G. A. Ackerson and K. S. Fu. On State Estimation in Switching Environments. IEEE Trans. Automatic Control, AC-15(1):10--17, Jan. 1970.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....authors present a general procedure to synthesize hybrid observers. The literature on observers design in the discrete and the continuous domain is rich. Here we briefly summarize some of the results that are relevant for our presentation. In the control literature, Ackerson first introduced in [1] the state estimation problem for switching systems, represented as continuous systems The work has been conducted with partial support of PARADES, a Cadence, Magneti Marelli and ST microelectronics E.E.I.G, by the European Community Projects IST 2001 33520 CC (Control and Computation) and ....

G. A. Ackerson and K. S. Fu. On state estimation in switching environments. IEEE Trans. on Automatic Control, 15(1):10-17, 1970.

.... onboard vehicle navigation [16] Another example is localizing robots navigating in, inspecting and repairing a network of sewerage pipes [11] To achieve effective localization in these environments, we propose to view localization as a problem of state estimation in a switching environment (see [2] for the seminal paper) and to approach it using methods for multiple hypothesis tracking (see [4, pp. 450 483] for a survey) This amounts to letting Markov localization handle the topological aspects of the problem and Kalman filtering the metric aspects. At the topological level, a Markov ....

....6 Curved roads can easily be accommodated as piecewise linear approximations and handled as a special case of the operations on junctions presented here. To this end, we model segment change as an abrupt change of linear system, which we handle using methods for multiple hypothesis tracking [2, 14, 15, 4]. This amounts to the embedding of one dimensional Kalman filters in a Markov model of the path (segment history) taken through the network, and to viewing segment change as a change of state in this Markov model. Like Kalman filters, discrete Markov models are designed to give a Bayesian ....

G. Ackerson and K.S. Fu. On State Estimation in Switching Environments. IEEE Trans. Aut. Control, 15(1), 1970.

....of computing the mean and covariance of the hidden real valued state vector given the observations (i.e. the filering problem) Shortly after Kalman s results on linear Gaussian state space models, much attention turned to the problem of state estimation with switching parameters. For example, Ackerson and Fu (1970) consider the problem of state estimation in linear state space models which receive (unobserved) state and output disturbances coming from Gaussian mixture distributions with Markov transition structure. Chang and Athans (1978) derive the equations for computing the conditional mean and variance ....

Ackerson, G. A. and Fu, K. S. (1970). On state estimation in switching environments. IEEE Transactions on Automatic Control, AC-15(1):10--17.

....1. 00 1 This example was used in [31] to demonstrate a compression technique for the extended pencil (5) The data are given by A = 2 Gamma1 1 0 ; B = 1 0 ; Q = 0 0 0 1 ; R = 0: If interpreted in terms of a linear system as in (7) 8) Q can be written as Q = C T QC; C = [ 0 1 ]; Q = 1: The exact stabilizing solution is X = I 2 , and the closed loop spectrum is f 0; 0 g. Due to the singularity of R, the condition number KDARE is not defined here (represented by a value 1 in the table) This example can be used, e.g. as a first test of any solver to deal with a ....

....is negative semidefinite. On the other hand, A; B) is controllable. In the case of a continuous time system, this property would assure the existence of a negative semidefinite solution. The stabilizing solution in the control theoretic sense is the positive definite solution X 1 . Example 6 [1] n m p parameter (A) j C max j jjXjj (X) KDARE 4 2 4 1.01 0.94 35.36 3.34 30.58 The data of this example represent a simple control problem for a satellite. The system is given by equations describing the small angle altitude variations about the roll and yaw axes of a satellite in circular ....

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G. Ackerson and K. Fu, On the state estimation in switching environments, IEEE Trans. Automat. Control, AC-15 (1970), pp. 10--17.

....hidden Markov models and estimation in that framework we refer to previously published comprehensive papers [2, 3] Here we will use a suboptimal scheme named adaptive forgetting through multiple models (AFMM) 4] to limit the computational burden. There are several other schemes that can be used [5, 6, 7, 8]. The goal is to calculate the a posteriori probabilities of the Markov states given past measurements and to estimate the parameters in the data models. The scheme consists of running at most M Kalman filters in parallel at any time t where M is a fixed positive number dictated by the ....

G.A. Ackerson, "On State Estimation in Switching Environments," IEEE Trans. on Aut. Control, Vol. AC-15, No. 1, 1970.

