Abstract-A novel DOA finding method for conformal array applications is proposed. By using sub-array divided and interpolation technique, ESPRIT-based algorithms can be used on conformal arrays for 1-D and 2-D DOA estimation. In this paper, the circular array mounted on a metallic cylindrical platform is divided to several subarrays, and each sub-array is transformed to virtual uniform linear array or virtual uniform planar array through interpolation technique. 1-D and 2-D direction of arrivals can be estimated accurately and quickly by using LS-ESPRIT and 2-D DFT-ESPRIT algorithms, respectively. This method can be applied not only to cylindrical conformal array but also to any other arbitrary curved conformal arrays. Validity of this method is proved by simulation results.

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The techniques of random antenna element distribution and rotation have been proposed to design a low side-lobe and low cross-polarization phased array or reflect array for a long time. In this communication, we demonstrate that the use of randomly rotated elements can provide random scattering phases and phase center distributions, which can lead an in-band radar cross section (RCS) reduction for the array. To illustrate the effectiveness of the proposed method, three 8 8  circularly polarized microstrip arrays: the uniform array without rotation, sequentially rotated array and randomly rotated array, are compared and analyzed. Results indicate that the RCS of the randomly rotated array can be reduced significantly, even in the main beam region, while maintaining its high radiation performance.

Recently, compressive sensing (CS) theory has been applied for synthesising maximally sparse arrays, in which the best subset of sampling element locations is chosen to compose a sparse array for matching a desired radiation pattern. However, their performances are strongly depended on the proper setting of the initial sampling locations, which are typically obtained by gridding the continuous array aperture. Such a setting is usually hard to handle for large planar array synthesis. To address this problem, a precision and effective method based on the perturbed compressive sampling (PCS) is proposed. Position perturbation variables are augmented to the traditional CS‐based model, which allow continuous element placement. Then, a joint sparse recovery approach is used to optimise the excitations and position perturbations of the elements simultaneously. Moreover, the authors implement an extended PCS model with a secondary grid strategy to reduce the modelling error and the computational cost. The proposed design problem is solved with a general sparse recovery solver, named FOCal under‐determined system solver. Numerical results show that the method yields a higher array sparsity, a faster computational speed and a better pattern matching accuracy than the existing CS‐based methods.

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