# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 1 Introduction
To solve the difficulty of artificially adjusting parameters, a single-snapshot DOA estimation approach based on the alternating-direction multiplier method (ADMM) was presented [27]. This algorithm reduced the need for manual intervention by automatically modifying parameters during training, improving the overall efficiency of the estimation process. An unsupervised learning network was developed based on a thresholded Capon spectral weighting penalty [28]. This method is especially useful in high-interference settings, where it increases the robustness and accuracy of the DOA estimation by implementing an adaptive spectral weighting penalty. [PERSON] et al. [29] introduced a novel deep convolutional neural network named TADCN for 2D-DOA estimation using an L-shaped array. The network incorporates a triple attention mechanism (TAM) to capture channel, height, and width relationships of signal features, improving feature extraction and spatial spectrum quality. However, the aforementioned methods are designed for far-field narrowband signal processing. Wideband signals exhibit different envelopes at each array element, which makes them difficult to directly apply to the DOA estimation of broadband signals. With the advancement of modern communication and radar technologies, narrowband signals increasingly fail to meet practical application requirements, such as those of electronic warfare, spectrum sensing, and through-wall radar imaging. These practical application scenarios have consequently elevated wideband DOA estimation to a critical research area. The general processing approaches for wideband DOA estimation include the incoherent signal subspace method (ISM) [30; 31; 32] and the coherent signal subspace method (CSM) [33; 34; 35]. The ISM algorithm estimates the DOAs of broadband sources by dividing the entire frequency domain into multiple sub-bands, performing narrowband estimation in each frequency band individually, and weighting the spatial spectra in each band. This type of algorithm performs poorly under low-SNR conditions. The CSM algorithm focuses the array covariance matrices of the broadband signals in each frequency band into a specific frequency band and applies narrowband techniques to estimate the spatial spectra in this frequency band. This method offers higher estimation accuracy and a lower resolution threshold and is capable of handling coherent sources. However, its sensitivity to prediction angle deviations means that inaccurate predictions can substantially degrade DOA estimation performance. In recent years, significant advancements have been made in wideband DOA estimation techniques, driven by their expanding applications across multidisciplinary domains. It is worth mentioning that some model-based methods have achieved good results in the DOA estimation of broadband signals. [PERSON] et al. conducted extensive research on this topic. A unified DOA estimation model for hybrid analog-digital architectures was proposed [36], accompanied by the derivation of a dynamic maximum likelihood estimator and its Cramer-Rao bound. Recent developments in tensor-based processing have also shown promising results in addressing high-dimensional signal processing challenges. For example, a tensor-decomposition-based channel estimation framework was introduced for extremely large-scale MIMO-OFDM systems with dynamic metasurface antennas [37]. While these model-driven approaches provide a strong theoretical foundation, their reliance on signal assumptions and their computational complexity in dynamic environments significantly constrain their applicability in real-time, adaptive scenarios. In contrast, deep learning methods offer enhanced estimation accuracy and robustness, particularly in complex electromagnetic environments. Among these methods, the integration of the attention mechanism considerably enhances DOA estimation performance [38]. However, the high computing cost of this method limits its application in real-time systems.