Low frequency approach to target identification

The paper presents a low frequency method for target identification, and its effectiveness is demonstrated for a large variety of objects varying in complexity from spheres and cubes to modern airplanes. A study of the selection of a discrete set of frequencies provides low misclassification error. A number of classification methods are examined using this discrete set of frequencies. It is shown that simple objects can be adequately classified by a linear discriminant method. For more complex targets, such as aircraft, a nearest neighbor approach is required. The introduction of phase and orthogonal polarization components are both found to further decrease misclassification error. A discussion of the trade off between the increased complexity and improved performance of various classification alternatives is provided.