The proposed algorithm, which is named the block-sparse SM-PNLMS (BS-SMPNLMS), is implemented by inserting a penalty of a mixed l 2 , 1 norm of weight-taps into the cost function of the SM-PNLMS. Furthermore, an improved BS-SMPNLMS algorithm (the (BS-SMIPNLMS algorithm) is also ...
A proportionate NLMS (PNLMS) algorithm was recently proposed, which could take advantage of the sparse nature of the system and offer improved convergence behaviour under such circumstances [103].The performance of PNLMS algorithm degrades when the system behaviour is changed from sparse to non-...
The superior performance of the proposed improved filter was validated by comparing three other Monte Carlo filters, including multinomial reweighted filter, two stage sampling algorithm and standard SIR filter in a classic bearings-only tracking problem. More studies on recovering the particle depletion ...
We introduce a novel algorithm for online estimation of Acoustic Impulse Responses (AIRs) which allows for fast convergence by exploiting prior knowledge a
An algorithm to improve the convergence performance of the improved μ-law PNLMS algorithm (IMPNLMS) for non-sparse impulse responses is proposed in this chapter. In this algorithm, an adaptive parameter μ of the μ.-law compression for sparse impulse response is incorporated into the IMPNLMS ...
Moreover, an improved proportionate normalized least mean square (i-PNLMS) control algorithm is proposed for the DSTATCOM. The system is first modeled on a MATLAB/Simulink platform and its performance is analyzed under steady state and load increment cases. The frequency response of the compensated...
The performance of proportionate filter update schemes such as the so-called proportionate normalized least mean squares algorithm (PNLMS) or the improved PNLMS (IPNLMS) for system identification of equalized impulse response (IR) are shown and the mutual influences of the subsystems are analyzed. ...
The proportionate normalized least mean square (PNLMS) algorithm is used in sparse system identification for its simplicity and adaptively step-size adjusting scheme. However, the PNLMS has the noisy input and the non-Gaussian output noise problem. A bias Compensated PNLMF algorithm (called BCPNLMF...
For identifying sparse systems, a recently proposed algorithm called upper threshold based zero attracting proportionate normalized least mean square (UT-ZA-PNLMS) algorithm has shown improved performance in terms of both the convergence rate and steady-state error in comparison to the ZAPNLMS algorithm...
Further, a block sparse memory improved proportionate affine projection sign algorithm (BS-MIP-APSA) was proposed in [397] which sustains the performance ability of the memory improved proportionate affine projection sign algorithm (MIP-APSA) [119] along with robustness to impulsive interference, ...