Complexity reduction in MHT/MFA tracking
Aubrey B. Poore · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
The performance advantage of the multiple frame data association methods over the single frame methods follows from the ability to hold difficult decisions in abeyance until more information is available and the opportunity to change past decisions to improve current decisions. In dense tracking environments the performance improvements of multiple frame methods over single frame methods is very significant, making it the preferred solution for difficult tracking problems. The price that one pays for this performance gain is the computational complexity (NP-hard) of the resulting data association problem. The number of strings of data or arcs that one forms for N frames of data each containing M reports is MN wherein we have omitted missed reports. This number grows exponentially with the length of the window (i.e., N). Thus, much preprocessing is required to manage memory usage and to achieve good runtime performance. The control of the computations makes use of a variety of techniques including a) bin gating, b) coarse pair and triple point dynamic gating, c) multi-frame gating, d) medium gating based on filter prediction gates, e) fine gating based on likelihood ratios, f) track hypothesis pruning, h) problem partitioning, and i) cluster tracking. While this work comments on many of these methods, the goal is to derive methods bin, coarse pair, and multi-frame gating in a simple setting as possible.