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Sensor arrays have been widely applied in various fields, e.g., wireless communication, radar, and navigation. Key roles of sensor arrays include providing spatial parameter estimations and enhancing parameter estimation performances in other domains. However, with the present complex electromagnetic environment, general estimation methods experience significant performance degradations when encountering complex signal propagation, such as multipath or occlusion situations. Meanwhile, a sensor array system itself also suffers from uncertainties, such as gain-phase errors and mutual coupling, which are classic but long-term problems. In this Special Issue, we have collected a range of representative research works on this topic. In response to errors in the sensor array itself, such as random array deformations and mutual coupling, sparse array design and small-aperture antenna design methods are proposed. Meanwhile, from a parameter estimation perspective, conformal maps, element selection, and deep learning-based methods are proposed. In response to the received data errors caused by complex environments, such as multipath propagation and low signal-to-noise ratios, methods based on reweighted sparse sensing, pseudo noise resampling, Bayesian estimation, and multiphase filters are proposed. Furthermore, in response to the high complexity brought by robust processing algorithms, methods such as Taylor Compensation and dimensionality reduction are proposed. The relevant methods have shown excellent performance under complex system errors through simulation tests or actual tests.
