Raytheon has developed a new tactical form-factored, imaging LADAR (LAser Detection And Ranging) seeker. In a joint activity with AMRDEC, the seeker was used in a tower test data collection at the Russell Measurement Facility at Redstone Arsenal, Alabama. The seeker collected 3D imagery of fixed structures and vehicles embedded in various clutter backgrounds for use in analysis of computer vision and automatic target recognition techniques. This paper presents a high-level overview of the seeker, a description of the test activities, representative LADAR range and intensity imagery collected during the test, and 3D rendered scenes constructed from the imagery.
This paper will discuss the design of a hybrid fuzzy-neural classifier for fusion of range and intensity channels coming from a LADAR sensor. Fusion was performed on a feature rather than pixel level. Results will be compared between ATR performance with and with out fusion. Also, discussed in this paper is the use of genetic algorithms for the training and optimization of the ATR system with a limited set of ground truth.
This paper describes a conceptual real-time systems approach to LADAR automatic target recognition (ATR). Previous work has demonstrated the viability of utilizing correlation filters derived from synthetic models for detection and recognition of mobile targets in Laser Radar (LADAR) sensor images. The distance correlation classifier filter (DCCF) provides a unique potential for reducing throughput while preserving performance. The application of this concept to a real-time system, however, involves refinement, trade studies, and optimization. Refinements in the correlation filter are discussed and evaluated in terms of performance, throughput, and memory. Preliminary performance results for several mobile targets are presented.
Correlation filters have been successfully utilized for object detection in many applications. Each sensor type, however, presents different advantages and challenges. This paper describes the application of correlation filter techniques for automatic target recognition (ATR) to Laser Radar (LADAR) sensor images. Filters are designed using synthetic models, and incorporate range and aspect tolerance for mobile objects. The model generator easily takes into account the sensor field of view (FOV), look-down angle, ground cell size, and shadows. The filters are also designed to exploit various coordinate transforms that are feasible with a LADAR sensor. The correlation algorithm has the unique potential of exploiting the intensity information in conjunction with the range measurements provided by the LADAR. Examples using fixed and mobile targets are presented, along with statistical performance results.
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