In this paper we present an efficient one-scan-back Probabilistic Data Filter (PDAF). Regarding the general case of an N-scan-back PDAF, it has been noted in the literature that with each additional scan back, there is a considerable increase in computational load while the amount of improvement in tracking performance diminishes. We therefore have designed a filter that aims to benefit at a minimal increase in computational cost from the one-scan-back architecture that effectively rules out unlikely measurement pairings. In this filter, we use the measurements in previous scan only to produce better weights for the measurements in the present scan. Thus, as compared to a "full" one-scan-back PDAF, we considerably reduce the number of updating and merging steps each scan. For the proposed filter, and the closely related "standard" (zero-scan-back) PDAF and "full" one-scan-back PDAF, we provide the theoretical background, numerical implementation, and simulation results.