Data association is the key component in single or multiple target tracking algorithms with measurement origin.
Probabilistic Data Association (PDA), in which all validated measurements are associated probabilistically to
the predicted estimate, is a well-known method to handle the measurement origin uncertainty. In PDA, the
effect of measurement origin uncertainty is incorporated into the updated covariance by adding the spread of the
innovations term. The updated covariance may become very large after few time steps in high clutter scenarios
due to spread of the innovations term. Large covariance results in a large gate, which is used to limit the possible
measurements that could have originated from the target. Hence, the track will be lost and estimate will just
follow the prediction. Also, large gate will make the well-separated target assumption invalid, even if the targets
are well-separated. Hence, after a few time steps all the targets in the surveillance region come under the same
group, making the Joint Probabilistic Data Association (JPDA). In this paper, adaptive gating techniques are
proposed to avoid the steady increase in the updated covariance in high clutter. The effectiveness of the proposed
techniques is demonstrated on simulated data.