  Target tracking problem has many practical applications in real life. In
submarines, target tracking is done using, preferably, passive sensors. These
sensors measure only the bearing angles between the observed target and the
ownship. Therefore, this problem is generally referred as bearing only target
tracking or target motion analysis. The classical approach is to use a state
observer based filter, i.e. Extended Kalman Filter, to estimate the range,
course and speed of the target, using only the bearings. In recent studies, the
problem is solved as a global optimization problem by utilizing evolutionary
algorithms with respect to some objective functions. In this study, we
investigate the effect of the commonly used cost functions on the performance
of the TMA algorithms. Particularly, we investigate the cost functions based on
bearing differences and equidistant line segments. The simulation results show
that the latter gives a sub-optimal solution to the target motion analysis
problem, compared to the former.
