Random Signals Estimation and Identification

Avoiding high computational loads is essential to online aerodynamic model identification algorithms, which are at the heart of any model-based adaptive flight control system. Multivariate simplex B-spline (MVSB) methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high performance aircraft.However, the computational efficiency of the MVSB method must be improved in order to enable real-time onboard applications, for example in adaptive nonlinear flight control systems. In this paper, a new recursive sequential identification strategy is proposed for the MVSB method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of computational load for large scale online identification problems as compared to the existing MVSB methods. The proposed method consists of two sequential steps for each time interval, and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step the B-coefficients for each module are estimated using a least squares estimator. In the second step the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean

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“…A definition of the optimal linear minimum mean square error(LMMSE) estimation from [28] is stated as follows:…”

Section: A Theoretical Developmentmentioning

confidence: 99%

Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Sun

Visser

Chu

et al. 2013

Journal of Guidance, Control, and Dynamics

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“…The quantities on the right-hand side of Equations (25)- (27) can be calculated using the forward pass of the Kalman filter [Equations (11)- (17)], the backward pass of the RTS smoother of the Kalman filter (Mohanty, 1986), and some extra recursions for the cross-variance in the forward and backward passes of Equations (31) and (32). These recursions are shown below.…”

Section: Trajectory Estimation Using Emmentioning

confidence: 99%

Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

Afify

Gong

Haton

1996

Computer Speech & Language

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“…in newborn or young infants (Nugent and Finley 1983). However the FFT approach often works poorly for short data records (Kay and Marple 1981;Mohanty 1986). Moreover, as is frequently pointed out (e.g.…”

Section: Introductionmentioning

confidence: 98%

“…That is, small amounts of noise corruption of the time-series data can cause large errors in the frequency domain. For this reason, various smoothing procedures have been developed which involve the use of spectral windows such as the Hamming, Hanning and Parzen windows (Marmarelis and Marmarelis 1978;Mohanty 1986). These procedures reduce statistical error at the expense of resolution, resulting in leakage or smearing (Marmarelis and Marmarelis 1978) in the Fourier series representation.…”

Section: Introductionmentioning

confidence: 99%

A robust orthogonal algorithm for system identification and time-series analysis

1989

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We describe and illustrate methods for obtaining a parsimonious sinusoidal series representation or model of biological time-series data. The methods are also used to identify nonlinear systems with unknown structure. A key aspect is a rapid search for significant terms to include in the model for the system or the time-series. For example, the methods use fast and robust orthogonal searches for significant frequencies in the time-series, and differ from conventional Fourier series analysis in several important respects. In particular, the frequencies in our resulting sinusoidal series need not be commensurate, nor integral multiples of the fundamental frequency corresponding to the record length. Freed of these restrictions, the methods produce a more economical sinusoidal series representation (than a Fourier series), finding the most significant frequencies first, and automatically determine model order. The methods are also capable of higher resolution than a conventional Fourier series analysis. In addition, the methods can cope with unequally-spaced or missing data, and are applicable to time-series corrupted by noise. Finally, we compare one of our methods with a well-known technique for resolving sinusoidal signals in noise using published data for the test time-series.

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Random Signals Estimation and Identification

Cited by 40 publications

References 0 publications

Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

A robust orthogonal algorithm for system identification and time-series analysis

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