2010
DOI: 10.1016/j.inffus.2009.06.004
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Multi-model information fusion Kalman filtering and white noise deconvolution

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Cited by 112 publications
(60 citation statements)
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“…According to Sun et al, 15 the CF and WMF white noise deconvolution estimation errorw ( j) (t|t + N) = w(t) −ŵ ( j) (t|t + N), N ≥ 0 satisfy the following formula:…”
Section: Robust Cf and Wmf Time-varying White Noise Deconvolution Estmentioning
confidence: 99%
“…According to Sun et al, 15 the CF and WMF white noise deconvolution estimation errorw ( j) (t|t + N) = w(t) −ŵ ( j) (t|t + N), N ≥ 0 satisfy the following formula:…”
Section: Robust Cf and Wmf Time-varying White Noise Deconvolution Estmentioning
confidence: 99%
“…Based on conservative WMF steady-state Kalman predictor, for the worst-case WMF systems (1) and (14) Sun et al [15] proved that filtering and smoothing errors…”
Section: A the First Class Of Guaranteed Cost Robust Wmf Kalman Filtmentioning
confidence: 99%
“…However, all of the above weighting fusers have the limitation that in order to compute the optimal weights, the computation of the cross-covariances between the local estimation errors is required, while the cross-covariances are usually unknown [6] or their computation is very complex [7] in many applications. In order to overcome this limitation, the covariance intersection fusion method has been presented and developed in [6,8,9] which can solve the fused filtering problems with unknown cross-covariances, and have the consistency and robustness.…”
Section: Introductionmentioning
confidence: 99%