<title>Importance of resolution in multiple-target tracking</title>

Abstract

Sensor resolution is crucial for the success of tracking in a dense multiple target environment. The probability of resolution (P<SUB>R</SUB>) and the probability of correct data association (P<SUB>DA</SUB>) are computed as a function of: (1) average object separation, (2) sensor resolution, and (3) one-sigma prediction error, for sensor measurements with dimensions n equals 1, 2, 3, ... . The values of P<SUB>R</SUB> and P<SUB>DA</SUB> are plotted versus the average object separation normalized by the sensor resolution, parameterized by the one-sigma prediction error, for values of n equals 1, 2, 3, ... . By inspection of these curves, it is obvious that P<SUB>R</SUB> is less than P<SUB>DA</SUB> for n equals 1, 2, 3, ... for any values of average object separation and sensor resolution, for almost any practical value of one-sigma prediction error of interest. This means that sensor resolution is a more important issue than data association in most practical applications. Nevertheless, 99 percent of the literature on multiple target tracking has ignored the issue of resolution.