## Robust Kalman filtering for signals and systems with large uncertainties

The book is intended for researchers in robust control and Read more. The book is intended for researchers in robust control and filtering theory, advanced postgraduate students, and engineers with an interest in applying the latest techniques of robust Kalman filtering. Robust Kalman filtering extends the Kalman filtering and the extended Kalman filtering to systems that contain uncertain parameters in addition to the usual white Gaussian noise Several examples are given, showing the robust Kalman filters outperforming the regular Kalman filter or the extended Kalman filter.

Each of the first ten chapters covers a specific topic, usually with a major theorem characterizing the robust filter followed by an example. The final chapter addresses its application to a particular problem. All rights reserved. Your email. For example, consider an object tracking scenario where a stream of observations is the input, however, it is unknown how many objects are in the scene or, the number of objects is known but is greater than one.

A multiple hypothesis tracker MHT typically will form different track association hypotheses, where each hypothesis can be viewed as a Kalman filter in the linear Gaussian case with a specific set of parameters associated with the hypothesized object. Thus, it is important to compute the likelihood of the observations for the different hypotheses under consideration, such that the most-likely one can be found.

In the information filter, or inverse covariance filter, the estimated covariance and estimated state are replaced by the information matrix and information vector respectively. These are defined as:. The information update now becomes a trivial sum.

### Stanford Libraries

The main advantage of the information filter is that N measurements can be filtered at each timestep simply by summing their information matrices and vectors. To predict the information filter the information matrix and vector can be converted back to their state space equivalents, or alternatively the information space prediction can be used.

Note that if F and Q are time invariant these values can be cached. Note also that F and Q need to be invertible. This is also called "Kalman Smoothing". There are several smoothing algorithms in common use.

• Democracy and the Generals: The Problem of Civilian Control over the Military and Autonomous Generals in Fifth-century Athens M.A., California State University, Fresno, 2006.
• Selenium: Its Molecular Biology and Role in Human Health.
• Robust Kalman Filtering for Signals and Systems with Large Uncertainties | Ian Petersen | Springer.
• Injecting Illicit Drugs?
• Trouble Follows Me.

The forward pass is the same as the regular Kalman filter algorithm. We start at the last time step and proceed backwards in time using the following recursive equations:. The same notation applies to the covariance. The equations for the backward pass involve the recursive computation of data which are used at each observation time to compute the smoothed state and covariance. The smoothed state and covariance can then be found by substitution in the equations. An important advantage of the MBF is that it does not require finding the inverse of the covariance matrix.

## Kalman filter

The minimum-variance smoother can attain the best-possible error performance, provided that the models are linear, their parameters and the noise statistics are known precisely. The smoother calculations are done in two passes. The forward calculations involve a one-step-ahead predictor and are given by. The above system is known as the inverse Wiener-Hopf factor. The backward recursion is the adjoint of the above forward system.

In the case of output estimation, the smoothed estimate is given by. The above solutions minimize the variance of the output estimation error. Note that the Rauch—Tung—Striebel smoother derivation assumes that the underlying distributions are Gaussian, whereas the minimum-variance solutions do not. Optimal smoothers for state estimation and input estimation can be constructed similarly. A continuous-time version of the above smoother is described in.

Expectation-maximization algorithms may be employed to calculate approximate maximum likelihood estimates of unknown state-space parameters within minimum-variance filters and smoothers. Often uncertainties remain within problem assumptions. A smoother that accommodates uncertainties can be designed by adding a positive definite term to the Riccati equation. In cases where the models are nonlinear, step-wise linearizations may be within the minimum-variance filter and smoother recursions extended Kalman filtering.

http://chenpeilun.com/includes/35/pirater-imei-iphone.html

## Robust Kalman filtering for signals and systems with large uncertainties - CERN Document Server

Pioneering research on the perception of sounds at different frequencies was conducted by Fletcher and Munson in the s. Their work led to a standard way of weighting measured sound levels within investigations of industrial noise and hearing loss. Frequency weightings have since been used within filter and controller designs to manage performance within bands of interest.

Typically, a frequency shaping function is used to weight the average power of the error spectral density in a specified frequency band. The same technique can be applied to smoothers. The basic Kalman filter is limited to a linear assumption. More complex systems, however, can be nonlinear. The nonlinearity can be associated either with the process model or with the observation model or with both. In the extended Kalman filter EKF , the state transition and observation models need not be linear functions of the state but may instead be nonlinear functions.

These functions are of differentiable type. The function f can be used to compute the predicted state from the previous estimate and similarly the function h can be used to compute the predicted measurement from the predicted state. However, f and h cannot be applied to the covariance directly. Instead a matrix of partial derivatives the Jacobian is computed. At each timestep the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the nonlinear function around the current estimate.

The sigma points are then propagated through the nonlinear functions, from which a new mean and covariance estimate are then formed. The resulting filter depends on how the transformed statistics of the UT are calculated and which set of sigma points are used. It should be remarked that it is always possible to construct new UKFs in a consistent way. In addition, this technique removes the requirement to explicitly calculate Jacobians, which for complex functions can be a difficult task in itself i. The sigma points are propagated through the transition function f.

Additionally, the cross covariance matrix is also needed. The filter consists of two differential equations, one for the state estimate and one for the covariance:. The distinction between the prediction and update steps of discrete-time Kalman filtering does not exist in continuous time. The second differential equation, for the covariance, is an example of a Riccati equation. Most physical systems are represented as continuous-time models while discrete-time measurements are frequently taken for state estimation via a digital processor.

• Quest for Identity: America Since 1945.
• Recommended for you;
• Kalman filter - Wikipedia.

Therefore, the system model and measurement model are given by. The prediction equations are derived from those of continuous-time Kalman filter without update from measurements, i.

• Context Management for Distributed and Dynamic Context-Aware Computing.