HAVA TRAFİK KONTROLU BENZETİMİNDE ETKİLEŞİMLİ ÇOKLU MODEL (INTERACTING MULTIPLE MODEL-IMM) KESTİRİM PERFORMANSI VE KALMAN FİLTRESİ İLE KARŞILAŞTIRILMASI

  • Alper Pahsa
Keywords: Interacting Multiple Model, Interacting Multiple Model-Linear, Interacting Multiple Model- Coordinated Turn, Kalman Filter

Abstract

It is known that adaptive estimation models are used in different time intervals of the motion projection of
maneuvering targets. In this study Interacting Multiple Model (IMM) estimation technique is implemented and
its performance is tested on an air traffic control track simulation. IMM algorithm is a second degree Bayesian
estimation technique and an adaptive estimation model. Air traffic control entity motion is initially simulated
with a constant speed of 125 m/s motion for 100 seconds then turned to the left with a 30 º with a 3 m/s angular
speed for 30 seconds and finally finished its motion with a constant speed of 125 m/s for 70 seconds. Then a
sensor is placed on a specific coordinate to measure the trajectory motion of the air traffic entity. For the
measurements and process simulated Gaussian noise is added during the calculations. The simulated air traffic
control entity’s motion trajectory and the measurements of the sensor are initially modelled with Interacting
Multiple Model-Linear (IMM-L) technique, then Interacting Multiple Model-Coordinated Turn (IMM-CT) and
finally they are modelled with a Kalman filter. According to the results the best estimate matches of the motion
trajectory of the air traffic control entity is generated by IMM-CT, then Kalman Filter and finally IMM-L
algorithms subsequently.

References

[1] Ru J., Li X. R., Jilkov P. V., “Multiple-Model
Detection of Target Maneuvers”, Dept. of Electrical
Engineering, University of New Orleans, LA, In. Proc.
2005, CiteSeer.IST, web page source:
http://citeseer.ist.psu.edu/cache/papers/cs2/401/http:zS
zzSzece.engr.uno.eduzSzislzSzReprints06zSzC149.pd
f/ru05multiplemodel.pdf
[2] Shalom Y.B., Li X.-R., Kirubarajan T.,
“Estimation with Applications to Tracking and
Navigation”, 2001, Wiley&Sons Inc.
[3] Schell C., Linder P. S., Zeidler R. J., “Tracking
Highly Maneuverable Targets With Unknown
Behaviour”, 2004, Proceedings of the IEEE Volume
92 Issue (3) pp. 558-574
[4] Simeonova L., Semerdjiev T., “Specific
Features of IMM Tracking Filter Design”, Procon
Ltd., Sofia, Bulgaristan, 2002, pp. 154-165
[5] MATLAB V 6.5.0.180913a Release13,
MathWorks Inc., June 2002, web page source:
http://www.mathworks.com/products/matlab/
[6] Shalom B., Yeddanapudi M., Pattipati K.,
“IMM Estimation for Multitarget-multisensor Air
Traffic Surveillance”, Proceedings of the IEEE, Vol
85, Iss 1, pp. 80-86, 1997,
[7] Ding Z., Hong L., “A Distributed IMM Fusion
Algorithm for Multi-platform Tracking”, Signal
Processing, Vol. 64, Issue 2, pp. 167-176, 1998
[8] Henk A. P. B., Edwin A. B., “Exact Bayesian
Filter and Joint IMM Coupled PDA Tracking of
Maneuvering Targets from Possibly Missing and False
Measurements”, Automata Volume 42, Iss: 1, pp. 127-
135, 2006
[9] Cruz, J., Pedroza J., Altamirano L., Olivera I.,
“A Performance Comparison of Estimation Filters for
Adaptive Imagery Tracking”, Siganl Processing,
Pattern Recognition and Applications Processing,
2006, Acta Press,
Published
2008-07-21
How to Cite
[1]
A. Pahsa, “HAVA TRAFİK KONTROLU BENZETİMİNDE ETKİLEŞİMLİ ÇOKLU MODEL (INTERACTING MULTIPLE MODEL-IMM) KESTİRİM PERFORMANSI VE KALMAN FİLTRESİ İLE KARŞILAŞTIRILMASI”, JAST, vol. 3, no. 4, pp. 25-36, Jul. 2008.
Section
Articles