Efficient Structures for Quadratic Time-frequency and Time-scale Array Processors

Sensor arrays are able to enhance desired signal reception while simultaneously suppressing undesired components through the use of directionality. In many important applications, the return signal is best modeled as being nonstationary and may lose coherence between sensors, severely limiting the performance of traditional array processors based on matched-field beamforming. Quadratic array processing is optimal for many stochastic signals of interest, but direct implementation poses a significant computational burden making it impractical in many situations. Recently it has been shown that quadratic time-frequency representations and time-scale representations (TFRs and TSRs) provide a structured detection framework for detecting certain nonstationary signals in the presence of nonstationary noise using a partially coherent sensor array, making quadratic array processing a viable alternative to suboptimal matched-filter techniques. In this paper we discuss restrictions on the decorrelation between sensors which lead to efficient TFR/TSR-based processors known as banded and subarray beamformers.