A target tracker using spatially distributed infrared measurements

Abstract

An extended Kalman filter algorithm is designed to track a point source target in an open-loop tracking problem, using outputs from a forward-looking infrared (FLIR) sensor as measurements. The filter separately estimates the translational position changes of the target in the FLIR field of view due to two effects: actual target motion and apparent motion caused by atmospheric turbulence. A Monte Carlo analysis is conducted to determine the performance of the filter as a function of signal-to-noise ratio, target spot size, the ratio of rms target motion to rms atmospheric jitter, target correlation times, and mismatches between the true target spot size and the size assumed by the filter. The performance of the extended Kalman filter is compared to the performance of an existing correlation tracker under identical conditions. A one sigma tracking error of 0.2 and 0.8 picture elements is obtained with signal-to-noise ratios of 20:1 and 1:1, respectively. No degradation in performance is observed when the spot size is decreased or when the target correlation time is increased over a limited range, when filter parameters are adjusted to reflect this knowledge. Sensitivity analysis shows that the filter is robust to minor changes in target intensity spot size.