Median cascaded canceller for robust adaptive array processing

Abstract

A median cascaded canceller (MCC) is introduced as a robust multichannel adaptive array processor. Compared with sample matrix inversion (SMI) methods, it is shown to significantly reduce the deleterious effects of impulsive noise spikes (outliers) on convergence performance of metrics such as (normalized) output residue power and signal to interference-plus-noise ratio (SINR). For the case of no outliers, the MCC convergence performance remains commensurate with SMI methods for several practical interference scenarios. It is shown that the MCC offers natural protection against desired signal (target) cancellation when weight training data contains strong target components. In addition, results are shown for a high-fidelity, simulated, barrage jamming and nonhomogenous clutter environment. Here the MCC is used in a space-time adaptive processing (STAP) configuration for airborne radar interference mitigation. Results indicate the MCC produces a marked SINR performance improvement over SMI methods.

Robust Auto-Regressive Spectrum using a Reiterative Median Cascaded Canceller

“…The RMCC algorithm [1][2][3] is an adaptive signal processing algorithm that may be used to form a robust estimate of an N t × 1 equivalent linear prediction weight vector having the general form w = [w 1 w 2 … w Nt ] T , from which a robust AR spectrum can be generated [4]. The quantity N t denotes the number of degrees of freedom (DOF) or the number of input channels used, and superscript T denotes transpose.…”

“…The RMCC is comprised of a set of identical median cascaded canceller (MCC) [1] processors in cascade (or, equivalently, a single MCC with its output channels redirected back onto its input channels). Such reiterative processing is used to improve the convergence of the resulting robust linear prediction weight vector.…”

“…Such reiterative processing is used to improve the convergence of the resulting robust linear prediction weight vector. It was shown in [2,3] that reiterative processing is required for achieving good performance in several cases.…”

“…Selection of the proper AR model order is important in order to reduce the common problem of spurious peaks, 1 1-4244-0525-4/07/$20.00 ©2007 IEEE. 2 IEEEAC paper #1351, Version 3, Updated Nov. 21,2006 which bear no correlation with true frequencies used in the formation of the input data.…”

“…Subsequent AR spectra using contaminated data may be badly distorted or have missing / additional spectral peaks in many real-world applications. This paper presents a novel method to form a robust AR spectrum using the Reiterative Median Cascaded Canceller (RMCC) algorithm [2,3]. In Section 2 we discuss an RMCC processor.…”