Target Detection Using Fractal Geometry

The concepts and theory of fractal geometry were applied to the problem of segmenting a 256 x 256 pixel image so that manmade objects could be extracted from natural backgrounds. The two most important measurements necessary to extract these manmade objects were fractal dimension and lacunarity. Provision was made to pass the manmade portion to a lookup table for subsequent identification. A computer program was written to construct cloud backgrounds of fractal dimensions which were allowed to vary between 2.2 and 2.8. Images of three model space targets were combined with these backgrounds to provide a data set for testing the validity of the approach. Once the data set was constructed, computer programs were written to extract estimates of the fractal dimension and lacunarity on 4 x 4 pixel subsets of the image. It was shown that for clouds of fractal dimension 2.7 or less, appropriate thresholding on fractal dimension and lacunarity yielded a 64 x 64 edge-detected image with all or most of the cloud background removed. These images were enhanced by an erosion and dilation to provide the final image passed to the lookup table. While the ultimate goal was to pass the final image to a neural network for identification, this work shows the applicability of fractal geometry to the problems of image segmentation, edge detection and separating a target of interest from a natural background

Wideband Waveform Design for Robust Target Detection

Future radar systems are expected to use waveforms of a high bandwidth, where the main advantage is an improved range resolution. In this paper, a technique to design robust wideband waveforms for a Multiple-Input-Single-Output system is developed. The context is optimal detection of a single object with partially unknown parameters. The waveforms are robust in the sense that, for a single transmission, detection capability is maintained over an interval of time-delay and time-scaling (Doppler) parameters. A solution framework is derived, approximated, and formulated as an optimization by means of basis expansion. In terms of probabilities of detection and false alarm, numerical evaluation shows the efficiency of the proposed method when compared with a Linear Frequency Modulated signal and a Gaussian pulse.Comment: This paper is submitted for peer review to IEEE letters on signal processin

Dolphin-inspired target detection for sonar and radar

Gas bubbles in the ocean are produced by breaking waves, rainfall, methane seeps, exsolution, and a range of biological processes including decomposition, photosynthesis, respiration and digestion. However one biological process that produces particularly dense clouds of large bubbles, is bubble netting. This is practiced by several species of cetacean. Given their propensity to use acoustics, and the powerful acoustical attenuation and scattering that bubbles can cause, the relationship between sound and bubble nets is intriguing. It has been postulated that humpback whales produce ‘walls of sound’ at audio frequencies in their bubble nets, trapping prey. Dolphins, on the other hand, use high frequency acoustics for echolocation. This begs the question of whether, in producing bubble nets, they are generating echolocation clutter that potentially helps prey avoid detection (as their bubble nets would do with man-made sonar), or whether they have developed sonar techniques to detect prey within such bubble nets and distinguish it from clutter. Possible sonar schemes that could detect targets in bubble clouds are proposed, and shown to work both in the laboratory and at sea. Following this, similar radar schemes are proposed for the detection of buried explosives and catastrophe victims, and successful laboratory tests are undertaken

Automatic active acoustic target detection in turbulent aquatic environments

This work is funded by the Environment and Food Security theme Ph.D. studentship from the University of Aberdeen, the Natural Environment Research Council (NERC) and Department for Environment, Food, and Rural Affairs (Defra grant NE/J004308/1), and the Marine Collaboration Research Forum (MarCRF). We would like to gratefully acknowledge the support from colleagues at Marine Scotland Science.Peer reviewedPublisher PD