We present robust high-performance implementations of signal-processing tasks performed by a high-throughput wildlife tracking system called ATLAS. The system tracks radio transmitters attached to wild animals by estimating the time of arrival of radio packets to multiple receivers (base stations). Time-of-arrival estimation of wideband radio signals is computationally expensive, especially in acquisition mode (when the time of transmission is not known, not even approximately). These computations are a bottleneck that limits the throughput of the system. We developed a sequential high-performance CPU implementation of the computations a few years back, and more recencely a GPU implementation. Both strive to balance performance with simplicity, maintainability, and development effort, as most real-world codes do. The paper reports on the two implementations and carefully evaluates their performance. The evaluations indicates that the GPU implementation dramatically improves performance and power-performance relative to the sequential CPU implementation running on a desktop CPU typical of the computers in current base stations. Performance improves by more than 50X on a high-end GPU and more than 4X with a GPU platform that consumes almost 5 times less power than the CPU platform. Performance-per-Watt ratios also improve (by more than 16X), and so do the price-performance ratios.