of the proposed scheme on a simulated autonomously-guided drone gives ample encouragement to test it later, on a real one.","PeriodicalId":231373,"journal":{"name":"Introduction and Implementations of the Kalman Filter","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121653973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Statically Fused Converted Measurement Kalman Filters 静态融合转换测量卡尔曼滤波器

Introduction and Implementations of the Kalman Filter Pub Date : 2019-04-09 DOI: 10.5772/INTECHOPEN.85711

Gongjian Zhou, Zhengkun Guo

引用次数: 1

Introductory Chapter: Kalman Filter - The Working Horse of Object Tracking Systems Nowadays 导论章:卡尔曼滤波——当今目标跟踪系统的主力

Introduction and Implementations of the Kalman Filter Pub Date : 2019-03-07 DOI: 10.5772/INTECHOPEN.84804

Felix Goavers

引用次数: 0

Tuning of the Kalman Filter Using Constant Gains 用恒定增益调谐卡尔曼滤波器

Introduction and Implementations of the Kalman Filter Pub Date : 2018-11-23 DOI: 10.5772/INTECHOPEN.81795

M. Ananthasayanam

{"title":"Tuning of the Kalman Filter Using Constant Gains","authors":"M. Ananthasayanam","doi":"10.5772/INTECHOPEN.81795","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81795","url":null,"abstract":"For designing an optimal Kalman filter, it is necessary to specify the statistics, namely the initial state, its covariance and the process and measurement noise covariances. These can be chosen by minimising some suitable cost function J . This has been very difficult till recently when a near optimal Recurrence Reference Recipe ( RRR ) was proposed without any optimisation but only filtering. In many filter applications after the initial transients, the gain matrix K tends to a constant during the steady state, which points to design the filter based on constant gains alone. Such a constant gain Kalman filter ( CGKF ) can be designed by minimising any suitable cost function. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. Though CGKF results may not be too close to those of RRR, they are acceptable. It accepts extremely simple models and the gains are robust in handling similar scenarios. In this chapter, we provide examples of applying the CGKF by ancient Indian astronomers, parameter estimation of spring, mass and damper system, airplane real flight test data, ballistic rocket, re-entry of space object and the evolution of space debris.","PeriodicalId":231373,"journal":{"name":"Introduction and Implementations of the Kalman Filter","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133255330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Scalable, FPGA-Based Implementation of the Unscented Kalman Filter 无气味卡尔曼滤波器的可扩展fpga实现

Introduction and Implementations of the Kalman Filter Pub Date : 2018-11-05 DOI: 10.5772/INTECHOPEN.80681

Jeremy Soh, Xiaofeng Wu

{"title":"A Scalable, FPGA-Based Implementation of the Unscented Kalman Filter","authors":"Jeremy Soh, Xiaofeng Wu","doi":"10.5772/INTECHOPEN.80681","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.80681","url":null,"abstract":"Autonomous aerospace systems may well soon become ubiquitous pending an increase in autonomous capability. Greater autonomous capability means there is a need for high-performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a field-programmable gate array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the unscented Kalman filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to simplify the development process, and a non-application-specific part, implemented in hardware as a parameterisable ‘ black box ’ module (i.e. IP core) to increase performance. Simulation results demonstrating a possible nanosatellite application of the design are presented; implementation (synthesis, timing, power) details are also presented.","PeriodicalId":231373,"journal":{"name":"Introduction and Implementations of the Kalman Filter","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121157917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Introduction to Kalman Filter and Its Applications 卡尔曼滤波及其应用简介

Introduction and Implementations of the Kalman Filter Pub Date : 2018-11-05 DOI: 10.5772/INTECHOPEN.80600

Youngjoo Kim, H. Bang

引用次数: 126