Probabilistic data association in high clutter environments

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.