Analysis on Strong Tracking Filtering for Linear Dynamic Systems
Strong tracking filtering (STF) is a popular adaptive estimation method to effectively deal with state estimation for linear and nonlinear dynamic systems with inaccurate models or sudden change of state. The key of the STF is to use a time-variant fading factor, which can be evaluated based on the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/648125 |
| Summary: | Strong tracking filtering (STF) is a popular adaptive
estimation method to effectively deal with state estimation
for linear and nonlinear dynamic systems with inaccurate
models or sudden change of state. The key of the STF is to use
a time-variant fading factor, which can be evaluated based on
the current measurement innovation in real time, to forcefully
correct one step state prediction error covariance. The strong
tracking filtering technology has been extensively applied in
many practical systems, but the theoretical analysis is highly
lacking. In an effort to better understand STF, a novel analysis
framework is developed for the strong tracking filtering and
some new problems are discussed for the first time. For this, we
propose a new perspective that correcting the state prediction
error covariance by using the fading factor can be thought of
directly modifying the state model by correcting the covariance
of the process noise. Based on this proposed point of view,
the conditions for the STF function to be effective are deeply
analyzed in a certain linear dynamic system. Meanwhile, issues
of false alarm and alarm failure are also briefly discussed for the
strong tracking filtering function. Some numerical simulation
examples are demonstrated to validate the results. |
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| ISSN: | 1024-123X 1563-5147 |