Automated Optimization of Tracking Algorithms Using Simulated Annealing

The task of tuning a tracking filter often requires a tedious "trial and error" effort, and thus it is prudent to attempt to automate the process. There are a number of heuristic algorithms, such as simulated annealing and genetic algorithms, which are designed to seek global maxima or minima in multi-variable problems, and which appear suitable for this application. In this study, simulated annealing was used to tune two types of track filters used for a standard benchmark problem.