A novel array structure called nested arrays is proposed to significantly increase the degrees of freedom (DOF) of linear arrays. This structure is formed by systematically nesting two or more uniform linear arrays, enabling O(N²) DOF using only N physical sensors through second-order statistics. The nested array concept is extended to multiple stages, and the optimal nesting strategy is analytically derived. Unlike existing arrays like minimum redundancy arrays, nested arrays provide closed-form expressions for sensor locations and DOF. The paper introduces a spatial smoothing-based DOA estimation method that does not rely on fourth-order cumulants or quasi-stationary signals, and a nonlinear beamforming approach that effectively utilizes the DOF of nested arrays. The proposed methods are validated through simulations. The nested array structure is shown to provide a filled uniform linear array (ULA) difference co-array, enabling increased DOF in passive sensing scenarios. The paper also discusses the extension of nested arrays to multiple levels, achieving O(N²K) DOF with higher-order statistics. The nested array is compared with other methods, showing its advantages in DOF, ease of construction, and applicability to both stationary and non-Gaussian sources. The spatial smoothing technique is used to construct a positive semidefinite covariance matrix for DOA estimation, and the beamforming approach exploits the DOF of the nested array for enhanced performance. The paper concludes that nested arrays offer a systematic way to increase DOF in passive sensing scenarios, with significant improvements over traditional methods.A novel array structure called nested arrays is proposed to significantly increase the degrees of freedom (DOF) of linear arrays. This structure is formed by systematically nesting two or more uniform linear arrays, enabling O(N²) DOF using only N physical sensors through second-order statistics. The nested array concept is extended to multiple stages, and the optimal nesting strategy is analytically derived. Unlike existing arrays like minimum redundancy arrays, nested arrays provide closed-form expressions for sensor locations and DOF. The paper introduces a spatial smoothing-based DOA estimation method that does not rely on fourth-order cumulants or quasi-stationary signals, and a nonlinear beamforming approach that effectively utilizes the DOF of nested arrays. The proposed methods are validated through simulations. The nested array structure is shown to provide a filled uniform linear array (ULA) difference co-array, enabling increased DOF in passive sensing scenarios. The paper also discusses the extension of nested arrays to multiple levels, achieving O(N²K) DOF with higher-order statistics. The nested array is compared with other methods, showing its advantages in DOF, ease of construction, and applicability to both stationary and non-Gaussian sources. The spatial smoothing technique is used to construct a positive semidefinite covariance matrix for DOA estimation, and the beamforming approach exploits the DOF of the nested array for enhanced performance. The paper concludes that nested arrays offer a systematic way to increase DOF in passive sensing scenarios, with significant improvements over traditional methods.