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Proceedings Paper

Simple psychovisual approach for multiple target detection and tracking in passive sonar imaging systems
Author(s): Aiman Albert Abdel-Malek; Guy R.L. Sohie
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Paper Abstract

Real-time detection and tracking of multiple targets in passive underwater sonar systems is essential for fast and correct response generation. In this paper, we exploit a model of the human visual early processing of sensory information in designing a smart system for the detection of moving targets and reliable estimation of their velocities. Psychophysical studies indicated that motion information are extracted by a system that responds to oriented spatiotemporal energy. Motion energy detection is modeled by a linear high-pass temporal filter (simple frame to frame difference) and a spatial band-pass Gaussian filtering stage (Laplacian of the Gaussian image) followed by a squarer and summation. The energy image is searched for multiple maxima- depending on the number of targets of interest, and a square window highlights each detected target. Detected targets are labeled according to their motion energy level, this labeling adds a confidence level to the detected target and helps reduce false alarms. A reliable estimate of the labeled target velocity magnitude and direction is produced through tracking of the energy maxima location from one-frame to another. As in the case of the human visual system, there is always a trade-off between accuracy and reliability of the velocity estimate. The algorithm was tested on simulated data. The synthetic data consists of image data in rectangular coordinates, spanning a fixed area, and with a fixed frame rate. The image value was generated as the number of detections over the frame interval at equivalent pixel locations. The intensity was modeled as a Poisson process. The intensity of the Poisson process was varied spatially to simulate non-uniform probability of detection.

Paper Details

Date Published: 20 August 1992
PDF: 10 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139952
Show Author Affiliations
Aiman Albert Abdel-Malek, GE Corporate Research and Development (United States)
Guy R.L. Sohie, GE Corporate Research and Development (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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