A Symmetrically Rotatory Ambiguity Cancelling Theorem to Resolve Ambiguities for SLA
Ao Cui, Weichuang Yu, Peiyu He, Zili Xu · 2019
To resolve the ambiguities in the DOA (Direction of Arrival) estimation for SLA (Sparse Linear Array), a symmetrically rotatory ambiguity cancelling theorem is proposed in this paper. The theorem is derived from a fact that the actual DOAs are linearly changing and ambiguities are nonlinearly changing with the SLA rotation. The mathematical proof reveals that when the rotatory angle and the element spacing satisfy the condition established by the theorem, the ambiguities can be distinguished by comparing the DOAs obtained from the original and rotated arrays. Furthermore, the theorem is validated by an elaborately designed X-shaped sparse array. Simulation results demonstrate the theorem is correct and effective.