Conventional ornithological monitoring systems rely heavily on single-channel recorders; deep learning classifiers to identify "what" species is present, but fail to capture "where" it is located or how individuals interact spatially. This limitation hinders the study of complex ecological behaviors, such as inter-specific spacing in dense vegetation; predator-prey dynamics. We propose a novel, dual-mode acoustic localization system designed to unify semantic classification; spatial tracking. Utilizing an economically scalable 16-channel Uniform Rectangular Array (UMA-16) interfaced with edge-computing platforms, we implement a hybrid spatial filtering pipeline structured to balance real-time latency constraints with achievable angular resolution. The first stage employs a computationally efficient, noise-robust linear scanning technique to generate an acoustic energy map; estimate source multiplicity. This preliminary data initializes a second-stage, super-resolution spectral estimation algorithm predicated on signal-noise subspace orthogonality, allowing the noise robustness of non-parametric beamforming methods with the precision of parametric approaches. By integrating these spatial filters with standard deep learning classifiers, the system resolves overlapping vocalizations in "Cocktail Party" scenarios; improves Signal-to-Noise Ratio (SNR) for cryptic species detection. We address the physical "Localization-Detection Range Disparity," demonstrating that while detection is viable at long ranges, precise localization is constrained by the array aperture to the near-to-mid field. The system outputs real-time video overlays of acoustic heatmaps for field observation; generates autonomous volumetric territory maps in fixed deployments, collectively providing ornithologists with a robust capability for analyzing the spatial ecology of avian vocalizations.
