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Sub-Nyquist Coprime Sensing with Compressed Inter-Element Spacing - Low Latency Approach


Affiliations
1 Department of Electrical Engineering, Indian Institute of Technology Delhi, India
     

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One of the generalizations of the coprime array is the Coprime Array with Compressed Inter-element Spacing (CACIS), which is characterized by inter-element spacing that could be varied. The prototype co-prime arrays and nested arrays are a special case of the CACIS scheme.

This paper addresses the aspects that were not considered in previous researches in the given context. The fundamentals of the difference set for the CACIS configuration are developed for low latency. In addition, the closed-form expressions for the weight function (number of samples that contribute to estimate the autocorrelation), and the bias window of the correlogram estimate, which were previously unknown, are derived. Ideally, the bias window should be an impulse. Several examples are provided along with simulations to verify the claims made. All possible sample pairs are used for estimation, which provides for low latency. As an application, temporal spectrum is considered for simulations.


Keywords

Co-Prime Arrays, Samplers, Sparse Sensing, Autocorrelation Estimation, Low Latency.
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  • Sub-Nyquist Coprime Sensing with Compressed Inter-Element Spacing - Low Latency Approach

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Authors

Usham V. Dias
Department of Electrical Engineering, Indian Institute of Technology Delhi, India

Abstract


One of the generalizations of the coprime array is the Coprime Array with Compressed Inter-element Spacing (CACIS), which is characterized by inter-element spacing that could be varied. The prototype co-prime arrays and nested arrays are a special case of the CACIS scheme.

This paper addresses the aspects that were not considered in previous researches in the given context. The fundamentals of the difference set for the CACIS configuration are developed for low latency. In addition, the closed-form expressions for the weight function (number of samples that contribute to estimate the autocorrelation), and the bias window of the correlogram estimate, which were previously unknown, are derived. Ideally, the bias window should be an impulse. Several examples are provided along with simulations to verify the claims made. All possible sample pairs are used for estimation, which provides for low latency. As an application, temporal spectrum is considered for simulations.


Keywords


Co-Prime Arrays, Samplers, Sparse Sensing, Autocorrelation Estimation, Low Latency.

References