Decision Learning Algorithm for Acoustic Vessel Classification

INTRODUCTIONMarine transportation plays a vital role in the global economic viability of the United States. As a maritime nation, the United States depends on a strong commercial maritime industry that is tied to maritime security and its stability. Ports and affiliated transportation are all part of a complex system and are potential targets, with wide-scale disaster implications. A need to detect, track and classify vessels of all sizes approaching our ports and harbors is imperative to the security of this country and its complex maritime systems. This case study is an application of the passive acoustic method for vessel classification. The complexity of the classification problem is approached using acoustic signature analysis. Prior studies at Stevens Institute of Technology found that reliable results can be obtained based on signal frequency analysis, specifically, some of the most useful signal characteristics are found in the signal's envelope spectrum. Detection of Envelope Modulation on Noise (DEMON) tool is used in this case study to analyze the acoustic signatures of various vessel types and to provide for specific features that are later implemented into a decision algorithm, which determines the final classification.BackgroundAcousticsAcoustics involves the study of the production, propagation, and reception of sound. As sound travels through water, the waves attenuate, which enable instrumentation to record the changes and associate them with vessel noise characteristics. The main sources of noise generated by a marine vessel are (1) mechanical noise of the main engine and auxiliary machine, (2) propeller cavitations noise, and (3) hydrodynamic noise of the moving vessel.1 The acoustic signature produced by the radiating noise consists of a continuous broadband spectrum and line spectrum. It is the specific configuration of the narrow band frequencies that helps classify and identify different classes of vessels.Classification Algorithms- Decision TreeThe classification objective is to identify vessels (ferry, speed boat, sail boat etc) based on a training set of acoustic signatures whose group-label is previously known and then be able to embrace any new observation. The general process of classification is placing individual vessels into groups labeled based on quantitative information of their attributes. Attributes are: an in-depth and structured set of categories that are usually denoted by a numerical code. In essence, the attributes are the preparation for the classification algorithm construction. A wide range of classification algorithms has been studied in various fields, some of which have been applied to acoustic signature classification. Common classification algorithms mentioned in acoustics studies are: neural networks, K-nearest neighbors, Gaussian, and decision tree. Due to its simplicity and ability to handle a mix type data set, the decision tree model was chosen as the classification algorithm for this case study. The decision tree uses relative entropy or Kullback-Leibler (KL) to study the contrast between two or more probability vectors. This approach sprung from information theory.2 A typical decision tree encodes in a form of tree, where data passes through branch like nodes, constructed from the attributes-rule mentioned earlier and eventually flows through to the final leafs representing the group label (ferry, sail boat, speed boat etc). The node selection is accomplished by selecting the attribute that divides the inhomogeneous data into minimal inhomogeneous subsets using entropy calculations (Kullback-Leibler method).MethodologyData CollectionStevens Passive Acoustic Detection System is composed of four ITC-6050C hydrophones manufactured by International Transducer Corporation and connected to an underwater computer, which communicates and feeds acoustical information (both acquisition and analysis) into the control room of the Maritime Security Laboratory (MSL) at Stevens Institute of Technology (figure 1a). …