Signal-to-Symbol Transformation: Reasoning in the HASP/SIAP Program

Reprinted, with permission, from IEEE Acoustic, Speech and Signal Processing, Spring, 1984. ABSTRACT In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with knowledge representation and reasoning techniques used in knowledge-based. HASP/SIAP is one such program that tries to interpret the meaning of passively collected sonar data. In this paper we explore some of the Al techniques that contribute in the "understanding" process. We also describe the organization of HASP/SIAP system as an example of a programming framework that show promise for applications in a class of similar problems.1 Using data from concealed hydrophone arrays, it must detect, localize, and ascertain the type of each ocean vessel within range. Tne presence and movements of submarines are of most interest, but there are strategic and tactical motives for monitoring all vessel types.