Target Scene Resolution and Calibration Using Innovative Region Based Active Contour Techniques

During terminal homing, when the interceptor aim point is guided principally by its IR seeker, there are many events within the seeker field of view (FOV) that can contribute to a failure to intercept. Some major events and associated IR image phenomena are: (1) Booster intensity overwhelming the intensity of the hard body. (2) Re-entry vehicle (RV) separation from a post boost vehicle (PBV), and (3) transient shock wave for an endo-atmospheric intercept. Given the stressing timelines of KEI, there is a need to quickly and accurately determine the precise location of the physical centroid of a ballistic missile hard body in order to find the desired point of intercept for the kill vehicle. A recently developed set of image processing algorithms developed at the UCLA Math Department will help solve this problem. These algorithms will differentiate several objects of different intensities, using the image data directly to find the centroid of the hard body. In this context, the only detection heuristic is that there may be more than one object within the images. The proposed image processing algorithms will be combined in an integrated fashion with the physical dynamics of the plume, shock wave and hard body. Our algorithms include feature detection, identification, tracking, and aim point selection. Approaches that will be investigated will include the following: 1. Image segmentation (active contour methods) to detect the separate parts of the boosting missile; 2. Use the object region rather then edge information for tracking; and 3. Considering that the image may be noisy. This approach has been successfully demostrated in a laboratory environment at UCLA on MIR ans commerciaal avaition applications.