Multiple-frame multiple-hypothesis method for tracking at low SNR
John Greenewald, Stanton Musick · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
This paper develops a multiple-frame multiple-hypothesis tracking (MF-MHT) method and applies it to the problem of maintaining track on a single moving target from dim images of the target scene. From measurements collected over several frames, the MF-MHT method generates multiple hypotheses concerning the trajectory of the target. Taken together, these hypotheses provide a smoothed and reliable estimate of the target state. This work supports TENET, an Air Force Research Lab. Project that is developing nonlinear estimation techniques for tracing. TENET software was used to simulate both target dynamics and sensor measurements over a series of Monte Carlo experiments conducted at various signal-to-noise ratios (SNRs). Results are presented that compare computational complexity and accuracy of MF-MHT to two previously-documented nonlinear approaches to predetection tracking, a finite difference scheme and a particle filter method. Results show that MF-MHT requires about 2-3 dB more SNR to compete with the nonlinear methods on an equal footing.