Finite Sets Statistics (FISST) provides an engineering friendly theoretic tool for target tracking in clutter.An overview of the studies on the FISST-based target tracking techniques is presented here.Special attention is paid to the following areas:optimal multi-target Bayes filter and its principled approximations

multi-target filter under unknown parameters

multiple maneuvering targets tracking

track-valued estimation

Joint Target detection and Tracking Filter (JoTTF)

Bayesian filtering with random finite set observations

and also the relevant applications.Finally

based on the progress of existing research in these areas

some key issues to enhance the precision and robustness of target tracking further are introduced which deserve more attention of the researchers' for solution.These include:performance evaluation of multi-target filtering

dim/small target tracking

multiple maneuvering targets tracking

multi-sensor multi-target tracking

Joint target Detection

Tracking and Classification (JDTC)

and so on.