<![CDATA[Background: Effective wildlife monitoring is crucial for conservation, but traditional methods are often invasive or lack spatial precision. Passive acoustic monitoring offers a non-invasive alternative, yet deriving meaningful spatial data from sound recordings remains a technical challenge, limiting its utility for detailed ecological analysis. Aims: This study aims to design and simulate a proof-of-concept, low-cost acoustic localization system. The goal is to translate Time Difference of Arrival (TDOA) data from a simple tetrahedral microphone array into two-dimensional spatial heatmaps, providing a visual and quantitative tool to map animal vocal activity for enhanced biodiversity assessment. Methods: A cross-shaped, four-sensor array was modeled. A custom MATLAB GUI was developed to simulate TDOA data from multiple sound sources at varied positions. The system processed this data to generate and compare four distinct types of spatial heatmaps: Gaussian Smoothing, Kernel Density Estimation, Grid Counting, and Inverse Distance Weighting Result: The simulation successfully generated all four heatmap types, validating the core data processing pipeline. The system provided estimated source coordinates with a root mean square error (RMSE) of 0.15-0.25 meters in a controlled 6x6m area and output key statistical metrics like cluster density and distribution. Conclusion: The prototype establishes a feasible framework for transforming raw acoustic signals into actionable spatial intelligence. This work provides a foundational step towards developing affordable, automated systems for long-term ecological monitoring, with future integration of machine learning promising direct species identification and behavioral insight.]]>
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