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Choosing an Adaptive Filter Algorithm (Adaptive Filter Toolkit)

LabVIEW 2013 Adaptive Filter Toolkit Help

Edition Date: June 2013

Part Number: 372357B-01

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You must consider both convergence speed and computational resource requirements when choosing an adaptive filter algorithm. For example, the sign least mean squares (LMS) algorithms require the fewest computational resources. However, the corresponding convergence speed is slow. The QR decomposition-based recursive least squares (QR-RLS) algorithm requires the most computational resources. However, the corresponding convergence speed is fast.

For most applications, experiment with the standard LMS or normalized LMS (NLMS) algorithm first. If the resulting convergence speed does not meet the application requirements, consider other adaptive filter algorithms. For active noise control applications, you must use the filtered-x LMS algorithms.

The following table lists the computational resource requirements and relative convergence speed for different adaptive filter algorithms.

Algorithm Computational Resource Requirements Relative Convergence Speed
Memory Usage Multiplication Addition Division Shift
Standard LMS 2n 2n+1 2n+1 0 0 Fast
NLMS 2n 2n+4 2n+3 1 0 Fast
Leaky LMS 2n 3n+1 2n+2 0 0 Fast
Normalized Leaky LMS 2n 3n+4 2n+2 1 0 Fast
Sign-Error LMS 2n n 2n+1 0 n Slow
Sign-Data LMS 2n n 2n+1 0 n Slow
Sign-Sign LMS 2n n 2n+1 0 0 Slow
Fast Block LMS (Constraint) 14n 10log2n+26 N/A 0 0 Fast
Fast Block LMS (Unconstraint) 14n 6log2n+26 N/A 0 0 Fast
Filtered-X LMS 2n+M 2n+M+2 2n+M+2 0 0 Fast
Normalized Filtered-X LMS 2n+M 2n+M+5 2n+M+4 1 0 Fast
Recursive Least Squares (RLS) n2+2n 2n2+4n 1.5n2+2.5n 0 0 Very Fast
QR-RLS n2+2n 5n2+9n 2n2+3n 2n 0 Very Fast
n is the length of the adaptive filter and M is the length of the estimated impulse response of the secondary path.

 

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