In a typical target tracking problem, the presence of random interference introduces uncertainty into the origin of measurements. Data association techniques, together with state estimation, are thus required to associate each measurement with the appropriate target or discard it as arising from clutter (False alarms). Probabilistic Data Association (PDA) is a well-known technique for single target tracking in clutter. Spurious measurements are rejected based on a validation technique that considers only those measurements that fall within an ellipsoid of probability concentration. In this work, we consider using a Doman-transformed False Discovery Rate approach (DTFDR) to perform this selection task. Tuning the false discovery rate in DTFDRPDA is equivalent to adjusting the gating probability in PDA. Our approach has the advantage that it can be easily decentralized.