000 06533nam a2200721 i 4500
001 6828872
003 IEEE
005 20200413152914.0
006 m eo d
007 cr cn |||m|||a
008 140521s2014 caua foab 000 0 eng d
020 _a9781627052320
_qebook
020 _z9781627052313
_qpaperback
024 7 _a10.2200/S00575ED1V01Y201403COM010
_2doi
035 _a(CaBNVSL)swl00403387
035 _a(OCoLC)880357729
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTK7872.F5
_bX546 2014
082 0 4 _a621.3815324
_223
090 _a
_bMoCl
_e201403COM010
100 1 _aXie, Bei.,
_eauthor.
245 1 0 _aPartial update least-square adaptive filtering /
_cBei Xie, Tamal Bose.
264 1 _aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_c2014.
300 _a1 PDF (ix, 105 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on communications,
_x1932-1708 ;
_v# 10
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
504 _aIncludes bibliographical references (pages 99-103).
505 0 _a1. Introduction -- 1.1 Motivation -- 1.2 Problem statement -- 1.3 Organization of the monograph --
505 8 _a2. Background -- 2.1 Basic adaptive filter models -- 2.2 Adaptive filter models -- 2.2.1 System identification -- 2.2.2 Channel equalization -- 2.3 Existing work on partial update adaptive filters -- 2.4 Basic partial update methods -- 2.4.1 Periodic partial update method -- 2.4.2 Sequential partial update method -- 2.4.3 Stochastic partial update method -- 2.4.4 MMax method --
505 8 _a3. Partial update CMA-based algorithms for adaptive filtering -- 3.1 Motivation -- 3.2 Review of constant modulus algorithms -- 3.3 Partial update constant modulus algorithms -- 3.3.1 Partial update CMA -- 3.3.2 Partial update NCMA -- 3.3.3 Partial update LSCMA -- 3.4 Algorithm analysis for a time-invariant system -- 3.4.1 Steady-state performance of partial update SDCMA -- 3.4.2 Steady-state performance of partial update dynamic LSCMA -- 3.4.3 Complexity of the PU SDCMA and LSCMA -- 3.5 Simulation, a simple FIR channel -- 3.5.1 Convergence performance -- 3.5.2 Steady-state performance -- 3.5.3 Complexity -- 3.6 Algorithm analysis for a time-varying system -- 3.6.1 Algorithm analysis of CMA1-2 and NCMA for a time-varying system -- 3.6.2 Algorithm analysis of LSCMA for a time-varying system -- 3.6.3 Simulation -- 3.7 Conclusion --
505 8 _a4. Partial-update CG algorithms for adaptive filtering -- 4.1 Review of conjugate gradient algorithm -- 4.2 Partial-update CG -- 4.3 Steady-state performance of partial-update CG for a time-invariant system -- 4.4 Steady-state performance of partial-update CG for a time-varying system -- 4.5 Simulations -- 4.5.1 Performance of different PU CG algorithms -- 4.5.2 Tracking performance of the PU CG using the first-order Markov model -- 4.6 Conclusion --
505 8 _a5. Partial-update EDS algorithms for adaptive filtering -- 5.1 Motivation -- 5.2 Review of Euclidean direction search algorithm -- 5.3 Partial update EDS -- 5.4 Performance of the partial-update EDS in a time-invariant system -- 5.5 Performance of the partial-update EDS in a time-varying system -- 5.6 Simulations -- 5.6.1 Performance of the PU EDS in a time-invariant system -- 5.6.2 Tracking performance of the PU EDS using the first-order Markov model -- 5.6.3 Performance comparison of the PU EDS with EDS, PU RLS, RLS, PU CG, and CG -- 5.7 Conclusion --
505 8 _a6. Special applications of partial-update adaptive filters -- 6.1 Application in detecting GSM signals in a local GSM system -- 6.2 Application in image compression and classification -- 6.2.1 Simulations -- 6.3 Conclusion --
505 8 _aBibliography -- Authors' biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _aAdaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity (O(N2)) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on May 21, 2014).
650 0 _aAdaptive filters
_xDesign and construction.
650 0 _aLeast squares.
653 _apartial update
653 _aadaptive filter
653 _aLSCMA
653 _aRLS
653 _aEDS
653 _aCG
700 1 _aBose, Tamal.,
_eauthor.
776 0 8 _iPrint version:
_z9781627052313
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on communications ;
_v# 10.
_x1932-1708
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6828872
856 4 0 _3Abstract with links to full text
_uhttp://dx.doi.org/10.2200/S00575ED1V01Y201403COM010
999 _c562074
_d562074