State Estimation of Non-monotonic, Partially Non-deterministic Software with Sparse Probing using an Unscented Kalman Filter Combined with Logic Reasoning

Abstract

This report describes a technique for assessing the state of a general-purpose system using partial probing. The technique utilizes an Unscented Kalman Filter (UKF) combined with in-process and post-process reasoning. While Kalman Filters (KF) Extended Kalman Filres (EKF), and UKF are typically applied to state-space systems, where an underlying theory provides the a-priori knowledge, this report suggests the application of UKF to monitor general-purpose software systems that do not have an underlying first-principles theory. The suggested technique uses a reasoning component compute the a-priori evaluation. An important aspect differentiating state-space systems from general-purpose software is that the latter is often concurrent, with a plurality or concurrently executing threads, processes, or devices. As a result, relative execution time of these components (and the derivative state space) is for all intents and purposes non-deterministic. In addition, the suggested technique enables monitoring with probing that is sparse in time and space namely, probing that occurs only one in n cycles or probing that only probes a subset of the software-systems state-space.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA583474

Entities

People

  • Doron Drusinsky

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computer Science
  • Covariance
  • Equations
  • Equations Of State
  • Estimators
  • Filters
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Military Research
  • Monitoring
  • Optimal Estimators
  • Random Variables
  • Reasoning
  • Space Systems
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space