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

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 are usually computationally expensive, and sensitive to model mismatch. On the other hand, sparse arrays, such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With $N$ denoting the number of sensors, these sparse arrays offer $O({N}^{2})$ freedoms for source estimation because their difference coarrays have $O({N}^{2})$ -long ULA segments. But these well-known sparse arrays have disadvantages: MRAs do not…

• CI
  California Institute of Technology
• OO
  Office of Naval Research

  Award: N00014-15-1-2118