Hybrid Discriminative/Class-Specific Classifiers for Narrow-Band Signals

Abstract

The class-specific (CS) method of signal classification operates by computing low-dimensional feature sets defined for each signal class of interest. By computing separate feature sets tailored to each class, i.e., class-specific features, the CS method avoids estimating probability distributions in a high-dimension feature space common to all classes. Building a CS classifier amounts to designing feature extraction modules for each class of interest. In this paper we present the design of three CS modules used to form a CS classifier for narrow-band signals of finite duration. A general module for narrow-band signals based on a narrow-band tracker is described. The only assumptions this module makes regarding the time evolution of the signal spectrum are: (1) one or more narrow-band lines are present, (2) the lines wandered either not at all, e.g., CW signal, or with a purpose, e.g., swept FM signal. The other two modules are suited for specific classes of waveforms and assume some a priori knowledge of the signal is available from training data. For in situ training, the tracker-based module can be used to detect as yet unobserved waveforms and classify them into general categories, for example short CW, long CW, fast FM, slow FM, etc. Waveform-specific class-models can then be designed using these waveforms for training. Classification results are presented comparing the performance of a probabilistic conventional classifier with that of a CS classifier built from general modules and a CS classifier built from waveform specific modules. Results are also presented for hybrid discriminative/ generative versions of the classifiers to illustrate the performance gains attainable in using a hybrid over a generative classifier alone.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2007
Accession Number
ADA494617

Entities

People

  • Brian F. Harrison
  • Paul Baggenstoss

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Data Sets
  • Feature Extraction
  • Frequency
  • Gaussian Noise
  • Hidden Markov Models
  • Information Science
  • Machine Learning
  • Matched Filters
  • Models
  • Noise
  • Probability
  • Probability Distributions
  • Spectra
  • Spectral Lines
  • Supervised Machine Learning

Fields of Study

  • Engineering

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space