Sparse Sensor Array Design and Processing

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Array signal processing exploiting multiple adaptively controlled sensors plays a fundamental role in various sensing and communication systems. Array signal processing is a key enabling technology for adaptive beamforming, interference suppression, multiuser access, direction-of-arrival (DOA) estimation, source localization, and image formation. Array signal processing techniques are broadly utilized in various applications including wireless communications, radar, sonar, medical imaging, and radio astronomy. The demand for increasingly higher spatial resolution and estimation accuracy in modern sensing and communication applications calls for larger array apertures. Conventional uniform linear arrays with sensors being placed at the Nyquist half-wavelength spacing require the number of sensors be approximately proportional to the array aperture. This project develops new sparse array design strategies and signal processing techniques to offer cost-effective solutions that enable large array apertures and high spatial resolutions with a smaller number of sensors. In particular, it will develop novel sparse array design strategies that incorporate structured-based array interpolations and reduce redundancies in the correlation-lag domain. Such solutions will bring multifold benefits, such as reducing the hardware complexity and enhancing communication and sensing performance in next-generation wireless communications, supporting high-resolution automotive radar imaging for autonomous driving, and facilitating optimized reconfigurable intelligent surface applications. On the educational front, this project will offer opportunities for training of graduate and undergraduate students, supporting summer research activities of local high school students, and recruiting students from underrepresented groups. The proposed efforts are integrated through the following three thrusts. (a) In the first thrust, new sparse array design strategies and signal processing techniques are developed. Sparse arrays are designed and optimized to minimize redundancies in the correlation lags and obtain a high number of degrees of freedom by accounting for the array interpolation capability. Signal bandwidth is exploited to offer frequency diversity that extends the array spatial dimension and resolves more sources. (b) In the second thrust, the challenging problem of estimating the DOAs of correlated and coherent signals is considered. The proposed work will examine the sparse array interpolation capability, analyze the achievable degrees of freedom, and devise effective signal processing techniques for their implementations. (c) In the third thrust, recognizing the spatial sparsity of signal arrivals in many practical scenarios, compressed measurement strategies are developed to reduce the dimension of large-size array signals so the signal dimension to be sampled for digital processing is significantly reduced. New compressed measurement methods are devised from information-theoretical, machine learning, and optimization perspectives to enhance DOA estimation performance. The outcomes from this project will advance the sparse array concept, theory, and algorithms beyond the current state-of-the-art, and will broaden the applicability and enhance the robustness of sparse array signal processing techniques in practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.