In this paper, we investigate the problem of joint direction of departure (DoD) and direction of arrival (DoA) estimation with bistatic MIMO radar. Specifically, a weaved co-prime array (WCA) bistatic MIMO (WCA-bMIMO) radar is proposed to produce a hole-free sum-difference co-array, which enables to offer significantly increased degrees of freedom (DoFs) and enlarged array aperture. Moreover, the two-dimensional spatial smoothing method is introduced to construct a smoothed received signal, which helps to tackle the rank deduction problem rooted in the equivalent one-snapshot received signal from the sum-difference co-array. Subsequently, the joint DoD and DoA estimates can be obtained by extracting the three-way tensor model. Furthermore, co-array Cramer-Rao Bound (CRB) for the bistatic co-prime MIMO radar is provided. Finally, the superiorities of the proposed bistatic co-prime MIMO radar in accuracy and resolution are proved by numerical simulation results.

Coprime arrays have shown desirable properties in reducing mutual coupling and increasing degrees of freedom (DOF) compared to uniform linear arrays (ULAs), which helps improve the estimation performance. However, the holes in the difference coarray of coprime arrays cause estimation performance loss. In this paper, we present a multi-layer holes filling strategy for coprime arrays to fill the holes, thus enhancing the uniform difference coarray. Specifically, we first construct a coprime array with multiple duplicated subarrays (CAMDS) and classify the holes in the difference coarray into multiple layers. Then we present a multi-layer filled coprime array (MLFCA) by introducing extra subarrays to fill the holes in different layers, which resultantly achieves significantly increased uniform DOFs (uDOFs). We then derive the optimal MLFCA in the scale of difference coarray and develop an optimized structure to extend the difference coarray. Finally, the proposed coprime arrays enjoy a hole-free difference coarray and achieve a larger uniform difference coarray than previous coprime arrays. Simulation results have verified the merits of the proposed coprime arrays through the subspace-based estimator.

In the past decade, sparse arrays have aroused considerable attention for the ability to provide larger array aperture, more degrees of freedom (DOFs) and better estimation performance compared with the uniform linear array (ULA). In this paper, the problem of sparse array design is investigated for direction of arrival (DOA) estimation of non-circular (NC) signals, where both the difference co-array (DCA) and sum co-array (SCA) of physical array configuration can be utilized. Specifically, we first introduce a sliding array strategy which moves the array along the axis as a whole in order to reduce the co-array redundancy and enhance the consecutive DOFs of the sum-difference co-array (SDCA). Subsequently, we focus on the SCA of the conventional nested array (NA) and propose an approach which increases the DOFs of the SCA by relocating proper sensors. Furthermore, based on the above strategy and approach, we derive an extended sliding nested array (ESNA) with few redundant virtual sensors and an enhanced SCA, which can offer increased consecutive DOFs, enlarged virtual array aperture (VAA) and can thus bring high angle estimation accuracy of NC signals. Finally, analytic solutions and simulation results are given to validate the superiority of the proposed ESNA in terms of DOFs and estimation performance.