Widely Linear Complex-Valued Least Mean M-Estimate Algorithms: Design and Performance Analysis
To utilize the full second-order statistical information of the complex-valued signal, a widely linear complex-valued LMM (WL-CLMM) algorithm is proposed by using different M-estimate functions. The proposed WL-CLMM algorithm can process both circular and noncircular complex-valued signals in impuls...
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| Veröffentlicht in: | Circuits, systems, and signal processing Jg. 41; H. 10; S. 5785 - 5806 |
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01.10.2022
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| ISSN: | 0278-081X, 1531-5878 |
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| Abstract | To utilize the full second-order statistical information of the complex-valued signal, a widely linear complex-valued LMM (WL-CLMM) algorithm is proposed by using different M-estimate functions. The proposed WL-CLMM algorithm can process both circular and noncircular complex-valued signals in impulsive noise environments. Moreover, a novel adaptive threshold adjustment method for the M-estimate function is designed according to the probability density function of the complex-valued error signal. In addition, to decrease the sensitivity of the input signal to the performance of the algorithm, the normalized version of WL-CLMM (WL-CNLMM) has been developed. Then, we carry out the mean behavior and mean square behavior analysis of the proposed algorithms. Simulation results show that the proposed algorithms outperform some existing complex-valued algorithms and the theoretical results are well matched. |
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| AbstractList | To utilize the full second-order statistical information of the complex-valued signal, a widely linear complex-valued LMM (WL-CLMM) algorithm is proposed by using different M-estimate functions. The proposed WL-CLMM algorithm can process both circular and noncircular complex-valued signals in impulsive noise environments. Moreover, a novel adaptive threshold adjustment method for the M-estimate function is designed according to the probability density function of the complex-valued error signal. In addition, to decrease the sensitivity of the input signal to the performance of the algorithm, the normalized version of WL-CLMM (WL-CNLMM) has been developed. Then, we carry out the mean behavior and mean square behavior analysis of the proposed algorithms. Simulation results show that the proposed algorithms outperform some existing complex-valued algorithms and the theoretical results are well matched. |
| Author | Li, Lei Pu, Yi-Fei |
| Author_xml | – sequence: 1 givenname: Lei orcidid: 0000-0002-1262-9733 surname: Li fullname: Li, Lei organization: School of Computer Science, Sichuan University – sequence: 2 givenname: Yi-Fei surname: Pu fullname: Pu, Yi-Fei email: puyifei_007@hotmail.com organization: School of Computer Science, Sichuan University |
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| CitedBy_id | crossref_primary_10_1109_TSP_2023_3311880 crossref_primary_10_1007_s00034_024_02637_x crossref_primary_10_1007_s00034_024_02786_z crossref_primary_10_1016_j_sigpro_2023_109146 crossref_primary_10_3390_sym16101375 crossref_primary_10_1007_s00034_023_02492_2 crossref_primary_10_1016_j_sigpro_2023_109302 crossref_primary_10_1109_TCSII_2024_3392981 crossref_primary_10_1007_s00034_022_02247_5 crossref_primary_10_1016_j_jfranklin_2023_06_038 |
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| Keywords | Complex-valued Impulsive noise suppression Adaptive filter Widely linear M-estimate |
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