Abstract
The recursive maximum filter (RMF) is an algorithm devised to solve the problem of detecting small moving objects in noisy image sequences. This problem arises in such applications as small target detection for infrared image sensors, and dim star detection for space-borne star sensors. In this paper, we analyze the performance of the RMF. First, we formulate the RMF as a Bellman equation for the dynamic programing. Then, false alarm probability is evaluated using the extreme value theory. Finally, detection probability is obtained as a function of input signal to noise ratios (SNRs). It is shown that input SNRs required for target detection can be reduced to less than half by the RMF.
