This chapter discusses the weighted least squares error processor, updated covariance matrix inverse, Kalman filter methods for adaptive array processing, minimum variance processor and simulation results. The specific form selected for a recursive processor should reflect the data weight scheme that is appropriate for the desired application. The various recursive algorithms may be developed by applying the matrix inversion lemma to the same basic weight update equation. Since the recursive algorithms are different from a DMI algorithm, primarily because the required matrix inversion is accomplished in a recursive manner, it is hardly surprising that many of the desirable properties found to apply to DMI algorithms also hold for recursive algorithms. Rapid convergence rates and insensitivity to eigenvalue spread are characteristics that make recursive processors attractive algorithm candidates provided sufficient computational power and accuracy are available to carry out the required calculations.

Chapter Contents:

• 6.1 The Weighted Least Squares Error Processor
• 6.2 Updated Covariance Matrix Inverse
• 6.3 Kalman Filter Methods for Adaptive Array Processing
• 6.4 The Minimum Variance Processor
• 6.5 Simulation Results
• 6.6 Summary and Conclusions
• 6.7 Problems
• 6.8 References

Inspec keywords: least squares approximations; Kalman filters; recursive filters; covariance matrices; array signal processing; adaptive signal processing

Other keywords: weighted least squares error processor; recursive methods; minimum variance processor; data weight scheme; adaptive array processing; matrix inversion lemma; updated covariance matrix inverse; Kalman filter methods

Subjects: Linear algebra (numerical analysis); Filtering methods in signal processing; Linear algebra (numerical analysis); Interpolation and function approximation (numerical analysis); Digital signal processing; Interpolation and function approximation (numerical analysis)