Assessment of aimpoint selection performance utilizing feature selection algorithms and the signal processing environment for analysis and reduction (SPEAR)

This paper presents the methodology and assessment of the passive and active sensor feature selection algorithms of interest to the Discriminating Interceptor Technology Program (DITP), as applied to aimpoint selection. The analysis identifies the performance achieved by utilizing individual sensor features and multi-sensor feature fusion. Traditional methods of determining the proper aimpoint have depended upon identifying target characteristics such as the geometric centroid, radiometric centroid, leading edge, and the trailing edge. Once these target points have been defined, the final aimpoint is selected by adding a bias to the target characteristic. However, these and similar algorithms have shown sensitivity to a priori knowledge of the threat, such as aspect angle, target length, thermal and dynamic characteristics. This paper will assess the utility of multi-sensor disparate sensor data to decrease performance sensitivity to a priori knowledge of the threat. These algorithms are among the feature selection algorithms in use in the DITP program for a passive and active fused sensor discrimination. The analysis utilizes simulated IR and ladar sensor data of a conical body that is initially at sufficient range to be realized as a slightly extended source on the focal plane, and then advanced through the later phase of the engagement to the point where the target is relatively close and highly resolved. The measures of performance consist of evaluating the deviations of the estimated aimpoint versus range to target, orientation angle, and aspect angle, for the feature selection algorithms considered.