A new sequential decision feedback method with application to target classification

This paper presents a new sequential decision feedback approach for pattern classification in a changing environment. An adaptive classification system is developed that uses the decisions of multiple aspects that may not be separated uniformly. A tap delay mechanism is used to impact the final decision at the current aspect view of the object. This system minimizes the error of the classifier while it maps the new feature vector to a familiar feature space for the classifier. The test results on an acoustic backscattered data set collected from six different objects: two mine-like and four non-mine-like at 72 aspect angles with 5 degrees of separation and with varying signal-to-reverberation ratio (SRR) from 4 to 16 dB are presented.