Robust recursive estimation in the presence of heavy-tailed observation noise

Includes bibliographical references (p. 33-41).Supported by the U.S. Army Research Office fellowship. ARO-DAAL03-86-G-0017 Supported by the U.S. Air Force Office of Scientific Research. AFOSR-85-0227 AFOSR-89-0276Irvin C. Schick and Sanjoy K. Mitter

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Detection in non gaussian environment: various approaches and use of mixture models

Most of the signal processing methods, especially in detection/estimation theory, are based on a Gaussian noise probability density function (PDF). As real noises are not usually Gaussian, a good performance can not be obtained by assuming a Gaussian probability density. This paper can be viewed as a survey of some alternatives when the noise is known to be non-Gaussian, and even non-stationnary or imperfectly known . The emphasis is on a noise PDF modeled as a mixture, this PDF being sum of two or more elementary density functions. This representation, used in minimax robustness and adaptive methods, is particularly suitable for impulsive noise model and for the case of uncertainties on the noise PDF . Some applications in underwater acoustics are given.Les bruits réels ne suivent que rarement la loi gaussienne; de bonnes performances ne peuvent être obtenues qu'en s'affranchissant de l'hypothèse gaussienne. On expose quelques méthodes de prise en compte du décalage entre la loi réelle et la loi gaussienne. On insiste sur l'importance de la représentation de la loi du bruit par un modèle de mixture, la densité de probabilité étant la somme de deux ou plusieurs densités élémentaire

Planiranje eksperimenta za robusnu identifikaciju dinamičkih sistema

Primenom principa crne kutije i teorija verovatnoće, stohastičkih procesa i matematičke statistike, uz korišćenje ulazno/izlaznih merenja razmatra se mogućnost dobijanja matematičkih modela. Okviri za dobijanje modela su opšti jer se pretpostavlja da su stohastički poremećaji negausovi. Takav model je preduslov za projektovanje široke klase industrijskih regulatora. Teorija planiranja eksperimenta ima važnu ulogu u povećanju brzine konvergencije rekurzivnih algoritama kao i u skraćenju vremena identifikacije. Povećana brzina konvergencije algoritama čini ih veoma povoljnim za praktičnu primenu. Ulazni signali za identifikaciju kreiraju se preko rekurzivne relacije za autokovarijacionu funkciju. Sinteza autokovarijacione funkcije zasnovana je na idejama iz prediktivnog upravljanja, pri čemu upravljački signal ima konačan alfabet. Praktična istraživanja pokazuju da poremećaj, u opštem slučaju, ima negausovu raspodelu. Posebno je važan slučaj kada se pojave opservacije koje su nekonzistentne u odnosu na glavninu populacije, autlajeri (outliers). Raspodele verovatnoće za taj slučaj su približno normalne (e -kontaminirane) i predmet su intenzivnog proučavanja u matematičkoj statistici. Za takav slučaj se predlažu robusni algoritmi identifikacije, pri čemu robusnost ima statistički karakter. Razmatra se primena robusnog Kalmanovog filtra u identifikaciji modela zasnovanih na grešci izlaza. Robusni prošireni Kalmanov filtar se koristi za identifikaciju opšte forme nelinearnog modela u prostoru stanja. Identifikacija procesa opisanih opštim modelom (nepoznati parametri i stanja procesa) zahteva uvođenje proširenog Masreljez-Martinovog filtra. Uvođenjem predloženih heurističkih modifikacija povećava se fleksibilnost, u smislu praktične primene kao i brzine konvergencije robusnog filtra. Prikazana je nadmoćnost predloženih robusnih algoritama u identifikaciji sistema sa vremenski promenljivim parametrima, koji zasnovani na OE klasi modela. Praktični aspekt dobijenih rezultata potvrđen je kroz eksperiment na pneumatskom cilindru koji se nalazi u laboratoriji centra za Automatsko upravljanje i fluidnu tehniku Fakulteta za mašinstvo i građevinarstvo u Kraljevu.By applying the principles of black boxes and probability theory, stochastic processes and mathematical statistics, with the use of input / output measurements, the possibility of obtaining mathematical models is considered. Frames for obtaining the model are general because it is assumed that the stochastic disturbances are non-Gaussian. Such a model is a prerequisite for the design of wide range of industrial controlers. The theory of experiment design plays an important role in increasing the speed of convergence of recursive algorithms as well as shortening the time of identification. Increased speed of convergence of algorithms makes them very favorable for practical application. The input signals for identification are created through the recursive relation for autocovariance. Design of autocovariance is based on the idea of predictive control, where the control signal has a finite alphabet. Practical studies show that disturbances, in general, have non-Gaussian distribution. Particularly important is the case when there are observations that are inconsistent with respect to the majority of population (outliers). Probability distribution for this case is approximately normal (e -contaminated) and is the subject of intensive study in mathematical statistics. In such case, the robust algorithms for identification, where robustness has a statistical nature, are proposed. It is considered the application of robust Kalman filter in identification of output error model. Robust extended Kalman filter is used for identification of the general form of the nonlinear statespace model. Identification of the processes described by general model (the unknown parameters and states of the process) requires the introduction of extended Masreliez-Martin's filter. By introducing of the proposed heuristic modification increases the flexibility in terms of practical application and the speed of convergence of the robust filter. The superiority of the proposed robust algorithms for system identification with timevarying parameters, which are based on OE models, has been shown. The practical aspects of the results have been confirmed by experiment on a pneumatic cylinder, which is located in the laboratory of the centre for automatic control and fluid technique of The Faculty of Mechanical and Civil Engineering in Kraljevo

Sensors, measurement fusion and missile trajectory optimisation

When considering advances in “smart” weapons it is clear that air-launched systems have adopted an integrated approach to meet rigorous requirements, whereas air-defence systems have not. The demands on sensors, state observation, missile guidance, and simulation for air-defence is the subject of this research. Historical reviews for each topic, justification of favoured techniques and algorithms are provided, using a nomenclature developed to unify these disciplines. Sensors selected for their enduring impact on future systems are described and simulation models provided. Complex internal systems are reduced to simpler models capable of replicating dominant features, particularly those that adversely effect state observers. Of the state observer architectures considered, a distributed system comprising ground based target and own-missile tracking, data up-link, and on-board missile measurement and track fusion is the natural choice for air-defence. An IMM is used to process radar measurements, combining the estimates from filters with different target dynamics. The remote missile state observer combines up-linked target tracks and missile plots with IMU and seeker data to provide optimal guidance information. The performance of traditional PN and CLOS missile guidance is the basis against which on-line trajectory optimisation is judged. Enhanced guidance laws are presented that demand more from the state observers, stressing the importance of time-to-go and transport delays in strap-down systems employing staring array technology. Algorithms for solving the guidance twopoint boundary value problems created from the missile state observer output using gradient projection in function space are presented. A simulation integrating these aspects was developed whose infrastructure, capable of supporting any dynamical model, is described in the air-defence context. MBDA have extended this work creating the Aircraft and Missile Integration Simulation (AMIS) for integrating different launchers and missiles. The maturity of the AMIS makes it a tool for developing pre-launch algorithms for modern air-launched missiles from modern military aircraft.EThOS - Electronic Theses Online ServiceGBUnited Kingdo