An approximation method for estimation in linear systems with parameter uncertainty

Speyer,; Gustafson,

doi:10.1109/cdc.1973.269110

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [141] the method of the Kalman filter is used to estimate the state with non!imiting parameters of the object for which, however, their distribution function is assumed known. This system is approximated by a new linear system with additional equations so that its order is twice that of the original system.…”

Section: Vh (T)= C M (0 X (T) +~ (T)mentioning

confidence: 99%

The Kalman-Bucy method of optimal filtering and its generalizations

Prokhorov¹,

Saul'ev²

1979

J Sov Math

View full text Add to dashboard Cite

Section: Vh (T)= C M (0 X (T) +~ (T)mentioning

confidence: 99%

The Kalman-Bucy method of optimal filtering and its generalizations

Prokhorov¹,

Saul'ev²

1979

J Sov Math

View full text Add to dashboard Cite

“…In an effort to alleviate these problems, the circular target model has been proposed as a target motion model, where the phase angle is a Brownian motion process and the acceleration magnitude can be either a random variable or a bounded stochastic process. This target model was suggested in [41 , i,_' concepts extracted from [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Passive Homing Missile Guidance Law Based on New Target Maneuver Models

Speyer

Kim

Tahk

1990

Guidance, Navigation and Control Conference

View full text Add to dashboard Cite

By using more realistic a priori knowledge about the target motion, tracking of maneuvering targets for homing missiles is enhanced. Since certain targets are assumed to execute evasive maneuvers orthogonal to their velocity vector, a new stochastic dynamic target model is proposed where this orthogonality is embedded. Along with this new acceleration dynamic model, the orthogonality is also enforced by the addition of a fictitious auxiliary measurement. The target states are estimated by the modified gain extended Kalman filter (MGEKF), and the angular target maneuver rate is constructed on-line. A guidance law that minimizes a quadratic performance index subject to the assumed stochastic engagement dynamics that includes state dependent noise is derived. This guidance law is determined in closed form where the gains are an explicit function of the estimated target maneuver rate as well as time to go. The numerical simulation for the two-dimensional angle-only measurement case indicates that the proposed target model with the MGEKF leads to remarkable estimation of the target states. Furthermore, the effect on terminal miss distance using this new guidance scheme is given.

show abstract

“…For this scenario, the robust filtering aims at minimizing the expectation of the performance index, the variance of the estimation error (H 2 ) or the worst-case energy gain (H ∞ ), with respect to random parameters. An earlier work on the robust estimations for systems with stochastic uncertainties may be traced back to the late 1970s, during which the average MMSE approaches were proposed in [70] [18]. The corresponding robust deconvolution problem was investigated in [17].…”

Section: Introduction 49mentioning

confidence: 99%

A polynomial approach to robust channel estimator design for DS-CDMA systems

Cao¹

View full text Add to dashboard Cite

show abstract

An approximation method for estimation in linear systems with parameter uncertainty

Cited by 4 publications

References 0 publications

The Kalman-Bucy method of optimal filtering and its generalizations

The Kalman-Bucy method of optimal filtering and its generalizations

A Passive Homing Missile Guidance Law Based on New Target Maneuver Models

A polynomial approach to robust channel estimator design for DS-CDMA systems

Contact Info

Product

Resources

About