Low-Complexity -Norm Penalized Shrinkage Linear and Widely Linear Affine Projection Algorithms

作者：Zhang, Youwen; Xiao, Shuang^*; Sun, Dajun; Liu, Lu

来源：Circuits, Systems, and Signal Processing, 2017, 36(8): 3385-3408.

DOI：10.1007/s00034-016-0465-6

• Signal processing
• Adaptive filter
• Low-complexity adaptive algorithm
• LMS

In this paper, we propose an -norm penalized shrinkage linear affine projection (-SL-AP) algorithm and an -norm penalized shrinkage widely linear affine projection (-SWL-AP) algorithm. The proposed algorithms provide variable step-size by minimizing the noise-free a posteriori error at each iteration and introduce an -norm constraint to the cost function. The -SWL-AP algorithm also exploits noncircular properties of the input signal. In contrast with conventional AP algorithms, the proposed algorithms increase the estimation accuracy for time-varying sparse system identification. A quantitative analysis of the convergence behavior for the -SWL-AP algorithm verifies the capabilities of the proposed algorithms. To reduce the complexity, we also introduce dichotomous coordinate descent (DCD) iterations to the proposed algorithms (-SL-DCD-AP and -SWL-DCD-AP) in this paper. Simulations indicate that the -SL-AP and -SWL-AP algorithms provide faster convergence speed and lower steady-state misalignment than the previous APA-type algorithms. The -SL-DCD-AP and -SWL-DCD-AP algorithms perform similarly to their counterparts but with reduced complexity.

• 出版日期2017-8
• 单位哈尔滨工程大学