The goal of a target detection system is to determine the location of potential targets in the field of view of the sensor. Traditionally, this is done using high quality images from a conventional imager. For wide field of view scenarios, this can pose a challenge for both data acquisition and system bandwidth. In this paper, we discuss a compressive sensing technique for target detection that dramatically reduce the number of measurements that are required to perform the task, as compared to the number of pixels in the conventional images. This in turn can reduce the data rate from the sensor electronics, and along with it the cost, complexity and the bandwidth requirements of the system. Specifically, we discuss a two-stage approach that first adaptively searches a large area using shift-invariant masks to determine the locations of potential targets (i.e. the regions of interest), and then re-visits each location to discriminate between target and clutter using a different set of specialized masks. We show that the overall process is not only highly efficient (i.e dramatically reduces the number of measurements as compared to the number of pixels), but does so without appreciable loss in target detection performance.

Date Published: 13 June 2014
PDF: 6 pages
Proc. SPIE 9090, Automatic Target Recognition XXIV, 90900M (13 June 2014); doi: 10.1117/12.2054102

Published in SPIE Proceedings Vol. 9090:
Automatic Target Recognition XXIV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)