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A Survey of Maneuvering Target Tracking  Part V: MultipleModel Methods
, 2003
"... ... without addressing the socalled measurementorigin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surv ..."
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... without addressing the socalled measurementorigin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiplemodel methodsthe use of multiple models (and filters) simultaneouslywhich is the prevailing approach to maneuvering target tracking in the recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes on the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.
State Estimation for Hybrid Systems: Applications to Aircraft Tracking ∗
"... The problem of estimating the discrete and continuous state of a stochastic linear hybrid system, given only the continuous system output data, is studied. Well established techniques for hybrid estimation, known as the Multiple Model Adaptive Estimation algorithm, and the Interacting Multiple Mode ..."
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The problem of estimating the discrete and continuous state of a stochastic linear hybrid system, given only the continuous system output data, is studied. Well established techniques for hybrid estimation, known as the Multiple Model Adaptive Estimation algorithm, and the Interacting Multiple Model algorithm, are first reviewed. Conditions that must be satisfied in order to guarantee the convergence of these hybrid estimation algorithms are then presented. These conditions also provide a means to predict, as a function of the system parameters, which transitions in a hybrid system are relatively easy to detect. A new variant of hybrid estimation algorithms, called the ResidualMean Interacting Multiple Model (RMIMM) algorithm, is then proposed and analyzed. The performance of RMIMM is demonstrated through multimodal aircraft trajectory tracking examples. 1
Performance analysis of hybrid estimation algorithms
 Decision and Control  42nd IEEE Conference on, v.5
, 2003
"... Abstract — In this paper, we analyze the performance of estimation algorithms for discretetime stochastic linear hybrid systems. The problem of being able to estimate both the discrete and continuous states of a hybrid system given only the continuous output sequence is a difficult one, and while a ..."
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Abstract — In this paper, we analyze the performance of estimation algorithms for discretetime stochastic linear hybrid systems. The problem of being able to estimate both the discrete and continuous states of a hybrid system given only the continuous output sequence is a difficult one, and while algorithms [1], [2] exist for this purpose, little has been proved on the limitations of these algorithms, or even the dependence of their performance on system parameters. We find necessary conditions to guarantee the convergence of these hybrid estimation algorithms. We also derive expressions to determine bounds on the discrete mode detection delay. These conditions also provide a method to predict a priori which transitions in a hybrid system are relatively easy to detect, as a function of the system parameters. Finally, we validate our conditions and predictions using first a simple yet illustrative 1D example, and then a more complex aircraft tracking example. I.
Performance Analysis of Two Tracking Methods
, 1996
"... The performance of tracking algorithms often cannot be quantified before they are put to use without recourse to Monte Carlo simulations. This paper describes a performance measure called the "probability of correct association" (PCA) to quantify the performance of two simple tracking meth ..."
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The performance of tracking algorithms often cannot be quantified before they are put to use without recourse to Monte Carlo simulations. This paper describes a performance measure called the "probability of correct association" (PCA) to quantify the performance of two simple tracking methods in the presence of clutter. The PCA is derived theoretically on the basis of a probabilistic model which describes object motion and clutter distribution. The validity of the theoretical values is demonstrated by showing that they agree closely with results obtained by simulation. 1 Introduction The process of tracking involves following the position of a moving object over a period of time. The performance of a tracking system is often degraded by the presence of nontarget objects, known as clutter. In practice, the extent to which the performance of a system deteriorates because of clutter often cannot be quantified before it is put to use without recourse to expensive Monte Carlo simulations....
MOVINGBANK MULTIPLE MODEL ADAPTIVE ESTIMATION APPLIED TO FLEXIBLE SPACESTRUCTURE CONTROL
, 1987
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STATISTICAL FILTERING* by
, 1977
"... Filters were once understood to be simply contrivances for freeing liquids from suspended impurities by passing them through sand or charcoal. Today, with ultraviolet filrers on our cameras, and electrical signal filters in our radios we interpret the word ..."
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Filters were once understood to be simply contrivances for freeing liquids from suspended impurities by passing them through sand or charcoal. Today, with ultraviolet filrers on our cameras, and electrical signal filters in our radios we interpret the word
Partition Algorithm for System Identification
, 2001
"... We review the fundamentals of filtering theory and of the Lainiotis Partition Algorithm for system identification. Original reults are presented on the question of convergence of the Lainiotis algorithm; namely convergence is proved (within the algorithm's resolution) as long as certain exponen ..."
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We review the fundamentals of filtering theory and of the Lainiotis Partition Algorithm for system identification. Original reults are presented on the question of convergence of the Lainiotis algorithm; namely convergence is proved (within the algorithm's resolution) as long as certain exponential expectation conditions are satisfied. The algorithm is then applied to the identification of siumlated systems as well the identification of a real DC motor. Contents 1
SEC1, PTY C SSIFICAT;ON OF TjIS PAGE (Wen Data Entered) READ INSTRUCTIONS REPORT DOCUMENTATION PAGE BEFORE COMPLETING FORM
"... LThis document has been pgroved 1 fcr public r lease and saile ' L E C T E distribtion is unimiited~FESB24 19D ..."
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LThis document has been pgroved 1 fcr public r lease and saile ' L E C T E distribtion is unimiited~FESB24 19D