2013
DOI: 10.1016/j.jprocont.2013.05.004
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Robust derivative-free Kalman filter based on Huber's M-estimation methodology

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Cited by 71 publications
(45 citation statements)
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“…[23,47,39]. Robust methods [4,44,11,32,1] rely on Huber [57] or Vapnik losses, leading to support vector regression [41,50,55] for state space models, and take advantage of interior point optimization methods [67,81,113]. Domain constraints are important for most applications, including camera tracking, fault diagnosis, chemical processes, vision-based systems, target tracking, biomedical systems, robotics, and navigation [53,98].…”
Section: Scope Of the Surveymentioning
confidence: 99%
See 1 more Smart Citation
“…[23,47,39]. Robust methods [4,44,11,32,1] rely on Huber [57] or Vapnik losses, leading to support vector regression [41,50,55] for state space models, and take advantage of interior point optimization methods [67,81,113]. Domain constraints are important for most applications, including camera tracking, fault diagnosis, chemical processes, vision-based systems, target tracking, biomedical systems, robotics, and navigation [53,98].…”
Section: Scope Of the Surveymentioning
confidence: 99%
“…The conjugate of · 1 is computed in (32), and it is easy to see that the function 1 2 · 2 is its own conjugate using definition 31.…”
Section: Algorithms and Convergence Ratesmentioning
confidence: 99%
“…To illustrate the effectiveness of the proposed filtering method, we investigate a second-order system where x k = [x 1 k , x 2 k ] T ∈ R 2 with missing measurements (2). Matrices A k and H k are given as follows:…”
Section: Numerical Examplementioning
confidence: 99%
“…Among a variety of existing filtering methods, the Kalman filtering approach has been widely adopted in practical engineering systems due to its capability of minimizing the quadratic sum of the error between the estimation and the actual state vector at each time step. In 1960, the classical Kalman filter was proposed to estimate the state of the linear systems with known parameters and noise statistics, and since then, a range of improved filters has been designed in the literature with the hope of handling the state estimation problems for different systems under different assumptions on noise (see, for example, previous works [2][3][4][5][6][7] and references therein).…”
Section: Introductionmentioning
confidence: 99%
“…4 shows that the singularity avoidance method is significant for filtering estimation accuracy and reliability for the MRP-based UKF. We use 3α rule to weigh the credibility of algorithm [32], [33]. The α is defined as the standard deviation of the estimation state by α = √ P. For the 3α rule, it is argued that the state estimate error should lie in the region of three times the standard deviation with probability, Fig.…”
Section: B High-precision Sins/gps Simulation Testmentioning
confidence: 99%