Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison

IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Radar, Sonar and Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© IEE
Published

Multitarget tracking systems in operational use today generally adopt Kalman filter (KF) techniques (coupled with a manoeuvre detector to introduce some kind of adaptivity), and nearest neighbour (NN) correlation. Today there are two new approaches to the tracking problem, namely: interacting multiple model joint probabilistic data association (IMMJPDA) and multiple hypothesis tracking (MHT) which promise improved tracking performance. The paper provides a performance comparison between these three tracking algorithms in terms of track maintenance probability and tracking errors. The NN + KF algorithm is used as reference because of its widespread use. Results show that MHT is superior to IMMJPDA and, as expected, both perform better than NN + KF; the cost of additional performance is increased, yet feasible, computing power.

Inspec keywords: Kalman filters; correlation methods; target tracking; filtering theory; radar detection; tracking filters; radar tracking

Other keywords: tracking algorithms; tracking errors; interacting multiple model joint probabilistic data association; manoeuvre detector; tracking performance; radar tracking problem; multiple hypothesis tracking; radar target detection; nearest neighbour correlation; performance comparison; IMMJPDA; Kalman filter; track maintenance probability; tracking filter

Subjects: Radar theory; Signal processing and detection

References

    1. 1)
      • S.S. Blackman . (1986) Multiple target tracking with radar applications.
    2. 2)
      • F.E. Daum , R.J. Fitzgerald . The importance of resolution in multiple target tracking. Proc. SPIE -Int. Soc. Opt. Eng. , 329 - 388
    3. 3)
      • Graziano, A., Miglioli, R., Farina, A.: `Multiple hypothesis tracking vs. Kalman filter with nearestneighbor correlation: Performance comparison', AGARD Conference Proceedings no. 557, May 1995, Lisbon, p. 25/1–25/11.
    4. 4)
      • A. Farina , F.A. Studer . (1985) Radar data processing vol. 1 - introduction and tracking.
    5. 5)
      • J.A. Roecker . A class of near optimal JPDA algorithms. IEEE Trans. , 2 , 504 - 510
    6. 6)
      • I.J. Cox , M.L. Miller . On finding ranked assignments with application to multitarget tracking and motion correspondence. IEEE Trans. Aerosp. Electron. Syst. , 1 , 486 - 489
    7. 7)
      • Y. Bar-Shalom , K.C. Chang , H.A.P. Blom , Y. Bar-Shalom . (1992) Tracking splitting targets in clutter by using an interactingmultiple model joint probabilistic data association filter, Multitarget-multisensor tracking: Applications and advances.
    8. 8)
      • Blackman, S.S., Dempster, R.J., Tucker, G.K., Roszkowski, S.H.: `Recent multiple hypothesis trackeradvances and results', Hughes interdepartmental correspondence, July 1995.
    9. 9)
      • Bar-Shalom, Y., Daeipour, E., Li, X.: `Adaptive beam pointing control of a phased array radar usingan IMM estimator', Proc. American Control Conference, June 1994, Baltimore, MD, p. 1–15.
    10. 10)
      • Y. Bar-Shalom , X.R. Li . (1993) Estimation and tracking, principles, techniques and software.
    11. 11)
      • Y. Bar-Shalom , K.C. Chang , P.B. Luh . Centralized and distributed algorithms for multitarget-multisensortracking systems, Control and dynamic systems.
    12. 12)
      • Bar-Shalom, Y., Daeipour, E., Kirubarajan, T.: `Adaptive beam pointing control of a phased array radarin the presence of ECM and false alarms using IMMPDAF', Proc. American Control Conference, June 1995, Seattle, WA.
    13. 13)
      • Y. Bar-Shalom , K.C. Chang , H.A.P. Blom , Y. Bar-Shalom . (1990) Automatic track formation in clutter with a recursivealgorithm, Multitarget-multisensor tracking: Advanced applications.
    14. 14)
      • Y. Bar-Shalom , T.E. Fortmann . (1988) Tracking and data association.
    15. 15)
      • Y. Bar-Shalom , H.A.P. Blom . The interacting multiple model algorithm for systems with markovianswitching coefficients. IEEE Trans. , 8 , 780 - 783
    16. 16)
      • J.A. Roecker , G.L. Phillis . Suboptimal joint probabilistic data associations. IEEE Trans. , 2 , 510 - 517
    17. 17)
      • R.J. Fitzgerald , Y. Bar-Shalom . (1990) Development of practical PDA logic for multitarget tracking by microprocessor, Multitarget-multisensor tracking: Advanced applications.
    18. 18)
      • Zhou, B.: `Multitarget tracking in clutter, algorithms for data association andstate estimation', May 1992, Thesis in electrical engineering, Pennsylvania State University.
    19. 19)
      • Y. Bar-Shalom . (1995) Multitarget-multisensor tracking, principles and techniques.
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-rsn_19970976
Loading

Related content

content/journals/10.1049/ip-rsn_19970976
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address