Recursive frame integration of limited data: RAFAIL

Real time infrared imaging and tracking usually requires a high probability of target detection along with a low false alarm rate, achievable only with a high "Signal-to-Noise Ratio" (SNR). Frame integration--summing of non-correlated frames--is commonly used to improve the SNR. But conventional frame integration requires significant processing to store full frames and integrate intermediate results, normalize frame data, etc. It may drive acquisition of highly specialized hardware, faster processors, dedicated frame integration circuit cards and extra memory cards. Non-stationary noise, low frequency noise correlation, non-ergodic noise, scene dynamics, or pointing accuracy may also limit performance. Recursive frame integration of limited data--RAFAIL, is proposed as a means to improve frame integration performance and mitigate the issues. The technique applies two thresholds--one tuned for optimum probability of detection, the other to manage required false alarm rate--and allows a non-linear integration process that, along with optimal noise management, provides system designers more capability where cost, weight, or power considerations limit system data rate, processing, or memory capability.