Efficient target detection using an adaptive compressive imager

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 reduces the number of measurements that is required to perform the task, as compared with the number of pixels in 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 revisits 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 with the number of pixels) but does so without appreciable loss in target detection performance.