This paper presents an automatic target recognition (ATR) system for laser radar (LADAR) imagery, designed to classify objects at multiple levels of discrimination (target detection, classification, and recognition) from single LADAR images. Segmentation is performed in both the range and non-range LADAR channels and results combined to increase object detection rate or decrease false positive detection rate. Through use of the range data, object subimages are projected and rotated to canonical orientations, providing invariance to translation, scale and rotations in 3-D. Global features are extracted for rapid target detection and local receptive field features are computed for target recognition 100% detection and recognition rates are shown for a small set of real LADAR data.

The strategy of using multiple versions of independently developed software as a means to tolerate residual software design faults is discussed. The effectiveness of multiversion software is studied by comparing estimates of the failure probabilities of these systems with the failure probabilities of single versions. The estimates are obtained under a model of dependent failures and compared with estimates obtained when failures are assumed to be independent. The experimental results are based on 20 versions of an aerospace application developed and independently validated by 60 programmers from 4 universities. Descriptions of the application and development process are given, together with an analysis of the 20 versions.>

Highly reliable and effective failure detection and isolation (FDI) software is crucial in modern avionics systems that tolerate hardware failures in real time. The FDI function is an excellent opportunity for applying the principal of software design diversity to the fullest, i.e., algorithm diversity, in order to provide gains in functional performance as well as potentially enhancing the reliability of the software. The authors examine algorithm diversity applied to the redundancy management software for a hardware fault-tolerant sensor array. Results of an experiment are presented that show the performance gains that can be provided by utilizing the consensus of three diverse algorithms for sensor FDI.>

A computational model for visual flow-field cueing and self-motion estimation is developed and simulated. The model is predicated on the notion that the pilot makes noisy, sampled measurements on the spatially distributed visual flow-field surrounding him and, on the basis of these measurements, generates estimates of his own linear and angular terrain-relative velocities which optimally satisfy, in a least-squares sense, the visual kinematic flow constraints. The least-squares formulation is applicable to general observer motions and viewing geometries; it is projection-plane independent and rational in its treatment of redundant and noisy flow cues. A subsidiary but significant output of the model is an `impact time' map, and observer-centered spatially scaled replica of the viewed surface. Simulations are presented to demonstrate the parametric sensitivity and ability to model relevant human visual performance data. Preliminary simulation results of human visual performance are encouraging.