....operation is again achieved by the Kalman filter operation. We call this method an extended MKF. Given the importance of the CDLM in system modeling, it is perhaps not surprising that approaches similar to the MKF described in this article have been proposed earlier. Indeed, the earlier work of Ackerson and Fu (1970), Akashi and Kumamoto (1977) and Tugnait (1982) and recent work of Liu and Chen (1995) and Doucet (1998) are all closely related. We will provide a more detailed account on each of these approaches in Section 3. The rest of the paper is organized as follows. In Section 2 we provide a brief ....

....complexity and accuracy: greater efficiency can be achieved if the distribution is approximated accurately, with relatively simple indicators. In section 5 we show and analyze several CDLMs in practice. Engineers have begun to deal with special forms of the CDLM since 1970s. In a pioneering work, Ackerson and Fu (1970) consider a linear system operating in switching environments, which they formulate as the model in Example 1 with the t being a finite discrete Markovian indicator process. To deal with the computational difficulty, they propose an approximate filtering procedure in which the posterior ....

Ackerson, G.A. and Fu, K.S. (1970). On state estimation in switching environments. IEEE Trans.

....of computing the mean and covariance of the hidden real valued state vector given the observations (i.e. the filering problem) Shortly after Kalman s results on linear Gaussian state space models, much attention turned to the problem of state estimation with switching parameters. For example, Ackerson and Fu (1970) consider the problem of state estimation in linear state space models which receive (unobserved) state and output disturbances coming from Gaussian mixture distributions with Markov transition structure. Chang and Athans (1978) derive the equations for computing the conditional mean and variance ....

Ackerson, G. A. and Fu, K. S. (1970). On state estimation in switching environments. IEEE Transactions on Automatic Control, AC-15(1):10--17.

....switching, and jump linear systems. We brie y review some of this literature, including some related neural network models. 4 Shortly after Kalman and Bucy solved the problem of state estimation for linear Gaussian state space models attention turned to the analogous problem for switching models (Ackerson and Fu, 1970). Chang and Athans (1978) derive the equations for computing the conditional mean and variance of the state when the parameters of a linear state space model switch according to arbitrary and Markovian dynamics. The prior and transition probabilities of the switching process are assumed to be ....

.... (KL) divergence between Q and P (Cover and Thomas, 1991) KL(QkP ) X fS t g Z Q(fS t ; X t g) log Q(fS t ; X t g) P (fS t ; X t gjfY t g) dfX t g: 13) 7 The intractability of the E step or smoothing problem in the simpler single state switching model has been noted by Ackerson and Fu (1970), Chang and Athans (1978) Bar Shalom and Li (1993) and others . Since the complexity of exact inference in the approximation given by Q is determined by its conditional independence relations, not by its parameters, we can choose Q to have a tractable structure a graphical representation which ....

Ackerson, G. A. and Fu, K. S. (1970). On state estimation in switching environments. IEEE Transactions on Automatic Control, AC-15(1):10-17.

....positive semidefinite and a negative semidefinite solution. But here, despite the fact that (A; B) is controllable, no negative semidefinite solution exists. See the analysis in [44] for details. The stabilizing solution in the control theoretic sense is the positive definite solution. 6. Source: [1]. The data of this example represent a very simple control problem for a satellite. The system is given by equations describing the small angle altitude variations about the roll and yaw axes of a satellite in circular orbit. These equations originally form a second order differential equation. A ....

G. Ackerson and K. Fu. On the state estimation in switching environments. IEEE Trans. Automat. Control, AC-15:10--17, 1970.

....we consider the analogs of these results for the image based, point process observation model. The state estimation problem for discrete time Markov jump linear systems (without modal observations) has been studied in some detail both generally and in the context of tracking maneuvering targets (Ackerson and Fu, 1970; Jaffer and Gupta, 1971; Chang and Athans, 1978; Tugnait, 1982; Blom and Bar Shalom, 1988; Bar Shalom and Li, 1993; Costa, 1994) It is well known that the optimal filter is impractical due to exponential growth in computational and memory requirements (the filtered density is a Gaussian mixture ....

Ackerson, G. A. and Fu, K. S. (1970). On state estimation in switching environments. IEEE Trans. Auto.

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G. A. Ackerson and K. S. Fu. On State Estimation in Switching Environments. IEEE Trans. Automatic Control, AC-15(1):10--17, Jan. 1970.

No context found.

Ackerson, G. A., and K. S. Fu, "On State Estimation in Switching Environments," IEEE Trans. Automatic Control, Vol. AC-15, pp. 10--17, Jan. 1970.

No context found.

G.A. Ackerson and K.S. Fu. On state estimation in switching environments. IEEE Transactions on Automatic Control, 15:10--17, 1970.

No context found.

G. Ackerson and K. Fu. On the state estimation in switching environments. IEEE Trans. Automat. Control, AC-15:10--17, 1970.

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