A significant problem in multi-target tracking (MU) is the observation-to-track data association. An observation is a signal received, from a target or background clutter, which provides positional information. If an observation is incorrectly associated with a track, that track could diverge and prematurely terminate or cause other tracks to also diverge. Mainly, there are two basic approaches used in data association: the nearest neighbor (NN) approach and the all-neighbors (AN) approach. In the NN approach, the track is updated by at most one observation but in the AN approach, weights are assigned for reasonable observations and a weight centroid of those observations is used to update the track. This paper introduces two techniques belonging to the AN approach: the probabilistic data association (PDA) technique and a new proposed technique. Examples are given to compare the PDA algorithm with the proposed algorithm for data association.