RWTH Aachen
Institute for Communication
Systems and Data Processing
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A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution

Stefan Kühl, Sebastian Nagel, Tobias Kabzinski, Peter Jax, and Christiane Antweiler
Book Title:
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Event Date:
April 2018
accepted for publication


Research in static and time-variant system identification has brought up a broad variety of identification algorithms. In acoustics, e.g., static measurements of transfer functions are commonly conducted using Inverse Cyclic Convolution (ICC) with Exponential Sweep excitation. Identification and tracking of time-variant systems, however, often employ adaptive filter algorithms, such as the Normalized Least Mean Square (NLMS) algorithm. An interesting implementation variant is the so-called Efficient NLMS (eNLMS) algorithm for arbitrary periodic excitation. ICC and the eNLMS algorithm originate from different fields and have so far evolved independently. This paper bridges the gap using a theoretical analysis of both algorithms to prove that they can be transferred into each other. This understanding provides a joint perspective, such that know-how from both fields can be combined to further optimize the system identification process.

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