The least mean squares (LMS) and maximum signal-to-noise ratio (SNR) algorithms converge slowly whenever there is a wide spread in the eigenvalues of the input signal correlation matrix. A wide eigenvalue spread occurs if the signal environment includes a very strong source of interference together with other weaker but nevertheless potent interference sources. This condition also happens when two or more very strong interference sources arrive at the array from closely spaced but not identical directions.

Chapter Contents:

• 7.1 Nolen Network Preprocessor
• 7.2 Interference Cancellation with a Nolen Network Preprocessor
• 7.3 Gram-Schmidt Orthogonalization Preprocessor
• 7.4 Simulation Results
• 7.5 Summary and Conclusions
• 7.6 Problems
• 7.7 References

Inspec keywords: correlation methods; eigenvalues and eigenfunctions; interference (signal); matrix algebra

Other keywords: interference sources; input signal correlation matrix eigenvalues; least mean squares algorithm; maximum signal-to-noise ratio algorithm; cascade preprocessors

Subjects: Electromagnetic compatibility and interference; Signal processing theory; Interpolation and function approximation (numerical analysis); Signal processing and detection; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Linear algebra (numerical analysis)