**7. Conclusions**

A DOA tracking algorithm based on the UT-MeMBer particle filter in an impulse noise environment is proposed in this paper. Since the FLOM matrix is used instead of the covariance matrix, the spatial spectrum based on FLOM can well reflect the real DOA in impulse noise environment. For the persistent surviving particles, the sigma point is selected by UT to approximate the posterior density of the state to improve the performance of the particle. Then, the MUSIC spatial spectral function of the FLOM matrix is used to represent the likelihood function of the particle. And the weighting of the likelihood function can further increase the weight of the particles in the high likelihood region. The results show that the UT-MB-FLOM-MUSIC algorithm is more effective than the PASTD, MB-MUSIC, and MB-FLOM-MUSIC algorithms in an impulse noise environment. The advantage of this algorithm is that the MeMBer filter can operate the array data more directly, and can effectively track the target number of time-varying DOA. The shortcoming of this algorithm is that it takes a long time. Our future work will focus on how to improve the efficiency of the algorithm, maneuvering target tracking in other noisy environments, etc.

**Author Contributions:** The work presented here was carried out in collaboration between follow authors. S.-y.W., R.-h.C., and Q.-t.X. defined the research theme. J.Z. and X.-d.D. designed the methods and experiments, carried out the simulation experiments. J.Z. interpreted the results and wrote the paper.

**Funding:** This work is supported by National Natural Science Foundation of China (Grant 61561016, 61962012), Guangxi Natural Science Foundation (Grant 2016GXNSFAA380073), Guangxi Key Laboratory of Cryptography and Information Security (GCIS201611), Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, and the GUET Excellent Gradute Thesis Program (Grant 17YJPYSS23).

**Conflicts of Interest:** The authors declare no conflict of interest.
