System State Estimation in the Presence of False Information Injection

Abstract

The problem of system state estimation in the presence of an adversary is investigated for linear dynamic systems. It is assumed that the adversary injects additive false information into the sensor measurement. The impact of the false information on the Kalman filter s estimation performance is analyzed for a general dynamic system. To be concrete, a target tracking system has been used as an example. In such a system, if the false information is injected only once, the effect of the false information on the Kalman filter proves to be diminishing over time, even when the Kalman filter is unaware of the false information injection. The convergence rate as a function of the maneuvering index is analyzed. If the false information is repeatedly injected into the system, the induced estimation error proves to reach a finite steady state. Numerical examples are presented to support the theoretical results.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA585155

Entities

People

  • Lauren Huie
  • Ruixin Niu

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Convergence
  • Eigenvalues
  • Equations
  • Estimators
  • Filters
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Measurement
  • Military Research
  • Signal Processing
  • Statistical Algorithms
  • Steady State
  • Target Tracking

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radio communications and signal processing.