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Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (pages 99-103).

1. Introduction -- 1.1 Motivation -- 1.2 Problem statement -- 1.3 Organization of the monograph --

2. 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 --

3. 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 --

4. 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 --

5. 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 --

6. 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 --

Bibliography -- Authors' biographies.

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Adaptive 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.

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