A study on the performance comparison of three optimal Alpha-Beta-Gamma filters and Alpha-Beta-Gamma-Eta filter for a high dynamic target

• Autorzy: Jeong T. , Njonjo A.W. , Pan B.F.

Identyfikatory

DOI

10.12716/1001.11.01.05

• Języki publikacji: EN

Abstrakty

EN

The Alpha-Beta-Gamma tracking filter is useful for tracking a constant acceleration target with zero lag error in the steady state. It, however, depicts a constant lag error for a maneuvering target. Various algorithms of the Alpha-Beta-Gamma tracking filter exist in literature and each one of them presents its own unique challenges and advantages depending on the design requirement. This study investigates the operation of three Alpha-Beta-Gamma tracking filter design methods which include Benedict-Bordner also known as the Simpson filter, Gray-Murray filter and the fading memory constant acceleration filter. These filters are then compared based on the ability to reduce noise and follow a maneuvering target with minimum lag error, against the jerky model Alpha-Beta-Gamma-Eta. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model in comparison with the constant acceleration models.

Słowa kluczowe

EN

integrated navigation; Alpha-Beta-Gamma filter; Benedict-Bordner filter; Simpson filter; Gray-Murray filter; target dynamics; Alpha-Beta-Gamma-Eta filter; Kalman filter

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2017
• Tom: Vol. 11, no. 1
• Strony: 55--61
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Jeong T.

• Korea Maritime University, Busan, Korea

autor

Njonjo A.W.

• Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

autor

Pan B.F.

• Korea Maritime University, Busan, Korea

Bibliografia

• 1 Benedict,  T.  R.  &  Bordner,  G.  W.  1962.  Synthesis  of  an  Optimal  Set  of  Radar  Track‐While‐Scan  Smoothing  Equations. IRE Trans. on Automatic Control. AC‐7: pp.2732.  
• 2 Brookner, E. 1998. Tracking and Kalman filtering made easy.  New York: Wiley. 
• 3 Gray, J. E. & Murray, W. J. 1993. A Derivation of an Analytic  Expression for the Tracking Index for the α‐β‐γ Filter.  IEEE Trans. On Aerospace and Electronic Systems Vol. 29  (3). 
• 4 Jury,  E.  I.  1964.  Theory  and  application  of  the  z‐transform  method. New York: Wiley. 
• 5 Kalata,  P.  R.  1984.  The  tracking  index:  A  generalized  parameter for α ‐ β and α ‐ β ‐ γ target tracker. IEEE  Trans. on Aerospace and Electronic Systems AES‐20 (2): pp.  174‐181. 
• 6 Pan B.F., Njonjo A.W. & Jeong T.G. 2016. Basic Study of the  Optimization of the Gain Parameters α , β and γ of a  Tracking  Module  for  ARPA  system  on  Board  High  Dynamic  Warships  Proceedings,  Journal  of  Navigation  and Port Research Vol.40 (5): pp.241‐247. 
• 7 Pan  B.F.,  Njonjo,  A.W.  &  Jeong,  T.G.  2016.  A  Study  of  Optimization  of  α‐β‐γ‐η  filter  for  tracking  a  high  Dynamic  target.  Proceedings,  2016  Asia  Navigation  conference. 
• 8 Simpson,  H.  R.  April  1963.  Performance  Measures  and  Optimization  Condition  for  a  Third  Order  SampledData Tracker. IEEE Transactions on Automatic Control Vol.  AC8: pp. 182–183.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)