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.

Confidence evaluation algorithms for the analysis of binary phase-shift keying (BPSK) signals are discussed. The phase of the product of the observed signal and constructed reference signal is extracted, and its spectrum is utilized to verify confidence. Two extreme statistics of the phase spectrum are developed for the confidence evaluation, which depend on goodness-of-fit tests based on the generalized extreme value distribution and generalized Pareto distribution respectively. Via extensive simulations, we verify the effectiveness of the proposed algorithms.

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.

A binary hypothesis test-based algorithm is proposed to assess the confidence of blind analysis results of BPSK signals. The reference signal is first constructed based on the given analysis result. The spectrum of multiplication between observed signals and reference signals is used as a distinguishing statistic and the decision based on the false alarm rate is accordingly taken to verify whether a specific analysis is confident or not. The theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.