As detection processing becomes increasingly advanced, for example, in infrared search and track (IRST) systems, the detection threshold becomes the bottleneck to overall system performance. Significantly reducing this threshold requires the capability to track targets in a high clutter environment. In theory, the multiple hypothesis tracking (MHT) algorithm is a solution to this problem. However, in practice, MHT in its basic form becomes computationally prohibitive for all but low to moderate false alarm densities. In this paper, we evaluate a computationally feasible alternate form, which we call a bi-level MHT algorithm. The basic form of this algorithm has been previously proposed, but results on its performance have been lacking. In addition to describing an implementation of a bi-level MHT algorithm, this paper present Monte Carlo simulation results characterizing the performance of the algorithm, and demonstrates the tradeoff between track acquisition range and false track rate for a simple IRST fly-by scenario.