Monotonous Trend Estimation of Deck Displacement for Automatic Landing of Rotorcraft UAVs

This paper presents a novel and practical procedure for estimating the mean deck height to assist in automatic landing operations of a Rotorcraft Unmanned Aerial Vehicle (RUAV) in harsh sea environments. A modified Prony Analysis (PA) procedure is outlined to deal with real-time observations of deck displacement, which involves developing an appropriate dynamic model to approach real deck motion with parameters identified through implementing the Forgetting Factor Recursive Least Square (FFRLS) method. The model order is specified using a proper order-selection criterion based on minimizing the summation of accumulated estimation errors. In addition, a feasible threshold criterion is proposed to separate the dominant components of deck displacement, which results in an accurate instantaneous estimation of the mean deck position. Simulation results demonstrate that the proposed recursive procedure exhibits satisfactory estimation performance when applied to real-time deck displacement measurements, making it well suited for integration into ship-RUAV approach and landing guidance systems.

[1]  Katsuhiko Ogata,et al.  Modern control engineering (3rd ed.) , 1996 .

[2]  Randolph L. Moses,et al.  High resolution radar target modeling using a modified Prony estimator , 1992 .

[3]  A. Ardeshir Goshtasby,et al.  Fitting Parametric Curves to Dense and Noisy Points , 2000 .

[4]  Michael S. Triantafyllou,et al.  Real time estimation of ship motions using Kalman filtering techniques , 1983 .

[5]  Randolph L. Moses,et al.  Full-polarization two-dimensional Prony modeling with application to radar target identification , 1993, Defense, Security, and Sensing.

[6]  J. F. Hauer,et al.  Application of Prony analysis to the determination of modal content and equivalent models for measured power system response , 1991 .

[7]  Daniel J. Trudnowski,et al.  Estimating damping effectiveness of BPA's thyristor controlled series capacitor by applying time and frequency domain methods to measured response , 1996 .

[8]  J. F. Hauer,et al.  Initial results in Prony analysis of power system response signals , 1990 .

[9]  Li Qi,et al.  Prony Analysis for Power System Transient Harmonics , 2007, EURASIP J. Adv. Signal Process..

[10]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[11]  Anna G. Stefanopoulou,et al.  Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments , 2005 .

[12]  Charles W. Therrien,et al.  An iterative Prony method for ARMA signal modeling , 1995, IEEE Trans. Signal Process..

[13]  M. M. Morcos,et al.  Prony application for locating faults on loop systems , 2001 .

[14]  P.O.A.L. Davies,et al.  A recursive approach to prony parameter estimation , 1983 .

[15]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[16]  Joshua R. Smith,et al.  Transfer function identification in power system applications , 1993 .

[17]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[18]  Larry L. Schumaker,et al.  International Conference on Curves and Surfaces (4th), Held in Saint-Malo, France, on 1-7 July 1999. Proceedings, Volume 2. Curve and Surface Fitting , 2000 .

[19]  Piero Barone,et al.  Prony methods in NMR spectroscopy , 1997, Int. J. Imaging Syst. Technol..

[20]  M. A. Moussa,et al.  Non‐Parametric Regression in Curve Fitting , 1992 .

[21]  R. Doraiswami,et al.  Real-time estimation of the parameters of power system small signal oscillations , 1993 .

[22]  H. Akaike A new look at the statistical model identification , 1974 .

[23]  D. J. Trudnowski Characteristics of identifying linear dynamic models from impulse response data using Prony analysis , 1992 .

[24]  M. A. Johnson,et al.  Prony analysis and power system stability-some recent theoretical and applications research , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[25]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[26]  L. Scharf,et al.  A Prony method for noisy data: Choosing the signal components and selecting the order in exponential signal models , 1984, Proceedings of the IEEE.

[27]  J. F. Hauer,et al.  Making Prony analysis more accurate using multiple signals , 1999 .

[28]  Katsuhito Ogata Modern control engineering / katsuhito Ogata , 1997 .

[29]  Shi-Wei Dong,et al.  A Heuristic Optimal Discrete Bit Allocation Algorithm for Margin Maximization in DMT Systems , 2007, EURASIP J. Adv. Signal Process..

[30]  Dong-Jo Park,et al.  Fast tracking RLS algorithm using novel variable forgetting factor with unity zone , 1991 .

[31]  Guido Carpinelli,et al.  Adaptive Prony method for waveform distortion detection in power systems , 2007 .