In a real-time tracking scenario, efficient tracking performance is particularly crucial given the computational power constraints inherent in low-cost, low-power sensors. The interval between successive radar measurements, known as the measurements per processing interval (MpPI), dictates the rate at which measurements are received. Since tracking algorithms operate recursively, each measurement must be processed to compute a full track before the next measurement arrives. Therefore, the efficiency of the tracker directly influences the achievable MpPI. Faster tracking algorithms enable higher MpPI operational modes, which are especially advantageous for scenarios like drone tracking, where objects move rapidly relative to the radar’s proximity. In this paper, we investigate the performance of a lightweight tracking library applying efficient software techniques, developed for use in onboard radar applications. In particular, we measured runtime profiles and track metrics including Generalized Optimal Sub-Pattern Assignment, Single-Integrated Air Picture and uncertainty metrics against Stone Soup, the leading open-source Python tracking library. A comparison of track performance and speed was completed by running both implementations of the Joint Probabilistic Data Association filter in various multi-target tracking scenarios. Results showed that our Standalone Tracker library software consistently outperformed Stone Soup in performance speed, while maintaining comparable track quality.