Low-Complexity Constrained Recursive Kernel Risk-Sensitive Loss Algorithm

Authors：Xiang, Shunling^*; Zhao, Chunzhe; Gao, Zilin; Yan, Dongfang

Source：2022, 14(5): 877.

DOI：10.3390/sym14050877

• constrained adaptive filtering
• kernel risk-sensitive loss
• instability
• mean square stability

The constrained recursive maximum correntropy criterion (CRMCC) combats the non-Gaussian noise effectively. However, the performance surface of maximum correntropy criterion (MCC) is highly non-convex, resulting in low accuracy. Inspired by the smooth kernel risk-sensitive loss (KRSL), a novel constrained recursive KRSL (CRKRSL) algorithm is proposed, which shows higher filtering accuracy and lower computational complexity than CRMCC. Meanwhile, a modified update strategy is developed to avoid the instability of CRKRSL in the early iterations. By using Isserlis's theorem to separate the complex symmetric matrix with fourth-moment variables, the mean square stability condition of CRKRSL is derived, and the simulation results validate its advantages.