Abstract: ‍ ‍Group target tracking has demonstrated broad application prospects in disaster relief and search and rescue， sea and land defense， and battlefield operations. Unlike the conventional multitarget tracking， group target tracking involves the tracking of not only multiple individual targets but also that of a group target. In a group， each subtarget must propagate synchronously to avoid collision； additionally， the number of subtargets and the structure in the group change over time. Depending on the number of subtargets and the sensor resolution， group target tracking can be classified into resolvable， indistinguishable， partially resolvable， and partially indistinguishable group target tracking. Hence， the problem associated with resolvable group target tracking requires the simultaneous estimation of the group structure as well as the interaction and number of subtargets within the group. Existing studies focus primarily on resolvable group-target-tracking methods based on the conventional data association and random-finite-set filters. Among them， the method based on random-finite-set filters alleviates the data-association problem by jointly modeling multiple target states as random finite sets， thus enabling better adaptation to tracking scenarios. To illustrate the research progress of resolvable group-target-tracking methods more clearly， some representative methods based on random-finite-set filters proposed in recent years are reviewed， including methods based on multitarget multi-Bernoulli filters， labeled random-finite-set filters， and Poisson multi-Bernoulli mixture filters. These methods are particularly advantageous for solving problems associated with resolvable group target tracking. Finally， the existing problems and future directions are prospected.