Super Nested Arrays: Linear Sparse Arrays With Reduced Mutual Coupling—Part I: Fundamentals

摘要: In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). While there are methods to counteract this through appropriate modeling and calibration, they usually computationally expensive, sensitive model mismatch. On other hand, sparse arrays, such as nested coprime minimum redundancy arrays (MRAs), have reduced compared uniform linear (ULAs). With $N$ denoting number sensors, these offer $O({N}^{2})$ freedoms for source because their difference coarrays -long ULA segments. But well-known disadvantages: MRAs do not simple closed-form expressions geometry; holes in coarray; contain a dense physical array, resulting significantly higher than MRAs. This paper introduces new called super which all good properties at same time achieves coupling. There is systematic procedure determine sensor locations. For fixed , aperture, hole-free coarray does array. pairs with small separations ( $\lambda /2,2\times \lambda /2$ etc.) reduced. Many theoretical proved simulations included demonstrate superior performance arrays. companion paper, further extension $Q$ th-order developed, reduces