Joint decision and Naive Bayes learning for detection of space multi-target

In the photoelectric tracking system, the detection of space multi-target is crucial for target localization and tracking. The difficulties include the interferences from CCD smear and strong noise, the few characteristics of spot-like targets and the challenge of multiple targets. In this paper, we propose a hybrid algorithm of joint decision and Naive Bayes (JD-NB) learning, and present the duty ratio feature to discriminate the target and smear blocks. Firstly, we extract the proper features and train the parameters of the Naive Bayes classifier. Secondly, target blocks are preliminarily estimated with the Naive Bayes. Lastly, the 4-adjacent blocks of the candidate target blocks are jointed to analyze the distribution pattern and the true target blocks are secondarily extracted by the method of pattern matching. Experimental results indicate that the proposed JD-NB algorithm not only possesses a high recognition rate of better than 90% for the target block, but also effectively overcomes the disturbance of the smear block. Moreover, it performs well in the detection of small and faint targets when the SNR of the block is higher than about 0.014.