This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar Measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical Cramer—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new Measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new Measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
observable distribution grid are investigated. A distribution
grid is observable if the state of the grid can be fully determined.
For the simulations, the modified 34-bus IEEE test feeder is used.
The Measurements needed for the state estimation are generated
by the ladder iterative technique. Two methods for the state
estimation are analyzed: Weighted Least Squares and Extended
Kalman Filter. Both estimators try to find the most probable
state based on the available Measurements. The result is that
the Kalman filter mostly needs less iterations and calculation
time. The disadvantage of the Kalman filter is that it needs some
foreknowlegde about the state.
In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different Measurements. Image registration is the process of transforming the different sets of data into one coordinate system. To be precise it involves finding transformations that relate spatial information conveyed in one image to that in another or in physical space. Image registration is performed on a series of at least two images, where one of these images is the reference image to which all the others will be registered. The other images are referred to as target images.
When trying to extract information from SAR images, we need to distinguish
two types of image property. The more important is where properties of the
scene (e.g., its dielectric constant, its geometry, its motion, etc.) produce effects
in the image Measurements or examination of the image then can provide
information about the scene. The second is generated purely by the system
and the signal processing.
The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy Measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory.
evc4 console app that monitors battery status and writes a csv log file and broadcasts every measurement. Use netcat on network to capture the Measurements.
Abstract—Stable direct and indirect decentralized adaptive radial basis
neural network controllers are presented for a class of interconnected
nonlinear systems. The feedback and adaptation mechanisms for each
subsystem depend only upon local Measurements to provide asymptotic
tracking of a reference trajectory. Due to the functional approximation
capabilities of radial basis neural networks, the dynamics for each
subsystem are not required to be linear in a set of unknown coeffi cients
as is typically required in decentralized adaptive schemes. In addition,
each subsystem is able to adaptively compensate for disturbances and
interconnections with unknown bounds.
