Novi normirani pojasni adaptivni filtar s promjenjivom duljinom koraka

This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. In the proposed VSS-NSAF, the step-size changes in order to have largest decrease in themean square deviation (MSD) for sequential iterations. To reduce the computational complexity of VSS-NSAF, the variable step-size selective partial update normalized subband adaptive filter (VSS-SPU-NSAF) is proposed. In this algorithm the filter coefficients are partially updated in each subband at every iteration. Simulation results show the good performance of the proposed algorithms in convergence speed and steady-state MSD.U ovom radu prikazan je novi algoritam za normirani adaptivni filtar s promjenjivim korakom. Kod predloženog filtra, veličina koraka mijenja se kako bi se dobilo najveće smanjenje srednje vrijednosti odstupanja za uzastopne iteracije. Kako bi se smanjila računska složenost filtra, predložen je normirani pojasni adaptivni filtar s promjenjivim korakom i selektivnim parcijalnim osvježavanjem. Kod tog algortima koeficijenti filtra parcijalno se osvježavaju u svakom pojasu i pri svakoj iteraciji. Simulacijski rezultati pokazuju dobru brzinu konvergencije i malu srednju vrijednost odstupanja u stacionarnom stanju za predloženi filtar

Analiza prijelazne pojave adaptivnih filtara primjenom općeg radnog okvira

Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a large range of adaptive filter algorithms simply by specifying some parameters of this generic filter coefficient update equation. In particular we point out that theoretical learning curves for the Least Mean Square (LMS), Normalized Least Mean Square (NLMS), the Affine Projection Algorithm (APA) and its relatives, as well as the Recursive Least Squares (RLS) algorithm are obtained as special cases of a general result. Subsequently, the recently introduced Fast Euclidian Direction Search (FEDS) algorithms as well as the Pradhan-Reddy subband adaptive filter (PRSAF) are used as non-trivial examples when we demonstrate the usefulness and versatility of the proposed approach to adaptive filter transient analysis through an experimental evaluation.U radu se predstavlja poopćena analiza prijelaznih pojava adaptivnih filtara, koja se zasniva na primjeni nedavno predstavljenog radnog okvira koji velik broj raznih algoritama adaptivnih filtara promatra kao specijalne slučajeve. Važna posljedica toga je da su rezultati, iako se teoretska analiza provodi na generičkoj jednadžbi za osvježavanje koeficijenta filtra, izravno primjenjivi na razne algoritme adaptivnih filtara jednostavnom specificikacijom nekih parametara generičke jednadžbe za osvježavanje koeficijenata filtra. Posebno se naglašava da su teoretske krivulje učenja za algoritam najmanjih kvadrata (LMS), normalizirani algoritam najmanjih kvadrata (NLMS), afini projekcijski algoritam (APA) i njemu srodnih algoritama, kao i za rekurzivni algoritam najmanjih kvadrata (RLS) dobivene kao posebni slučajevi poopćenog rješenja. Potom se nedavno predstavljeni algoritmi brze euklidske usmjerene pretrage (FEDS) te Pradhan-Reddy pojasni adaptivni filtar (PRSAF) koriste kao netrivijalni primjeri za dokazivanje korisnosti i univerzalnosti predloženog pristupa analizi prijelaznih pojava adaptivnih filtara kroz eksperimentalnu evaluaciju

Analiza prijelazne pojave adaptivnih filtara primjenom općeg radnog okvira

Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a large range of adaptive filter algorithms simply by specifying some parameters of this generic filter coefficient update equation. In particular we point out that theoretical learning curves for the Least Mean Square (LMS), Normalized Least Mean Square (NLMS), the Affine Projection Algorithm (APA) and its relatives, as well as the Recursive Least Squares (RLS) algorithm are obtained as special cases of a general result. Subsequently, the recently introduced Fast Euclidian Direction Search (FEDS) algorithms as well as the Pradhan-Reddy subband adaptive filter (PRSAF) are used as non-trivial examples when we demonstrate the usefulness and versatility of the proposed approach to adaptive filter transient analysis through an experimental evaluation.U radu se predstavlja poopćena analiza prijelaznih pojava adaptivnih filtara, koja se zasniva na primjeni nedavno predstavljenog radnog okvira koji velik broj raznih algoritama adaptivnih filtara promatra kao specijalne slučajeve. Važna posljedica toga je da su rezultati, iako se teoretska analiza provodi na generičkoj jednadžbi za osvježavanje koeficijenta filtra, izravno primjenjivi na razne algoritme adaptivnih filtara jednostavnom specificikacijom nekih parametara generičke jednadžbe za osvježavanje koeficijenata filtra. Posebno se naglašava da su teoretske krivulje učenja za algoritam najmanjih kvadrata (LMS), normalizirani algoritam najmanjih kvadrata (NLMS), afini projekcijski algoritam (APA) i njemu srodnih algoritama, kao i za rekurzivni algoritam najmanjih kvadrata (RLS) dobivene kao posebni slučajevi poopćenog rješenja. Potom se nedavno predstavljeni algoritmi brze euklidske usmjerene pretrage (FEDS) te Pradhan-Reddy pojasni adaptivni filtar (PRSAF) koriste kao netrivijalni primjeri za dokazivanje korisnosti i univerzalnosti predloženog pristupa analizi prijelaznih pojava adaptivnih filtara kroz eksperimentalnu evaluaciju

A Family of Affine Projection Adaptive Filtering Algorithms With Selective Regressors

In this paper we present a general formalism for the establishment of the family of selective regressor affine projection algorithms (SR-APA). The SR-APA, the SR regularized APA (SR-RAPA), the SR partial rank algorithm (SR-PRA), the SR binormalized data reusing least mean squares (SR-BNDR-LMS), and the SR normalized LMS with orthogonal correction factors (SR-NLMS-OCF) algorithms are established by this general formalism. We demonstrate the performance of the presented algorithms through simulations in acoustic echo cancellation scenario

A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal

Abstract ᅟ In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1-norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions. The good performance of SR-NSAF is demonstrated through several simulation results in system identification, acoustic echo cancelation (AEC) and line EC (LEC) applications. The theoretical relations are also verified by presenting various experimental results