A Fault Tolerant Approach to State Estimation and Failure Detection in Nonlinear Systems

Lancraft, R. E.; Çağlayan, Alper

doi:10.23919/acc.1982.4787964

Cited by 3 publications

(1 citation statement)

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“…Such schemes inherently assume accuracy of filter tuning parameters that may not always be the case and may have trouble distinguishing between measurement outliers and fast dynamic response. Interacting multiple-model (IMM) estimation is another well-known approach [30][31][32] in which several filters are propagated representing various possible failure modes. Although multiple-model approaches are applicable to both linear [29] and nonlinear systems (through the use of particle filters [33]), computational intensity can be high especially for large numbers of sensors or failure modes.…”

Section: A State Estimator Overviewmentioning

confidence: 99%

Smart Projectile State Estimation Using Evidence Theory

Rogers

Costello

2012

Journal of Guidance, Control, and Dynamics

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Smart projectile state estimation is a challenging task due to highly nonlinear vehicle dynamic behavior and unreliable or noisy sensor feedback. Although Kalman-filter-based algorithms are currently the primary means of sensor fusion and state estimation for smart weapons applications, they are limited in estimation accuracy, their ability to combine data from a wide variety of sensors, and their ability to recognize and reject erroneous feedback. This paper explores the use of evidence theory (or Dempster-Shafer theory) for projectile state estimation purposes. Evidence theory offers a generalized framework capable of incorporating feedback from a wide variety of sensors with little a priori knowledge about sensor noise characteristics. The framework's main strengths are its ability to manage varying levels of dynamic uncertainty between sensors and its capability to recognize and report conflicting sensor feedback. The paper begins with an overview of evidence theory and a discussion of its strengths when applied to projectile state estimation. Then an evidence-theory-based filter is described, and an example roll-angle estimator is presented. Results are discussed and performance is compared with an extended Kalman filter. The ability to identify and eliminate malfunctioning sensors from the estimation process proves to be a key advantage of the proposed design over current methods.

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