MLPDA (Maximum Likelihood Probabilistic Data Association) has drawn attention as an effective target track
extraction algorithm in high false density environments. In this algorithm, the target track is estimated as the maximum
likelihood state vector, by using multiple observation frames that include the target signal and many false signals. The
track is confirmed whether it is the true target or not, by comparing its likelihood with a given track confirmation
threshold. However, when the target signals are lost at several frames, the conventional MLPDA deteriorates the track
estimation accuracy due to false signals in frames without the target signal. In this paper, we propose multiple
hypothetical frame selection MLPDA, which can extract the target track under the situation where the target signals are
lost in several frames. Specifically, a batch of stored frames is first selected for track extraction. If the track is not
confirmed, our algorithm offers multiple frame selection hypotheses where some frames are assumed to be the frames
without the target signal and the other frames include the target signal. The track is extracted under these hypotheses,
respectively, and the most likely hypothesis is accepted. If all hypotheses are rejected, our proposed method generates
hypotheses that increase the number of frames without the target signal, and verifies them again. Furthermore, the
hypotheses that have likelihoods above a given threshold are retained in order to modify the wrong frame selection later.
Simulation results show the validity of our proposed method.