Chapters 4 through 9 considered the transient response characteristics and implementation considerations associated with different classes of algorithms that are widely used for adaptive array applications. This chapter summarizes the principal characteristics of each algorithm class before considering some practical problems associated with adaptive array system design. In each chapter of Part 2 the convergence speed of an algorithm representing a distinct adaptation philosophy was compared with the convergence speed of the least mean squares (LMS) algorithm. The convergence speeds of the various algorithms are compared for a selected example in this chapter. Since the misadjustment versus rate of adaptation tradeoffs for the random search algorithms-linear random search (LRS), accelerated random search (ARS), and guided accelerated random search (GARS) - and for the differential steepest descent (DSD) algorithm of Chapter 4 are unfavorable compared with the LMS algorithm, recourse to these methods would be taken only if the meager instrumentation required was regarded as a cardinal advantage or nonunimodal performance surfaces were of concern. Furthermore, the Howells-Applebaum maximum signal-to-noise ratio (SNR) algorithm has a misadjustment versus convergence speed trade-off that is nearly identical with the LMS algorithm.

Inspec keywords: random processes; adaptive signal processing; gradient methods; least mean squares methods; array signal processing

Other keywords: LRS algorithm; DSD algorithm; ARS algorithm; guided accelerated random search algorithm; linear random search algorithm; adaptive array system design; SNR; Howells-Applebaum maximum signal-to-noise ratio algorithm; nonunimodal performance surfaces; LMS algorithm; accelerated random search algorithm; differential steepest descent algorithm; distinct adaptation philosophy; least mean square algorithm; GARS algorithm

Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Other topics in statistics; Signal processing theory; Interpolation and function approximation (numerical analysis); Signal processing and detection