Abstract
A sparsity-aware variable kernel width proportionate affine projection
Highlights
Today, with the evolution of information technology, adaptive filtering (AF) algorithms are used in wireless communication, noise reduction, system identification (SI), and automatic control systems [1,2,3,4,5,6]
Experimental results show that the lp-norm variable kernel width proportionate affine projection (LP-VPAP) converges the fastest and gets the lowest steady state misalignment compared with affine projection (AP), zero-attracting AP (ZA-AP), reweighted ZA-AP (RZA-AP), proportionate AP (PAP), maximum correntropy criterion (MCC), variable kernel width MCC (VKW-MCC), and proportionate AP MCC (PAPMCC) algorithms
According to the simulation above, we found that the devised LP-VPAP obtains better performance when p = 0.5 and ρ = 4 × 10−7 are selected
Summary
With the evolution of information technology, adaptive filtering (AF) algorithms are used in wireless communication, noise reduction, system identification (SI), and automatic control systems [1,2,3,4,5,6]. The traditional LMS, NLMS and AP algorithms show great potential that can be further improved to use the sparse characteristic in the system’s impulse response (IR). To fully use the sparse characteristic, the literature [21] has proposed the famed proportionate NLMS (PNLMS) algorithm by introducing a proportionate update technique to reasonably allocate the step sizes corresponding to each filter tap coefficient. The proportionate affine projection symmetry maximum correntropy criterion (PAPMCC) uses the MCC and the data reusing technique to enhance the robustness of the PAP [50]. The MCC criterion is used in this paper to build a new cost function to improve PAP algorithm, integrate the variable kernel width technique and the l p -norm-like constraint into the cost-function to develop the l p -norm variable kernel width proportionate affine projection (LP-VPAP). Experimental results show that the LP-VPAP converges the fastest and gets the lowest steady state misalignment compared with AP, ZA-AP, RZA-AP, PAP, MCC, VKW-MCC, and PAPMCC algorithms
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