Adaptive mixture approximation for target tracking in clutter

Target tracking represents a state estimation problem recurrent in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, the vast majority of the existing tracking algorithms have to deal with association hypotheses which can grow in the number over time; in that case, the posterior state distribution can become computationally intractable and approximations have to be introduced. In this work, the impact of the number of hypotheses and corresponding reductions is investigated both in terms of employed computational resources and tracking performances. For this purpose, a recently developed adaptive mixture model reduction algorithm is considered in order to assess its performances when applied to the problem of single object tracking in the presence of clutter and to provide additional insights on the addressed problem

Internally positive representations and stability analysis of linear delay systems with multiple time-varying delays

This chapter introduces the Internally Positive Representation of linear systems with multiple time-varying state delays. The technique, previously introduced for the undelayed case, aims at building a positive representation of systems whose dynamics is, in general, indefinite in sign. As a consequence, stability criteria for positive time-delay systems can be exploited to analyze the stability of the original system. As a result, an easy-to-check sufficient condition for the delay-independent stability is provided, that is compared with some well known conditions available in the literature

Polynomial Approach for Filtering and Identification of a Class of Uncertain Systems

Abstract this paper considers the filtering and identification problems for a class of discrete-time uncertain stochastic systems that admit a finite number of linear working modes. It is shown here that this class of uncertain systems can be modeled by using a suitably defined extended system, whose state evolves according to a bilinear model. A polynomial filtering algorithm is derived for such extended system, which readily provides the polynomial estimates of both the original state and the working mode. Simulations show the effectiveness of the proposed approach and the improvements with respect to standard linear filtering algorithms

DISTRIBUTED-DELAY MODELS OF THE GLUCOSE-INSULIN HOMEOSTASIS AND ASYMPTOTIC STATE OBSERVATION

Abstract In this paper the problem of the real-time reconstruction of plasma insulin concentration by using only blood glucose measurements is investigated. This is an interesting problem because the knowledge of the time course of the glucose and insulin concentrations in an individual provides precious informations concerning its health state, and may assume the role of a clinical instrument. For the purpose of the reconstruction of the insulinemia a dynamical model of the glucose-insuline homeostasis is required. The present work considers distributed delay models. Such models have been preferred in recent papers with respect to the standard Minimal Models, available in literature from 70's, because they allow to couple the glucose and insulin dynamics in a unique extended system, whose solutions have been proven to be positive, bounded, and globally asymptotically stable around the basal values of the equilibrium point. Data are acquired according to the Intra Venous Glucose Tolerance Test (IVGTT). Simulation results are reported in order to validate the developed theory

Design of observers for systems with rational output function

This note presents an approach for the design of asymptotic state observers for systems characterized by output functions that are ratios of polynomials in the state. The case of linear and bilinear input-state dynamics is considered, and conditions for exponential error decay are provided. The first step towards the construction of the observer is to show that the dynamics of a system in the considered class can be embedded into the dynamics of a system of higher dimension, with time-varying linear state dynamics and linear output map. The construction of the observer here proposed exploits the structure of the extended system. The solution of a Riccati differential equation provides the observer gain

State observation for systems with linear state dynamics and polynomial output

This paper investigates the problem of asymptotic state reconstruction for a class of continuous-time systems characterized by linear input-state dynamics and polynomial state-output function. It is shown that the dynamics of systems in this class can be embedded into the dynamics of systems of higher dimension, with time-varying linear state dynamics and linear state-output map. An asymptotic state observer for the original system is presented, whose design is based on the equations of the extended system. The observer gain is computed on-line by solving a Riccati differential equation. The interest in this observer is in its capability of state reconstruction also in cases in which the original system is not drift-observable (observable for zero input) nor uniformly observable (observable for any input)

Design of observers for systems with rational output function

Abstract-This note presents an approach for the design of asymptotic state observers for systems characterized by output functions that are ratios of polynomials in the state. The case of linear and bilinear input-state dynamics is considered, and conditions for exponential error decay are provided. The first step towards the construction of the observer is to show that the dynamics of a system in the considered class can be embedded into the dynamics of a system of higher dimension, with time-varying linear state dynamics and linear output map. The construction of the observer here proposed exploits the structure of the extended system. The solution of a Riccati differential equation provides the observer gain

Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets

The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification

State observers for nonlinear systems with smooth/bounded input

summary:It is known that for affine nonlinear systems the drift-observability property (i. e. observability for zero input) is not sufficient to guarantee the existence of an asymptotic observer for any input. Many authors studied structural conditions that ensure uniform observability of nonlinear systems (i. e. observability for any input). Conditions are available that define classes of systems that are uniformly observable. This work considers the problem of state observation with exponential error rate for smooth nonlinear systems that meet or not conditions of uniform observability. In previous works the authors showed that drift-observability together with a smallness condition on the input is sufficient to ensure existence of an exponential observer. Here it is shown that drift- observability implies a kind of local uniform observability, that is observability for sufficiently small and smooth input. For locally uniformly observable systems two observers are presented: an exponential observer that uses derivatives of the input functions; an observer that does not use input derivatives and ensures exponential decay of the observation error below a prescribed level (high-gain observer). The construction of both observers is straightforward. Moreover the state observation is provided in the original coordinate system. Simulation results close the paper