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Pattern Recognition with Fuzzy Objective Function Algorithms

Fundamentals of Satellite Navigation. GPS Systems Segments. GPS Satellite Signal Characteristics and Message Formats. Satellite Signal Acquisitions and Tracking. Effects of RF Interference on GPS Satellite Signal Receiver Tracking. Performance of Standalone GPS. Differential GPS. Integration of GPS with other Sensors. Galileo. The Russian GLONASS, Chinese Bediou, and Japanese QZSS Systems. GNSS Markets and Applications.

Understanding GPS : principles and applications

This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Detection of abrupt changes: theory and application

This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal. >

Unsupervised optimal fuzzy clustering

From the Publisher:
Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.

Fuzzy Modeling for Control

A class of fuzzy ISODATA clustering algorithms has been developed previously which includes fuzzy means. This class of algorithms is generalized to include fuzzy covariances. The resulting algorithm closely resembles maximum likelihood estimation of mixture densities. It is argued that use of fuzzy covariances is a natural approach to fuzzy clustering. Experimental results are presented which indicate that more accurate clustering may be obtained by using fuzzy covariances.

Fuzzy clustering with a fuzzy covariance matrix

The problem of detection and identification of cardiac transient rhythms, using the associated R-R interval sequence, is studied. A generalized likelihood ratio technique is proposed, in which the transient rhythm category is identified by means of a maximum-likelihood hypothesis test. Simultaneously, the magnitude of the change in the R-R interval pattern is estimated. The method is easily mechanized on-line using a moving window of data and prestored gains. Experimental results using actual data are presented to indicate the utility of the method.

ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms

A wireless navigational system and process utilize multipath wireless signals normally discarded from the navigation solution to enhance navigational performance under a range of operational scenarios. A relationship between the receiver's location and one or more multipath signals is established, facilitating subsequent navigation using the multipath signal(s).

Geographical navigation using multipath wireless navigation signals

Systems and methods for mapping a structure detect wireless signals, including at least one multipath signal that has experienced at least one reflection against a portion of the structure prior to the detection. The wireless signals are analyzed to estimate reflection points for the multipath signal(s), and a map of at least the portion of the structure is generated based on the estimated reflection points.

Systems and methods for transparency mapping using multipath signals

Incorrect landmark and loop closure measurements can cause standard SLAM algorithms to fail catastrophically. Recently, several SLAM algorithms have been proposed that are robust to loop closure errors, but it is shown in this paper that they cannot provide robust solutions when landmark measurement errors occur. The root cause of this problem is that the robust SLAM algorithms only focus on generating solutions that are locally consistent (i.e. each measurement agrees with its corresponding estimates) rather than globally consistent (i.e. all of the measurements in the solution agree with each other). Moreover, these algorithms do not attempt to maximize the number of correct measurements included in the solution, meaning that often correct measurements are ignored and the solution quality suffers as a result. This paper proposes a new formulation of the robust SLAM problem that seeks a globally consistent map that also maximizes the number of measurements included in the solution. In addition, a novel incremental SLAM algorithm, called incremental SLAM with consistency-checking, is developed to solve the new robust SLAM problem. Finally, simulated and experimental results show that the new algorithm significantly outperforms state-of-the-art robust SLAM methods for datasets with incorrect landmark measurements and can match their performance for datasets with incorrect loop closures.

/pdf/robust-incremental-slam-with-consistency-checking-3kc7zbmc7a.pdf

Donald E. Gustafson

Papers

Fuzzy clustering with a fuzzy covariance matrix

ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms

Geographical navigation using multipath wireless navigation signals

Systems and methods for transparency mapping using multipath signals

Robust incremental SLAM with consistency-checking