Detecting slow moving targets in SAR images

Abstract

Ground moving target indication (GMTI) radars can detect slow-moving targets if their velocities are high enough to produce distinguishable Doppler frequencies. However, no reliable technique is currently available to detect targets that fall below the minimum detectable velocity (MDV) of GMTI radars. In synthetic aperture radar (SAR) images, detection of moving targets is difficult because of target smear due to motion, which could make low-RCS targets fall below stationary ground clutter. Several techniques for SAR imaging of moving targets have been discussed in the literature. These techniques require sufficient signal-to-clutter ratio (SCR) and adequate MDV for pre-detection. Other techniques require complex changes in hardware. Extracting the maximum information from SAR image data is possible using adaptive, model-based approaches. However, these approaches lead to computational complexity, which exceeds current processing power for more than a single object in an image. This combinatorial complexity is due to the need for having to consider a large number of combinations between multiple target models and the data, while estimating unknown parameters of the target models. We are developing a technique for detecting slow-moving targets in SAR images with low signal-to-clutter ratio, without minimal velocity requirements, and without combinatorial complexity. This paper briefly summarizes the difficulties related to current model-based detection algorithms. A new concept, dynamic logic, is introduced along with an algorithm suitable for the detection of very slow-moving targets in SAR images. This new mathematical technique is inspired by the analysis of biological systems, like the human brain, which combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of model-based techniques.