Performance metrics as aids for fusion algorithm validation

摘要

Performance Metrics (PMs) may be used to evaluate correlation and fusion algorithm performance, particularly in conjunction with Monte Carlo runs of candidate algorithms. These PMs, in some cases, have been used for many years by researchers; less often in industry applications. A survey of recent literature in tracking and fusion shows there are many PMs from which to choose. A few of the more popular metrics include: percent of miscorrelations, percent of correct correlations, total tracking time (tracking persistence), time on target, and percent of total targets tracked and correlated. These types of statistics may be obtained from Monte Carlo simulation test runs. Determination of and access to the truth data for comparison purposes are only part of the problem when using a performance metric. A versatile test tool which can be tailored to the application is also essential. Use of Monte Carlo simulation test results to compute performance metrics is reviewed. Recent experience with PM usage in algorithm development projects is recounted in case studies with appropriate tables and charts. Factors affecting algorithm performance and hence, PM values are considered and discussed. Several questions are posed (and partly answered) regarding ultimate use of PM results.