Sparse arrays have obvious superiority in array element layout and the number of degrees of freedom (DOF) for resolving more sources than the number of physical sensors with high estimation accuracy. Nevertheless, any of the existing array configurations with fixed number of sensors corresponds to a certain array element arrangement, the virtual coarray and the uniform DOF may be affected significantly when the positions of one or several sensors changed. In this paper, a generalized nested array (GNA) configuration family is proposed by incorporating the concept of family into array design. The arrays in GNA family generally consist of three subarrays, and the inherent geometric characteristic is analyzed from the perspective of two-dimensional (2D) representation. For the array configurations of GNA family, one can be transformed into another by setting order factor, thus they can provide more flexible element arrangements as compared to a specific array configuration. Moreover, each array of the GNA family has hole-free difference coarray. Based on the GNA family, compressive sensing (CS) approach is employed for direction-of-arrival (DOA) estimation by solving an l ₁ norm minimization problem. The theoretical propositions are proved and numerical simulations are performed to demonstrate the superior performance of the proposed GNA family and the DOA estimation.

Direction-of-Arrival (DOA) estimation with sparse non-uniform linear arrays (SNLAs) has recently attracted a growing concern for the reason that more sources than the number of physical sensors can be resolved as compared with the conventional uniform linear arrays (ULAs). In this paper, we develop an underdetermined DOA estimation method using unfold coprime array (unfold CPA) from the perspective of sum-difference co-array (SDCA). By jointly using the temporal and spatial information of the impinging sources and the Khatri-Rao (KR) product operation, the SDCA of the unfold CPA are obtained, which can provide more consecutive DOF and larger array aperture than the SNLAs based on the difference co-array (DCA). Additionally, benefitting from the array configuration of the unfold CPA, the weight functions of the physical sensor pairs with small separations are equal to zero, thus the mutual coupling effects between sensors are reduced significantly. The theoretical propositions and numerical simulations are provided to confirm the effectiveness and superior performance of the proposed method.

This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method.

In this letter, we introduce two generalized sparse antenna arrays (SAAs) designed based on the maximum inter-element spacing constraint (MISC) criterion. The proposed arrays, named GEMISCE and GEMISCO, are developed by filling the holes of enhanced MISC array (EMISC) through sensor rearrangement, with a special focus on even and odd maximum inter-element spacings (MIES). The GEMISCE and GEMISCO are constructed by configurating the inter-element spacings (IES) set and are proved to possess filled difference coarrays. In addition, several common characteristics for the hole-free MISC-based array designs are outlined. Theoretical propositions and simulations validate the performance of the proposed arrays in terms of uniform degrees of freedom and mutual coupling (MC), underscoring their potential to enhance target identifiability and estimation accuracy.