4 Transversal and Linear Combiner Adaptive Algorithms

This chapter documents the functions used to create, initialize, and update the coefficients of transversal (Section 2.2.1) and linear combiner (Section 2.2.2) adaptive filters. Table 4.1 summarizes the transversal adaptive functions currently supported and gives a short description and a pointer to the reference page of each function. Each function is documented in a separate section including the following information related to the function:

• Purpose: Short description of the algorithm implemented by this function.
• Syntax: Shows the function calling syntax. If the function has optional parameters, this section will have two calling syntaxes. One with only the required formal parameters and one with all the formal parameters.
• Description: Detailed description of the function usage with explanation of its input and output parameters.
• Example: A short example showing typical use of the function. The examples listed can be found in the ASPT/test directory of the ASPT distribution. The user is encouraged to copy from those examples and paste in her own applications.
• Algorithm: A short description of the operations internally performed by the function.
• Remarks: Gives more theoretical and practical remarks related to the usage, performance, limitations, and applications of the function.
• Resources: Gives a summary of the memory requirements and number of multiplications, addition/subtractions, and division operations required to implement the function in real time. This can be used to roughly calculate the MIPS (Million Instruction Per Second) required for a specific platform knowing the number of instructions the processor needs to perform each operation.
• See Also: Lists other functions that are related to this function.
• Reference: Lists literature for more information on the function.

Table 4.1: List of functions for creating, initializing, and updating transversal and linear combiner adaptive filters.

Function Name	Reference	Short Description
asptarlmsnewt	4.1	AR-modeling implementation of LMS-Newton method.
asptbfdaf	4.2	Block Frequency Domain Adaptive Filter (BFDAF) algorithm.
asptblms	4.3	Block Least Mean Squares
asptbnlms	4.4	Block Normalized Least Mean Squares
asptdrlms	4.5	Data Reusing Least Mean Squares
asptdrnlms	4.6	Data Reusing Normalized Least Mean Squares
asptleakylms	4.7	Leaky Normalized LMS algorithm.
asptlclms	4.8	Linearly Constrained LMS (LCLMS) algorithm.
asptlms	4.9	Least Mean Squares (LMS) and several of its variants.
asptmvsslms	4.10	Modified Variable Step Size LMS algorithm.
asptnlms	4.11	Normalized LMS (NLMS) algorithm.
asptpbfdaf	4.12	Partitioned Block Frequency Domain (PBFDAF) algorithm.
asptrcpbfdaf	4.13	Reduced Complexity PBFDAF (RCPBFDAF) algorithm.
asptrdrlms	4.14	Recent Data Reusing Least Mean Squares
asptrdrnlms	4.15	Recent Data Reusing Normalized Least Mean Squares
asptrls	4.16	Recursive Least Squares (RLS) algorithm.
aspttdftaf	4.17	Transform domain Fault Tolerant Adaptive Filter.
aspttdlms	4.18	Transform domain LMS algorithm.
asptvffrls	4.19	Variable Forgetting Factor RLS (VFFRLS).
asptvsslms	4.20	Variable Step Size LMS (VSSLMS) algorithm.
init_ arlmsnewt	4.21	Initialize AR modeling implementation of LMS-Newton method.
init_ bfdaf	4.22	Initialize Block Frequency Domain Adaptive Filter.
init_ blms	4.23	Initialize Block Least Mean Squares
init_ bnlms	4.24	Initialize Block Normalized Least Mean Squares
init_ drlms	4.25	Initialize Data Reusing Least Mean Squares
init_ drnlms	4.26	Initialize Data Reusing Normalized Least Mean Squares
init_ leakynlms	4.27	Initialize Leaky Normalized Least Mean Squares.
init_ lclms	4.28	Initialize Linearly Constrained LMS.
init_ lms	4.29	Initialize Least Mean Squares (LMS).
init_ mvsslms	4.30	Initialize Modified Variable Step Size LMS algorithm.
init_ nlms	4.31	Initialize Normalized LMS.
init_ pbfdaf	4.32	Initialize Partitioned Block Frequency Domain.
init_ rcpbfdaf	4.33	Initialize Reduced Complexity Partitioned BFDAF.
init_ rdrlms	4.34	Initialize Recent Data Reusing Least Mean Squares
init_ rdrnlms	4.35	Initialize Recent Data Reusing Normalized Least Mean Squares
init_ rls	4.36	Initialize Recursive Least Squares.
init_ tdftaf	4.37	Initialize Transform Domain Fault Tolerant Adaptive Filter.
init_ tdlms	4.38	Initialize Transform domain LMS.
init_ vffrls	4.39	Initialize Variable Forgetting Factor RLS (VFFRLS).
init_ vsslms	4.40	Initialize Variable Step Size LMS.