Keith Edgar Brown

Short-time fractional Fourier methods for the time-frequency representation of chirp signals

The fractional Fourier transform (FrFT) provides a valuable tool for the analysis of linear chirp signals. This paper develops two short-time FrFT variants which are suited to the analysis of multicomponent and nonlinear chirp signals. Outputs have similar properties to the short-time Fourier transform (STFT) but show improved time-frequency resolution. The FrFT is a parameterized transform with parameter, a, related to chirp rate. The two short-time implementations differ in how the value of a is chosen. In the first, a global optimization procedure selects one value of a with reference to the entire signal. In the second, a values are selected independently for each windowed section. Comparative variance measures based on the Gaussian function are given and are shown to be consistent with the uncertainty principle in fractional domains. For appropriately chosen FrFT orders, the derived fractional domain uncertainty relationship is minimized for Gaussian windowed linear chirp signals. The two short-time FrFT algorithms have complementary strengths demonstrated by time-frequency representations for a multicomponent bat chirp, a highly nonlinear quadratic chirp, and an output pulse from a finite-difference sonar model with dispersive change. These representations illustrate the improvements obtained in using FrFT based algorithms compared to the STFT.

Bio-inspired wideband sonar signals based on observations of the bottlenose dolphin (Tursiops truncatus)

This paper uses advanced time-frequency signal analysis techniques to generate new models for bio-inspired sonar signals. The inspiration comes from the analysis of bottlenose dolphin clicks. These pulses are very short duration, between 50 and 80 micros, but for certain examples we can delineate a double down-chirp structure using fractional Fourier methods. The majority of clicks have energy distributed between two main frequency bands with the higher frequencies delayed in time by 5-20 micros. Signal syntheses using a multiple chirp model based on these observations are able to reproduce much of the spectral variation seen in earlier studies on natural dolphin echolocation pulses. Six synthetic signals are generated and used to drive the dolphin based sonar (DBS) developed through the Biosonar Program office at the SPAWAR Systems Center, San Diego, CA. Analyses of the detailed echo structure for these pulses ensonifying two solid copper spherical targets indicate differences in discriminatory potential between the signals. It is suggested that target discrimination could be improved through the transmission of a signal packet in which the chirp structure is varied between pulses. Evidence that dolphins may use such a strategy themselves comes from observations of variations in the transmissions of dolphins carrying out target detection and identification tasks.

Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

Fault diagnosis on autonomous robotic vehicles with RECOVERY: an integrated heterogeneous-knowledge approach

The need for embedding fault diagnosis into goal-orientated autonomous robotic vehicles for increased mission robustness is described. The RECOVERY system, a method for increasing the diagnostic capability by integrating commonly available heterogeneous knowledge is presented. Initial real-water results using the Ocean Systems Laboratorys RAUVER vehicle are given.

Analysis and classification of broadband echoes using bio-inspired dolphin pulses

To date most sonars use narrow band pulses and often only the echo envelope is used for object detection and classification. This paper considers the advantages afforded by bio-inspired sonar for object identification and classification through the analysis and the understanding of the broadband echo structure. Using the biomimetic dolphin based sonar system in conjunction with bio-inspired pulses developed from observations of bottlenose dolphins performing object identification tasks, results are presented from experiments carried out in a wave tank and harbor. In these experiments responses of various targets to two different bio-inspired signals are measured and analyzed. The differences in response demonstrate the strong dependency between signal design and echo interpretation. In the simulations and empirical data, the resonance phenomena of these targets cause strong notches and peaks in the echo spectra. With precision in the localization of these peaks and dips of around 1 kHz, the locations are very stable for broadside insonification of the targets and they can be used as features for classification. This leads to the proposal of a broadband classifier which operates by extracting the notch positions in the target echo spectra.

Principal features-based texture classification with neural networks

Abstract A texture image classification system is presented based upon the use of two cascaded multi-layer feedforward neural networks. The first network transforms a set of high-dimensional and correlated feature images into another set of dimensionally compressed, uncorrelated principal feature images whilst reducing computation effort and minimizing the information lost. The second accomplishes the task of feature pattern classification by using only those principal features obtained by the former. Such a cascaded use of neural networks significantly simplifies the structure of the classification network and increases the efficiency of the overall classification process. These are demonstrated by the comprehensive results obtained from practical applications of the system.

An integrated diagnostic architecture for autonomous underwater vehicles

The architecture of an advanced fault detection and diagnosis (FDD) system is described and applied with an Autonomous Underwater Vehicle (AUV). The architecture aims to provide a more capable system that does not require dedicated sensors for each fault, can diagnose previously unforeseen failures and failures with cause-effect patterns across different subsystems. It also lays the foundations for incipient fault detection and condition-based maintenance schemes. A model of relationships is used as an ontology to describe the connected set of electrical, mechanical, hydraulic, and computing components that make up the vehicle, down to the level of least replaceable unit in the field. The architecture uses a variety of domain dependent diagnostic tools (rulebase, model-based methods) and domain independent tools (correlator, topology analyzer, watcher) to first detect and then diagnose the location of faults. Tools nominate components, so that a rank order of most likely candidates can be generated. This modular approach allows existing proven FDD methods (e.g., vibration analysis, FMEA) to be incorporated and to add confidence to the conclusions. Illustrative performance is provided working in real time during deployments with the RAUVER hover capable AUV as an example of the class of automated system to which this approach is applicable.

Detecting faults in heterogeneous and dynamic systems using DSP and an agent-based architecture

This paper demonstrates the use of multi-agent systems (MAS), firstly as a modelling technique for dynamic physical systems and secondly as the basis for a generic and powerful diagnostic system, which can support heterogeneous distributed systems. First an overview of the diagnostic techniques including those offered by the two communities fault detection and isolation (FDI ) and DX (based on intelligent techniques) is given. The use of digital signal processing (DSP) as a significant technique for improved fault diagnosis is illustrated. A rule-based engine is used to control the behaviours of the agents and also as a tool for diagnosis. Finally, the integration of DSP agents and the rule-based engine into MAS is demonstrated using a real-life application, a class-AB amplifier (a power electronic circuit). It is shown that the integration of DSP agents and rules into MAS provides a powerful tool for prognosis and for detection of abrupt (short and open circuit) and incipient faults.

Selection of Wavelet for De-noising PD waveforms for Prognostics and Diagnostics of Aircraft Wiring

Electrical wiring and interconnected systems (EWIS) have become a major area of research and development for variety of industries. A variety of factors lead to catastrophic failure described. Here partial discharge (PD) analysis methods for diagnosing aircraft wiring faults are explored since PD signals are strongly correlated with the defects that produce them and form an ideal part for prognostic and diagnostic test system. A simulation of PD signal based on high-voltage insulation testing standard is detailed. Wavelet based (time-frequency) analysis is shown to be a good approach to de-noise PD signals. Leading to an overall set of conclusions and on-going work.

Real-time sidescan simulator and applications

New generations of sonars appeared in the last decade. The major interest in SAS systems and high frequency sonars is in the improvement of the sonar resolution and the reduction of noise level. Sonar images are distance-images but at high resolution they tends to appear visually as optical images. Usually the algorithms developed for sidescan were specific for sonar images due to the poor resolution essentially. With high resolution sonars, algorithms developed in the image processing field for natural images became applicable. In this paper we present a real-time and realistic sidescan simulator, and test image-based classification algorithms (such as PCA and eigenface algorithms) with synthetic images in order to characterize the precision necessary for these image-based algorithm to work.