This paper presents an iterative algorithm for detecting and estimating specular components (SCs) and the dense component (DC) of single-input multiple-output (SIMO) ultra-wideband (UWB) multipath channels. The algorithm super-resolves SCs in the delay-angle-of-arrival domain and estimates the parameters of a parametric model of the delay-angle power spectrum characterizing the DC. It also estimates channel noise. The algorithm is inspired by the sparse Bayesian learning (SBL) framework, which allows for a threshold condition to determine whether a candidate SC should be retained or pruned. This threshold is adapted using results from extreme-value analysis to ensure a prescribed probability of detecting spurious SCs. The algorithm is shown to be robust in detecting and accurately estimating SCs even when their separation in delay and angle is down to half the Rayleigh resolution limit. It also outperforms a state-of-the-art super-resolution channel estimator in terms of robustness in estimating the amplitudes of closely spaced specular components. The algorithm is tested using synthetic and real channel data, demonstrating its effectiveness in accurately estimating SCs and DC parameters. The paper also discusses the signal model for UWB channels, including the continuous-time and discrete-frequency models, and the sparse Bayesian formulation for inference. The algorithm is designed to estimate the supports of spectral lines in the presence of colored noise, with a focus on the DC and additive white Gaussian noise (AWGN). The algorithm is shown to be effective in resolving SCs in the delay-angle domain and estimating the DC parameters, with a threshold adapted to control the probability of detecting spurious SCs. The paper concludes with a discussion of the computational complexity and performance of the algorithm in different scenarios.This paper presents an iterative algorithm for detecting and estimating specular components (SCs) and the dense component (DC) of single-input multiple-output (SIMO) ultra-wideband (UWB) multipath channels. The algorithm super-resolves SCs in the delay-angle-of-arrival domain and estimates the parameters of a parametric model of the delay-angle power spectrum characterizing the DC. It also estimates channel noise. The algorithm is inspired by the sparse Bayesian learning (SBL) framework, which allows for a threshold condition to determine whether a candidate SC should be retained or pruned. This threshold is adapted using results from extreme-value analysis to ensure a prescribed probability of detecting spurious SCs. The algorithm is shown to be robust in detecting and accurately estimating SCs even when their separation in delay and angle is down to half the Rayleigh resolution limit. It also outperforms a state-of-the-art super-resolution channel estimator in terms of robustness in estimating the amplitudes of closely spaced specular components. The algorithm is tested using synthetic and real channel data, demonstrating its effectiveness in accurately estimating SCs and DC parameters. The paper also discusses the signal model for UWB channels, including the continuous-time and discrete-frequency models, and the sparse Bayesian formulation for inference. The algorithm is designed to estimate the supports of spectral lines in the presence of colored noise, with a focus on the DC and additive white Gaussian noise (AWGN). The algorithm is shown to be effective in resolving SCs in the delay-angle domain and estimating the DC parameters, with a threshold adapted to control the probability of detecting spurious SCs. The paper concludes with a discussion of the computational complexity and performance of the algorithm in different scenarios.