Mean track life and track `half-life´ in nearest-neighbor tracking

In tracking a target through clutter, the selection of incorrect measurements for track updating causes track divergence and eventual loss of track. It is shown that, by modeling the plot-to-track association algorithm as a Markov process in the tracking error, one can calculate the mean track life and track half-life analytically, without recourse to Monte Carlo simulations. The method is illustrated for nearest-neighbor association in two space dimensions. Explicit results are presented showing the effect of clutter spatial density on tracking performance. The results indicate the existence of a critical density region in which the tracking performance degrades rapidly with increasing clutter density