Product Processing for Tapered Sparse Arrays
Document Type
Conference Proceeding
Date of Original Version
6-12-2021
Abstract
The product processor output has recently been introduced as a spatial power spectral density estimate, unifying product arrays such as coprime arrays, nested arrays, and standard uniform line arrays. The expected value and covariance function of this estimate for a white Gaussian process was derived in previous work over these various array configurations. However, this prior work used a uniform taper in all cases. In this paper, we show that when product arrays are windowed with non-uniform tapers, the expected value of the product processor output is the convolution of the true spatial power spectral density with the spatial Fourier transform of the difference coarray. This expected value makes a Fourier transform pair with a spatial autocorrelation estimate obtained by windowing the true autocorrelation function. We also derive the covariance function of the product processor output with non-uniform tapers, and compare these derived statistics for the aforementioned array geometries. Also, in prior work, the moments were provided only for linear arrays; this paper extends the estimation results to multidimensional arrays.
Publication Title, e.g., Journal
3rd International Conference on Electrical Communication and Computer Engineering Icecce 2021
Citation/Publisher Attribution
Sartori, Daniel, and Kaushallya Adhikari. "Product Processing for Tapered Sparse Arrays." 3rd International Conference on Electrical Communication and Computer Engineering Icecce 2021 (2021). doi: 10.1109/ICECCE52056.2021.9514103.