Joint tracking and identification with robustness against unmodeled targets

Tim Zajic; Ravi B. Ravichandran; Ronald P. S. Mahler; Raman K. Mehra; Michael J. Noviskey

doi:10.1117/12.488537

25 August 2003 Joint tracking and identification with robustness against unmodeled targets

Tim Zajic, Ravi B. Ravichandran, Ronald P. S. Mahler, Raman K. Mehra, Michael J. Noviskey

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.488537
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

We report here on an application of a particle systems implementation of the probability hypothesis density (PHD). The PHD of the multitarget posterior density has the property that, given any volume of state space, the integral of the PHD over that volume yields the expected number of targets present in the volume. The application we consider is the joint tracking and identification of multiple aircraft, with the observations consisting of noisy position measurements and high range resolution radar (HRRRR) signatures. We also take into consideration the presence of clutter and a probability of detection less than unity. Experimental results are presented.

Citation Download Citation

Tim Zajic, Ravi B. Ravichandran, Ronald P. S. Mahler, Raman K. Mehra, and Michael J. Noviskey "Joint tracking and identification with robustness against unmodeled targets", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.488537

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