Fusion of multisensor data can improve target probability of detection but suffers from a potentially increased false alarm rate. The optimal sensor decision rule in the case of multiple sensor systems and known target location is of course a likelihood ratio test. This approach, however, is not applicable to many practical scenarios, such as sonar, in which the location of the target is not known and hence the alternative hypothesis becomes composite. Therefore, we propose predetection fusion and highlight its application to a variety of multitarget multisensor trackers. Additionally, the algorithm is motivated by the need for an efficient way to process the volume of data from large sensor networks that consist of low quality sensors. We thus propose predetection fusion as a contact sifting procedure followed by an Expectation Maximization step that refines the location of the estimated detections. Results are provided on a synthetic dataset and on a challenging realistic multistatic sonar dataset. The performance of predetection fusion is compared against the performance of the optimal multi-hypothesis GLRT approach.